WEBVTT - COVID-19 Ch 11: Modeling

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<v Speaker 1>So when the word came down that audience venues were

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<v Speaker 1>being shut down for the foreseeable future, that was a

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<v Speaker 1>real blow. A big part of my job as an

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<v Speaker 1>operatic soprano, or at least that part of it that

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<v Speaker 1>I actually get paid for, is crowds, both the audience

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<v Speaker 1>and on stage with my colleagues. But now a year

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<v Speaker 1>that looked at least eventful is suddenly just empty, ironically

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<v Speaker 1>wiped clean by this tiny organism. Contracts have fallen through,

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<v Speaker 1>and that's obviously really stressful financially, but also performing is

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<v Speaker 1>a big part of how I define myself, so it's

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<v Speaker 1>not being great mentally either. What adds extra stress to

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<v Speaker 1>this is that in any art form that requires your body,

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<v Speaker 1>it is by its very nature time bound. I will

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<v Speaker 1>never sound exactly like I do right now ever again.

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<v Speaker 1>And usually that is fine because it just is what

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<v Speaker 1>it is. That's just aging. But this is an unspecified

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<v Speaker 1>period of time of not being able to do what

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<v Speaker 1>I've trained to do for twenty years now and not

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<v Speaker 1>knowing when I'll get to do that again is a

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<v Speaker 1>really big part of how stressful that's been. It's a

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<v Speaker 1>really scary concept that when your talent is time bound

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<v Speaker 1>you really can't afford to waste a year. But putting

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<v Speaker 1>all of that aside, let's just take a look at

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<v Speaker 1>basic essentials. Here in Australia, our opera companies and our

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<v Speaker 1>concerts generally move to a festival schedule. That is to say,

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<v Speaker 1>we don't really have any set groups of artists or

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<v Speaker 1>operas where we don't hire to a regular schema. There's

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<v Speaker 1>no operas you're guaranteed to see staged. We run off

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<v Speaker 1>individual contracts and the flavor of the season. What this

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<v Speaker 1>boils down to, in practicality is a system where you

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<v Speaker 1>have heaps of variety for the audience, but no real

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<v Speaker 1>stability for the artists who are hired specifically for each opera.

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<v Speaker 1>One year, you might be exactly the sound everyone wants

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<v Speaker 1>and you get so much work that you barely go

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<v Speaker 1>a month without learning any like something new. And the

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<v Speaker 1>next year they want a completely different sound and you

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<v Speaker 1>get nothing. And it's not like you can change your

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<v Speaker 1>voice to fit what they want. It's your voice. It's

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<v Speaker 1>literally part of your body, which side note is terrifying

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<v Speaker 1>in the face of a virus, especially a respiratory virus,

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<v Speaker 1>because we don't have a clear idea of what each

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<v Speaker 1>year holds. We don't have a steady report on our earnings,

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<v Speaker 1>and that means we don't qualify for any income protection

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<v Speaker 1>that our government affords us through our welfare system. It

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<v Speaker 1>really does feel like the government just straight up does

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<v Speaker 1>not care about us at this point, and while we

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<v Speaker 1>as Aussie artists are kind of used to that, it

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<v Speaker 1>doesn't make it hurt any less. But there is a

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<v Speaker 1>silver lining, and that is our art's community. We are

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<v Speaker 1>incredibly resilient and we're usually pretty positive. We pull together

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<v Speaker 1>and what friendships we have are really forged the fire

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<v Speaker 1>of adrenaline. I'm actually part of a group of artists

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<v Speaker 1>that's dedicated to upskilling. While we're out of work, we

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<v Speaker 1>figure we may as well use the time that we

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<v Speaker 1>have Each day, one of us teaches the rest of

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<v Speaker 1>us a skill that we found useful or interesting from

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<v Speaker 1>different crafts to kind of channel those creative needs to

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<v Speaker 1>mental health strategies for dealing with this weird turmoil that

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<v Speaker 1>we've all been thrust into. It's really helped to take

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<v Speaker 1>the edge off the stress that comes with keeping in

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<v Speaker 1>practice without knowing what you're keeping in practice for, or

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<v Speaker 1>if there's even anything to keep in practice for and

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<v Speaker 1>keeping in touch with people who are in the same

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<v Speaker 1>boat really does help reassure you that there is sure

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<v Speaker 1>somewhere at the end of all of this. But for now,

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<v Speaker 1>we just will keep to ourselves. We help our communities

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<v Speaker 1>whenever we can, and you know, maybe we post some

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<v Speaker 1>art every now and then to show people that we

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<v Speaker 1>all still have the capacity for beauty. And then we

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<v Speaker 1>just hold on.

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<v Speaker 2>I'm a social worker in a large county in Ohio

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<v Speaker 2>working in child welfare assessments or commonly known as child

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<v Speaker 2>protective services. I am the one that goes out to

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<v Speaker 2>investigate allegations of abuse and or neglect. I've been doing

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<v Speaker 2>this job for about a year and a half. I'm

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<v Speaker 2>originally from Mexico City, and ironically enough, I lived through

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<v Speaker 2>the H one N one outbreak during my senior year

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<v Speaker 2>of high school three weeks of vacation. Later, life went

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<v Speaker 2>on unlike our current situation. We are technically working from

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<v Speaker 2>home now, which means I do all my paperwork at home,

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<v Speaker 2>but I still have field work. My days are unpredictable.

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<v Speaker 2>We never know what kind of cases we're going to

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<v Speaker 2>get ahead of time, and we either respond to cases

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<v Speaker 2>face to face within twenty four hours, seventy two hours

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<v Speaker 2>the same day, or in emergencies in one hour. As

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<v Speaker 2>you can imagine, people are not typically happy to see

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<v Speaker 2>me knock on their door and tell them they have

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<v Speaker 2>a case open with our agency and that there is

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<v Speaker 2>alleged maltreatment. Add to that all ready stressful situation a

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<v Speaker 2>stranger showing up at their door asking to come into

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<v Speaker 2>their home during a pandemic. A lot of these homes

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<v Speaker 2>are in areas of subsidized housing where space is limited,

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<v Speaker 2>making social distancing extremely difficult. It's very rare I am

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<v Speaker 2>actually able to maintain six feet of distance between people.

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<v Speaker 2>I have to get a full tour of the home,

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<v Speaker 2>especially when there are allegations of hazardous home conditions, so

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<v Speaker 2>interviewing people on the front porch isn't always an option.

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<v Speaker 2>In the worst case scenario where I have to remove

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<v Speaker 2>a child from a home, that child and whatever belongings

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<v Speaker 2>they have come in my car. Sometimes I respond to

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<v Speaker 2>hospitals and I am interviewing people in hospital rooms, where

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<v Speaker 2>it's also hard to maintain six feet of distance, especially

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<v Speaker 2>if there are providers in the room as well. I'm

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<v Speaker 2>supposed to ask every family before I go into their

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<v Speaker 2>home if anyone has experienced a fever, a cough, or

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<v Speaker 2>has been exposed to COVID nineteen. However, even if they

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<v Speaker 2>say yes, I cannot leave a child in a home

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<v Speaker 2>until I have fully assessed the family and the home

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<v Speaker 2>and have determined that the child is safe. I've had

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<v Speaker 2>families tell me that their friend or neighbor tested positive,

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<v Speaker 2>and I have to continue my assessment and just hope

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<v Speaker 2>for the best. I wear a mask, have hand sanitizer,

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<v Speaker 2>and wash my hands as much as I can, but

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<v Speaker 2>that's difficult when you're driving from house to house and

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<v Speaker 2>don't have anywhere to stop. The scariest part is that

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<v Speaker 2>the number of reports of child abuse or neglect have

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<v Speaker 2>significantly decreased. Children are not interacting with mandated reporters and

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<v Speaker 2>disclosing what is going on at home. A lot of

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<v Speaker 2>these children do not have access to technology and cannot

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<v Speaker 2>check in with their providers, even over the phone or

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<v Speaker 2>on the internet. Four children that are in the custody

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<v Speaker 2>of the county, visitations with their parents is held over

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<v Speaker 2>video conferencing when available to both the parents and foster parents.

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<v Speaker 2>This is less than ideal, but it's the best we

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<v Speaker 2>can do. While following stay at home orders and social

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<v Speaker 2>distancing guidelines. Court dates have been postponed over and over again,

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<v Speaker 2>and the only hearing being held are those where we

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<v Speaker 2>have requested emergency custody of a child who is an

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<v Speaker 2>imminent risk of harm. This means that the cases that

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<v Speaker 2>are already open and trying to go through all court

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<v Speaker 2>proceedings to either reunify with their child or terminate parental

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<v Speaker 2>rights are at a standstill. These cases will remain open

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<v Speaker 2>much longer than usual. On a personal level, I've always

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<v Speaker 2>had health anxiety and generalized anxiety, which are at peak

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<v Speaker 2>levels since this outbreak. I have to try to set

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<v Speaker 2>it aside while I do my job, but it has

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<v Speaker 2>become increasingly difficult. We aren't hiring more people because they

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<v Speaker 2>have not figured out how to train people from AFAR

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<v Speaker 2>as shadowing is a huge part of training. As you

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<v Speaker 2>can imagine, this job has an extremely high turnover rate

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<v Speaker 2>and we always need more people to make matters worse.

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<v Speaker 2>My husband has severe asthma and I'm constantly afraid I'll

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<v Speaker 2>bring the virus home to him. My coworkers and I

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<v Speaker 2>have all accepted that we are likely going to come

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<v Speaker 2>in contact with the virus and get sick. It's just

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<v Speaker 2>a matter of when.

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<v Speaker 3>My name is doctor Morgan Menzie. I'm a small animal

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<v Speaker 3>veterinarian in Houston, Texas. The clinic I currently work at

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<v Speaker 3>as a high volume general practice, meaning we see anything

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<v Speaker 3>from routine wellness care to emergencies.

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<v Speaker 4>In early February, we were.

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<v Speaker 3>Pretty concerned about our ability to get personal protective equipment

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<v Speaker 3>or PPE. As general practitioners, we do quite a bit

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<v Speaker 3>of surgery, and we use gloves, masks, gowns, etc. The

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<v Speaker 3>veterinarians and some of the veterinarian nurses at our job

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<v Speaker 3>began to order cloth masks in anticipation of not being

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<v Speaker 3>able to get disposable ones, and then when COVID nineteen

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<v Speaker 3>hit the US pretty hard, veterinarians were called to donate

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<v Speaker 3>as much of our disposable PPE as we could to

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<v Speaker 3>the human doctors on the front lines. Of course, this

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<v Speaker 3>was something we were happy and willing to do, but

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<v Speaker 3>that meant we had to be very conscious about how

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<v Speaker 3>we were using our PPE. One other way veterinarians were

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<v Speaker 3>asked to help was to donate our ventilators. So our clinic,

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<v Speaker 3>it's a general practice, we don't have a ventilator, but

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<v Speaker 3>a lot of the specialty care facilities in certain states

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<v Speaker 3>like Colorado, New York, I think even Michigan have donated

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<v Speaker 3>their ventilators to human.

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<v Speaker 4>Hospitals for their use.

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<v Speaker 3>So we were called as a profession to try and

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<v Speaker 3>delay elective procedures if we were able to, and that

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<v Speaker 3>included vaccinating pets. But I think one of the biggest

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<v Speaker 3>debates that I've seen in our profession is what we

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<v Speaker 3>consider elective. So many of the vaccines dogs and cats receive,

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<v Speaker 3>in my mind, are considered essential. Dogs and cats are

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<v Speaker 3>required to be vaccinated for rabies by law, and they're

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<v Speaker 3>required to keep this vaccine up to date.

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<v Speaker 4>The other thought was.

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<v Speaker 3>You know, dogs in Texas are highly recommended to get

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<v Speaker 3>the leptosporosis vaccine every year as well. If we stopped

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<v Speaker 3>vaccinating for this, would we see more cases of lectos

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<v Speaker 3>sporosis and people. Veterinarians are definitely at the front lines

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<v Speaker 3>when it comes to keeping people safe from zoonotic diseases,

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<v Speaker 3>and I really I can't imagine what would happen if

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<v Speaker 3>we had another outbreak like Raby's or lepto on top.

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<v Speaker 4>Of this current pandemic.

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<v Speaker 3>The biggest change for us started in the middle of March.

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<v Speaker 3>At that time, we moved to curbsite service only. This

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<v Speaker 3>meant that the veterinary nurses would collect history for the

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<v Speaker 3>pet over the phone from the owner, got to the

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<v Speaker 3>parking lot, retrieved the pet from the car, and then

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<v Speaker 3>once the pets inside, I do my physical exam and

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<v Speaker 3>then call the owner with my treatment plan and to

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<v Speaker 3>address any questions they may have.

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<v Speaker 4>At first, this was really nice.

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<v Speaker 3>I mean, most vetinaries are introverts and engaging in small

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<v Speaker 3>talk with clients all day was exhausting. Being able to

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<v Speaker 3>get on the phone and get to the point quickly

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<v Speaker 3>was sort of nice. However, almost six weeks into this thing,

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<v Speaker 3>I'm realizing that the small talk really helped me to

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<v Speaker 3>break up some of the hard conversations I had to

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<v Speaker 3>have throughout the day. Additionally, it's hard to know if

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<v Speaker 3>the owner is understanding what I'm diagnosing in their pet

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<v Speaker 3>or the treatment plan for their pet over the phone.

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<v Speaker 3>I realized so much on body language to understand my clients,

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<v Speaker 3>and I'm sorely missing that right now. The most difficult

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<v Speaker 3>parts of all this has been euthanasias. When an owner

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<v Speaker 3>brings in their pet for euthanasia, I can't hug them

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<v Speaker 3>or comfort them, and the ways that I usually would.

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<v Speaker 3>We stand six feet away from the owner saying goodbye

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<v Speaker 3>to their pet, and deliver the drugs through a very

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<v Speaker 3>long extension set, which makes this process much more clinical

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<v Speaker 3>than it used to be.

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<v Speaker 4>The thing I worry about the most is.

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<v Speaker 3>The human doctors on our front lines that have been

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<v Speaker 3>hit the hardest. As a veterinarian, I'm accustomed to making

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<v Speaker 3>tough decisions that could potentially lead to a pet's death.

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<v Speaker 3>It's hard enough to lose a patient when you've done

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<v Speaker 3>everything in your power to save them, but when your

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<v Speaker 3>resources are low, you're overwhelmed and you have to make

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<v Speaker 3>difficult decisions about who gets a hospital bed and who

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<v Speaker 3>needs to go home.

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<v Speaker 4>That takes a huge toll to all the doctors out there.

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<v Speaker 3>Just know us vetinarians are rooting for you, and if

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<v Speaker 3>you need a shoulder to cry on, we're here for you.

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<v Speaker 5>At first, it seemed so far away, something we just

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<v Speaker 5>heard about, but that couldn't touch us. The first confirmed

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<v Speaker 5>death was in Everett, not far from where our funeral

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<v Speaker 5>home is. I remember the day in January when we heard.

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<v Speaker 4>Of this case.

0:12:09.600 --> 0:12:12.160
<v Speaker 5>As a funeral director, myself and my coworkers are very

0:12:12.200 --> 0:12:14.640
<v Speaker 5>cautious of emerging disease as we deal directly with the

0:12:14.679 --> 0:12:17.320
<v Speaker 5>dead and in facilities or homes of those people where

0:12:17.320 --> 0:12:20.080
<v Speaker 5>their loved ones or staff may also be infected. It

0:12:20.120 --> 0:12:22.680
<v Speaker 5>still didn't seem real or plausible that our daily lives

0:12:22.679 --> 0:12:26.160
<v Speaker 5>would change. This situation has blown up since that day,

0:12:26.200 --> 0:12:29.640
<v Speaker 5>as you all know, as of today May first, twenty twenty,

0:12:30.160 --> 0:12:32.920
<v Speaker 5>Washington has had eight hundred and one deaths from COVID.

0:12:33.600 --> 0:12:36.439
<v Speaker 5>Every day we receive notification of new deaths, and as

0:12:36.480 --> 0:12:38.600
<v Speaker 5>we are one of the largest firms in Seattle, we

0:12:38.679 --> 0:12:42.000
<v Speaker 5>have received several hundred of these cases. I have completely

0:12:42.000 --> 0:12:44.200
<v Speaker 5>lost count of the COVID cases that are now under

0:12:44.200 --> 0:12:47.280
<v Speaker 5>my purview. One of the most heartbreaking things I've witnessed

0:12:47.320 --> 0:12:50.120
<v Speaker 5>is not only the death toll, but the families directly

0:12:50.160 --> 0:12:53.720
<v Speaker 5>impacted in many ways by this One impact comes from

0:12:53.760 --> 0:12:56.440
<v Speaker 5>the risk of exposure to the disease itself to people

0:12:56.480 --> 0:12:59.360
<v Speaker 5>living with or around the person who died. When I

0:12:59.400 --> 0:13:01.480
<v Speaker 5>call family who have had a loss to set up

0:13:01.480 --> 0:13:04.560
<v Speaker 5>the next steps for them, they're often grieving, but now

0:13:04.600 --> 0:13:07.360
<v Speaker 5>they can't even come to meet with us. They themselves

0:13:07.400 --> 0:13:09.920
<v Speaker 5>are often on quarantine and must stay alone for two

0:13:09.920 --> 0:13:11.640
<v Speaker 5>weeks before they can even begin.

0:13:11.480 --> 0:13:12.520
<v Speaker 2>To process their grief.

0:13:13.400 --> 0:13:16.120
<v Speaker 5>People need hugs and shoulders to cry on when they

0:13:16.120 --> 0:13:18.640
<v Speaker 5>have a loss, and no one can offer that right now.

0:13:19.280 --> 0:13:21.520
<v Speaker 5>We were the ones that did that, and now, out

0:13:21.520 --> 0:13:25.000
<v Speaker 5>of fear for our own safety, neither can we. The

0:13:25.080 --> 0:13:28.320
<v Speaker 5>second impact on these families came when the governor issued

0:13:28.320 --> 0:13:31.120
<v Speaker 5>the stay at home order. The original order that came

0:13:31.160 --> 0:13:35.679
<v Speaker 5>mid March brote out funerals or gatherings completely. Families were devastated.

0:13:35.880 --> 0:13:38.880
<v Speaker 5>We began to panic, not just because of the loss

0:13:38.880 --> 0:13:41.120
<v Speaker 5>of the healing capacity that a funeral can bring for

0:13:41.160 --> 0:13:43.800
<v Speaker 5>a lot of people, but the religious aspect and belief

0:13:43.800 --> 0:13:47.320
<v Speaker 5>systems that some cultures have. Some cultures have certain traditions

0:13:47.400 --> 0:13:50.240
<v Speaker 5>or ceremonies that must happen for a person's soul to

0:13:50.320 --> 0:13:53.280
<v Speaker 5>pass into the next realm. It was within a week

0:13:53.320 --> 0:13:56.960
<v Speaker 5>that the governor revised this restriction. The massive implications on

0:13:56.960 --> 0:14:00.720
<v Speaker 5>people's mental health were petitioned by people in funeral homes, churches,

0:14:00.800 --> 0:14:03.800
<v Speaker 5>and the general public, and the mandate was quickly repealed.

0:14:04.480 --> 0:14:06.920
<v Speaker 5>It was finally settled upon by the end of March

0:14:07.000 --> 0:14:09.080
<v Speaker 5>that we would be allowed to hold a gathering that

0:14:09.160 --> 0:14:13.319
<v Speaker 5>was attended by immediate family only. The definition of immediate

0:14:13.360 --> 0:14:16.800
<v Speaker 5>family was left up to the families themselves. Some are small,

0:14:17.120 --> 0:14:21.160
<v Speaker 5>some are very large. Some relationships extend beyond blood, and

0:14:21.200 --> 0:14:23.680
<v Speaker 5>that was not something we were able to determine ourselves.

0:14:24.480 --> 0:14:27.760
<v Speaker 5>Things look so very different. It's not just the COVID deaths.

0:14:28.080 --> 0:14:32.960
<v Speaker 5>People are still dying from suicide, murder, drug overdoses and accidents.

0:14:33.480 --> 0:14:36.480
<v Speaker 5>Those families that have been thrown into a tragic loss

0:14:36.640 --> 0:14:39.600
<v Speaker 5>also have to navigate this new system of grieving without

0:14:39.600 --> 0:14:42.960
<v Speaker 5>a hug, and it's been awful to watch. We are

0:14:43.160 --> 0:14:46.960
<v Speaker 5>out of PPE. We are considered second level in need

0:14:47.000 --> 0:14:49.960
<v Speaker 5>for PPE, so trying to get masks and gloves is

0:14:50.000 --> 0:14:52.960
<v Speaker 5>a challenge. We order them, they are on back order.

0:14:53.120 --> 0:14:56.000
<v Speaker 5>They never arrive. We have to clean the whole facility

0:14:56.040 --> 0:14:59.440
<v Speaker 5>after a funeral. The scarcity of disinfectants was rough. It's

0:14:59.480 --> 0:15:03.160
<v Speaker 5>gotten better, but for a time it did not feel safe.

0:15:03.280 --> 0:15:06.360
<v Speaker 5>Last week, the Seattle area funeral homes ran out of

0:15:06.360 --> 0:15:10.000
<v Speaker 5>the specialty body bags we use for COVID cases. They're

0:15:10.000 --> 0:15:13.800
<v Speaker 5>known as disaster pouches, and their extra protective, leak proof

0:15:14.040 --> 0:15:18.240
<v Speaker 5>and impermeable to pathogen and molecular travel. This seems to

0:15:18.280 --> 0:15:20.880
<v Speaker 5>be the very basic level of ppe that we are

0:15:20.920 --> 0:15:25.160
<v Speaker 5>no longer able to accommodate. We use now three regular

0:15:25.240 --> 0:15:28.000
<v Speaker 5>bags and do the best we can. Never in my

0:15:28.080 --> 0:15:30.520
<v Speaker 5>career did I think I would see the FEMA refrigerated

0:15:30.520 --> 0:15:33.520
<v Speaker 5>body trailers. I remember the day about two weeks ago

0:15:33.560 --> 0:15:36.640
<v Speaker 5>when I saw my first. We have two now, and

0:15:36.680 --> 0:15:39.640
<v Speaker 5>they are full at about forty bodies each, not to

0:15:39.680 --> 0:15:43.960
<v Speaker 5>mention our internal cooler, which holds several hundred bodies. I

0:15:44.040 --> 0:15:46.760
<v Speaker 5>live in a home with children and immunal compromise people.

0:15:47.480 --> 0:15:49.880
<v Speaker 5>Every day I am terrified at what I might bring home.

0:15:50.480 --> 0:15:53.480
<v Speaker 5>The time it takes to clean and sanitize daily is unreal.

0:15:54.040 --> 0:15:56.760
<v Speaker 5>The consistent stress of trying to do my job, be

0:15:56.840 --> 0:16:00.000
<v Speaker 5>a mother and wife, and keep myself protected is immense.

0:16:00.800 --> 0:16:04.280
<v Speaker 5>I cry or nearly cry every day, either on my

0:16:04.360 --> 0:16:07.040
<v Speaker 5>way to work or on the way home. I cannot

0:16:07.040 --> 0:16:10.680
<v Speaker 5>express in words how exhausted and emotionally drained my coworkers

0:16:10.720 --> 0:16:13.720
<v Speaker 5>and I am. I know we all love what we

0:16:13.840 --> 0:16:17.320
<v Speaker 5>do and helping people navigate the worst day of someone's life,

0:16:17.920 --> 0:16:19.840
<v Speaker 5>but we all need this to be over as soon

0:16:19.840 --> 0:16:24.680
<v Speaker 5>as possible. I love helping families, making a grieving widow smile,

0:16:25.120 --> 0:16:29.320
<v Speaker 5>facilitating a chance to say goodbye. I feel essential. People

0:16:29.400 --> 0:16:32.560
<v Speaker 5>need me. I stand in the back at funeral services

0:16:32.600 --> 0:16:35.760
<v Speaker 5>for immediate family, where families take off their masks to

0:16:35.840 --> 0:16:38.800
<v Speaker 5>hug and cry on each other's shoulders. That's what people

0:16:38.840 --> 0:16:42.400
<v Speaker 5>do at funerals. Little comfort is found from a gaze

0:16:42.520 --> 0:17:24.720
<v Speaker 5>from a masked face at six feet away.

0:17:28.880 --> 0:17:34.480
<v Speaker 2>Wow. Wow, those first hand accounts like wow.

0:17:34.920 --> 0:17:40.440
<v Speaker 5>Just so so phenomenal. Thank you everyone for sending those in.

0:17:40.600 --> 0:17:42.880
<v Speaker 5>We really appreciate every one of you that has taken

0:17:42.880 --> 0:17:44.960
<v Speaker 5>the time to fill out the form and to send

0:17:45.000 --> 0:17:47.520
<v Speaker 5>us your stories there. It's incredible to get to hear

0:17:47.560 --> 0:17:50.320
<v Speaker 5>stories from so many different people right now.

0:17:50.560 --> 0:17:53.920
<v Speaker 2>Yeah, it really is. Thank you, Thank you, We very

0:17:53.960 --> 0:17:55.400
<v Speaker 2>much appreciate it.

0:17:56.160 --> 0:17:56.360
<v Speaker 4>Hi.

0:17:56.760 --> 0:18:00.760
<v Speaker 2>I'm Aaron Welsh and I'm Arin Oman Updyke and this

0:18:00.800 --> 0:18:01.879
<v Speaker 2>podcast will kill you.

0:18:02.720 --> 0:18:06.160
<v Speaker 5>Welcome to the eleventh episode eleven.

0:18:06.640 --> 0:18:08.840
<v Speaker 2>I can't believe it eleven either.

0:18:09.000 --> 0:18:09.640
<v Speaker 5>I'm shocked.

0:18:10.040 --> 0:18:12.760
<v Speaker 2>I don't know how we've done this. Quite honestly, I

0:18:12.800 --> 0:18:15.400
<v Speaker 2>don't know. It's all a blur, Aaron, It's all a blur.

0:18:16.040 --> 0:18:19.640
<v Speaker 5>This is our Anatomy of a Pandemic series on COVID nineteen.

0:18:20.520 --> 0:18:23.280
<v Speaker 5>This week, we're diving into a topic that has generated

0:18:23.359 --> 0:18:28.080
<v Speaker 5>a ton of headlines and has influenced decisions that have

0:18:28.200 --> 0:18:33.120
<v Speaker 5>impacted billions of people around the world. That is math

0:18:33.240 --> 0:18:35.080
<v Speaker 5>modeling of infectious disease.

0:18:36.200 --> 0:18:37.200
<v Speaker 2>Let's hear it from math.

0:18:37.880 --> 0:18:41.080
<v Speaker 5>Clap clap clap for math. This might be the one

0:18:41.160 --> 0:18:43.000
<v Speaker 5>episode I convinced my brother to listen to.

0:18:46.520 --> 0:18:49.840
<v Speaker 2>How long will this pandemic go on? I don't know

0:18:50.280 --> 0:18:51.520
<v Speaker 2>how bad is it going to get?

0:18:51.680 --> 0:18:52.320
<v Speaker 4>Great question?

0:18:52.760 --> 0:18:53.960
<v Speaker 2>How can we slow it down?

0:18:54.080 --> 0:18:55.119
<v Speaker 5>Would love to know that?

0:18:55.760 --> 0:18:59.359
<v Speaker 2>And how can we even begin to address those questions?

0:19:00.080 --> 0:19:03.520
<v Speaker 2>Let me guess the answer at least for that question

0:19:04.520 --> 0:19:09.280
<v Speaker 2>is math Math Surprise a rise. In this episode, we

0:19:09.359 --> 0:19:13.119
<v Speaker 2>want to lay a groundwork for understanding what mathematical models

0:19:13.200 --> 0:19:16.240
<v Speaker 2>of infectious disease actually look like, where they get the

0:19:16.320 --> 0:19:19.960
<v Speaker 2>data that they use, what current models of COVID nineteen

0:19:20.040 --> 0:19:23.560
<v Speaker 2>are being used for, and most importantly, how we can

0:19:23.600 --> 0:19:26.360
<v Speaker 2>actually evaluate these headline making models.

0:19:26.520 --> 0:19:27.879
<v Speaker 5>That is very important.

0:19:28.200 --> 0:19:33.440
<v Speaker 2>Yeah, it's I'm very excited. And to walk us through

0:19:33.640 --> 0:19:37.480
<v Speaker 2>the wonderful world of math models is doctor Mike Famulari,

0:19:37.920 --> 0:19:41.879
<v Speaker 2>Senior research scientist at the Institute for Disease Modeling. He

0:19:42.040 --> 0:19:47.159
<v Speaker 2>did such a fantastic job of breaking down these complex

0:19:47.200 --> 0:19:51.280
<v Speaker 2>topics and We're so very excited to share his interview

0:19:51.440 --> 0:19:51.760
<v Speaker 2>with you.

0:19:52.440 --> 0:19:56.240
<v Speaker 4>But before we do that, it's quarantin any time.

0:19:56.640 --> 0:19:58.200
<v Speaker 2>Oh yeah, quarantiny time.

0:19:58.280 --> 0:20:01.160
<v Speaker 5>Baby, what are we doing today?

0:20:01.359 --> 0:20:01.560
<v Speaker 6>Erin?

0:20:01.880 --> 0:20:07.240
<v Speaker 2>Oh, you know, Quarantini eleven correspondingly, of course makes sense.

0:20:08.359 --> 0:20:11.160
<v Speaker 5>But what's in quarantine eleven? That's what we really want to.

0:20:11.119 --> 0:20:15.240
<v Speaker 2>Know, the key question. It's basically a Manhattan I approve.

0:20:15.760 --> 0:20:20.879
<v Speaker 5>I mean, listen, it's quarantine times. Okay, We're not going

0:20:20.960 --> 0:20:22.280
<v Speaker 5>to get too fancy.

0:20:22.240 --> 0:20:26.840
<v Speaker 2>Nah na na, and you know it's delicious, it's simple.

0:20:27.600 --> 0:20:30.600
<v Speaker 5>We will post the full recipe for this quarantiny and

0:20:30.800 --> 0:20:34.199
<v Speaker 5>our non alcoholic plusy Berta on our website and all

0:20:34.240 --> 0:20:36.440
<v Speaker 5>of our social media channels, so you can figure out

0:20:36.440 --> 0:20:39.960
<v Speaker 5>how we make a Manhattan non alcoholic if you follow us.

0:20:43.280 --> 0:20:46.119
<v Speaker 2>Okay, now that that's out of the way, we do

0:20:46.240 --> 0:20:48.600
<v Speaker 2>still have a few more pieces of business to tend to.

0:20:49.560 --> 0:20:52.760
<v Speaker 2>We received some feedback from our last episode in this series,

0:20:52.800 --> 0:20:56.119
<v Speaker 2>which was on education, and that episode primarily focused on

0:20:56.160 --> 0:20:59.000
<v Speaker 2>the impact that COVID nineteen has had on schools in

0:20:59.040 --> 0:21:01.679
<v Speaker 2>the US, and we wanted to share a few of

0:21:01.680 --> 0:21:02.720
<v Speaker 2>these responses with you.

0:21:03.800 --> 0:21:06.320
<v Speaker 5>The first email excerpt comes from someone who wanted to

0:21:06.359 --> 0:21:09.120
<v Speaker 5>clarify a point of discussion in the education episode where

0:21:09.119 --> 0:21:13.160
<v Speaker 5>we talked about equity in schools, particularly highlighting the long

0:21:13.280 --> 0:21:17.000
<v Speaker 5>history of racism and disparity in education for Native Americans

0:21:17.040 --> 0:21:21.600
<v Speaker 5>in this country. So I'll read you part of that email.

0:21:22.359 --> 0:21:25.400
<v Speaker 5>Near the beginning of the interview, the substantial and historically

0:21:25.560 --> 0:21:29.560
<v Speaker 5>entrenched disparities in public education in our country were casually dismissed.

0:21:30.040 --> 0:21:32.800
<v Speaker 5>As a Native American whose mother struggled with boarding school

0:21:32.800 --> 0:21:35.840
<v Speaker 5>abuse and the traumatic scars of education racism for most

0:21:35.840 --> 0:21:39.440
<v Speaker 5>of her life, this was distressing to hear. Alarming structural

0:21:39.520 --> 0:21:43.800
<v Speaker 5>disparities exist at all levels of public school education for poor, Black,

0:21:44.160 --> 0:21:49.919
<v Speaker 5>LATINX and Native American students in both urban and rural contexts. Furthermore,

0:21:50.040 --> 0:21:53.080
<v Speaker 5>data about these disparities have been collected and widely reported

0:21:53.080 --> 0:21:56.080
<v Speaker 5>on for more than one hundred and twenty years since W. E. B.

0:21:56.240 --> 0:21:59.000
<v Speaker 5>Du Bois began publishing his sociological work at the turn

0:21:59.040 --> 0:22:00.560
<v Speaker 5>of the twentieth century.

0:22:01.800 --> 0:22:06.800
<v Speaker 2>Yes, yep, great, excellent points. Thank you for sending us

0:22:06.840 --> 0:22:10.240
<v Speaker 2>that email. And then the second email comes from a

0:22:10.280 --> 0:22:13.359
<v Speaker 2>Finnish journalist who wanted to provide a more nuanced picture

0:22:13.440 --> 0:22:17.439
<v Speaker 2>of the impact of this pandemic on Finnish schools. The

0:22:17.440 --> 0:22:20.840
<v Speaker 2>social security net is undoubtedly more advanced than the American

0:22:21.280 --> 0:22:23.960
<v Speaker 2>but the fact is that also in the Finnish society,

0:22:24.040 --> 0:22:27.480
<v Speaker 2>the Corona pandemic has brought societies inequalities to light in

0:22:27.520 --> 0:22:30.480
<v Speaker 2>a very uncomfortable way. When talking about the schools and

0:22:30.560 --> 0:22:34.000
<v Speaker 2>children in particular, this highlights both differences in income and wealth,

0:22:34.040 --> 0:22:37.040
<v Speaker 2>as well as problems with domestic violence, substance abuse, and

0:22:37.080 --> 0:22:40.320
<v Speaker 2>mental health issues. Since the schools closed in mid March,

0:22:40.400 --> 0:22:43.840
<v Speaker 2>both teachers and child welfare services have expressed concern of

0:22:43.920 --> 0:22:47.040
<v Speaker 2>those who, for example, have a very toxic home environment

0:22:47.280 --> 0:22:49.600
<v Speaker 2>and for whom the school is normally a sanctuary with

0:22:49.640 --> 0:22:52.600
<v Speaker 2>safe adults and a warm meal every day. Many families

0:22:52.640 --> 0:22:54.960
<v Speaker 2>have lost income and many are struggling with the extra

0:22:55.040 --> 0:22:57.600
<v Speaker 2>expenses brought on by having the whole family at home

0:22:57.640 --> 0:23:00.000
<v Speaker 2>all of the time. Not everyone has an Internet connect

0:23:00.280 --> 0:23:03.159
<v Speaker 2>at home, and in for example, low income families with

0:23:03.240 --> 0:23:05.840
<v Speaker 2>multiple children, they might not have enough computers for all

0:23:05.880 --> 0:23:09.399
<v Speaker 2>of them to attend school. Schools also report difficulty in

0:23:09.440 --> 0:23:11.680
<v Speaker 2>getting hold of some children and families, and the means

0:23:11.680 --> 0:23:13.960
<v Speaker 2>to protect these children have worsened now that they don't

0:23:13.960 --> 0:23:17.400
<v Speaker 2>meet the children regularly, children with special needs and need

0:23:17.440 --> 0:23:20.400
<v Speaker 2>of extra support might have lost that. In Finland, school

0:23:20.480 --> 0:23:22.840
<v Speaker 2>lunches are normally free of charge to all pupils, and

0:23:22.920 --> 0:23:25.119
<v Speaker 2>during the state of emergency, when the schools have closed

0:23:25.119 --> 0:23:28.399
<v Speaker 2>their doors, the government still recommends that municipalities who are

0:23:28.440 --> 0:23:31.439
<v Speaker 2>responsible for the education provide lunch for those who need it.

0:23:31.800 --> 0:23:35.280
<v Speaker 2>Not all municipalities do, and between those who do, it's

0:23:35.359 --> 0:23:38.119
<v Speaker 2>done in many different ways. In some cities you can

0:23:38.119 --> 0:23:40.439
<v Speaker 2>pick up lunch every day and others weekly, and some

0:23:40.560 --> 0:23:43.399
<v Speaker 2>offer money instead. For many kids, the school lunch might

0:23:43.440 --> 0:23:45.239
<v Speaker 2>be the only meal they eat during the day, So

0:23:45.280 --> 0:23:48.119
<v Speaker 2>for those children and families where their municipality does not

0:23:48.200 --> 0:23:50.760
<v Speaker 2>offer lunch, the situation is very difficult.

0:23:51.760 --> 0:23:53.280
<v Speaker 5>Again, thank you for sending that.

0:23:53.760 --> 0:23:55.600
<v Speaker 2>Yes, yes, thank you so much.

0:23:55.800 --> 0:23:59.920
<v Speaker 5>It's bad everywhere is what that means for sure, for sure.

0:24:01.000 --> 0:24:02.840
<v Speaker 5>And the last thing that we wanted to share was

0:24:02.880 --> 0:24:06.399
<v Speaker 5>a correction about the twenty percent reduction in pay to

0:24:06.440 --> 0:24:09.280
<v Speaker 5>public school teachers in Hawaii that was mentioned during the interview.

0:24:09.600 --> 0:24:12.960
<v Speaker 5>This reduction, which would also include other public employees not

0:24:13.200 --> 0:24:16.679
<v Speaker 5>just teachers, has not actually happened yet as of May first,

0:24:17.080 --> 0:24:19.440
<v Speaker 5>so these pay cuts have been proposed but have not

0:24:19.520 --> 0:24:22.840
<v Speaker 5>been finalized yet and may not be finalized depending on

0:24:22.880 --> 0:24:23.840
<v Speaker 5>how things are decided.

0:24:24.320 --> 0:24:29.800
<v Speaker 2>So yeah, another important correction. Yeah, thank you so much

0:24:30.119 --> 0:24:33.760
<v Speaker 2>for sharing those insights and corrections with us. We love

0:24:33.840 --> 0:24:35.920
<v Speaker 2>hearing from our listeners and we wish that we could

0:24:35.960 --> 0:24:38.600
<v Speaker 2>respond to each and every one of you. If only

0:24:38.640 --> 0:24:40.560
<v Speaker 2>there were more hours in the day.

0:24:41.040 --> 0:24:46.600
<v Speaker 5>Constant refrain, constant refrain. Okay, are we ready to talk

0:24:46.640 --> 0:24:47.320
<v Speaker 5>about math?

0:24:48.040 --> 0:24:52.000
<v Speaker 2>Let's do it. Yes, we'll take a quick break and

0:24:52.080 --> 0:25:00.439
<v Speaker 2>then we'll get down to business.

0:25:25.880 --> 0:25:26.040
<v Speaker 1>Hi.

0:25:26.160 --> 0:25:30.200
<v Speaker 6>I'm doctor Mike Famulari, and I'm a principal research scientist

0:25:30.200 --> 0:25:33.920
<v Speaker 6>that an Institute for Disease Modeling IDM. Institute for Disease

0:25:33.960 --> 0:25:37.960
<v Speaker 6>Modeling is a research institute that's a collaboration between Intellectual

0:25:38.040 --> 0:25:40.800
<v Speaker 6>Ventures and Bill and be Linda Gates that focuses on

0:25:41.480 --> 0:25:47.439
<v Speaker 6>issues around disease control and elimination and ideally eradication, and

0:25:47.560 --> 0:25:50.879
<v Speaker 6>until recently had a heavy focus on developing world applications

0:25:50.880 --> 0:25:57.119
<v Speaker 6>including malaria elimination and control, polio eradication, HIV control, tuberculosis,

0:25:57.440 --> 0:26:02.520
<v Speaker 6>typhoid vaccination policy, things like that. You know, Starting in January,

0:26:03.240 --> 0:26:05.119
<v Speaker 6>we started to pay more and more attention to this

0:26:05.119 --> 0:26:08.720
<v Speaker 6>thing that is now COVID nineteen, recognizing its pandemic potential

0:26:08.880 --> 0:26:12.399
<v Speaker 6>and have increasingly pivoted a bunch of efforts towards trying

0:26:12.400 --> 0:26:15.560
<v Speaker 6>to understand what's happening with COVID and trying to understand

0:26:15.600 --> 0:26:18.240
<v Speaker 6>what we can do about it to besides staying home

0:26:18.320 --> 0:26:22.280
<v Speaker 6>for the next infinite months or you know, letting it

0:26:22.400 --> 0:26:24.280
<v Speaker 6>rip and seeing what happens.

0:26:24.680 --> 0:26:27.320
<v Speaker 2>Great, thank you so very much for joining us today.

0:26:27.320 --> 0:26:30.040
<v Speaker 2>We're very excited to chat with you about some math modeling.

0:26:31.040 --> 0:26:32.639
<v Speaker 6>Yeah, thank you for having me. It's one of my

0:26:32.640 --> 0:26:34.040
<v Speaker 6>favorite topics, of course.

0:26:34.480 --> 0:26:38.360
<v Speaker 2>Okay, so before we get into the COVID nineteen specific stuff,

0:26:38.760 --> 0:26:42.680
<v Speaker 2>we would love to just lay a groundwork for what

0:26:42.840 --> 0:26:46.640
<v Speaker 2>math models are and what they're used for in infectious disease,

0:26:47.080 --> 0:26:49.320
<v Speaker 2>and so could you just start us off by answering,

0:26:49.520 --> 0:26:52.800
<v Speaker 2>you know, what is a math model and what are

0:26:52.840 --> 0:26:55.280
<v Speaker 2>some of the goals of mathematical modeling.

0:26:56.080 --> 0:26:58.480
<v Speaker 6>Yeah, it's an excellent question, and I think it's way

0:26:58.520 --> 0:27:01.720
<v Speaker 6>bigger than just infectious disease is. But certainly infectious diseases

0:27:01.760 --> 0:27:05.560
<v Speaker 6>having its moment right now like maybe never before. So

0:27:06.320 --> 0:27:09.800
<v Speaker 6>the key idea with mathematical modeling in general is you're

0:27:09.840 --> 0:27:13.840
<v Speaker 6>trying to make a simplified synthetic version of the real

0:27:13.880 --> 0:27:17.640
<v Speaker 6>world in some way that has really explicit rules. That's

0:27:17.640 --> 0:27:20.880
<v Speaker 6>the mathematics part. And then with those rules of how

0:27:20.920 --> 0:27:24.439
<v Speaker 6>your synthetic you know, representation of the world actually interacts,

0:27:24.800 --> 0:27:26.959
<v Speaker 6>you try to learn about the different possibilities of how

0:27:26.960 --> 0:27:30.480
<v Speaker 6>the real world could interact. And you also often try

0:27:30.480 --> 0:27:33.000
<v Speaker 6>to work backwards and say, I've seen these things in

0:27:33.040 --> 0:27:35.760
<v Speaker 6>the real world. I think I can map them onto

0:27:35.800 --> 0:27:38.879
<v Speaker 6>my representation. I sort of say, my model kind of

0:27:38.880 --> 0:27:41.320
<v Speaker 6>looks like the real world in some specific way, and

0:27:41.320 --> 0:27:43.280
<v Speaker 6>then I can often ask questions of the model I

0:27:43.320 --> 0:27:46.840
<v Speaker 6>can't ask of the real world, like, you know, how

0:27:46.880 --> 0:27:49.119
<v Speaker 6>did the transmission actually happen? I didn't measure how a

0:27:49.200 --> 0:27:53.199
<v Speaker 6>virus got from one lung to one mouth, but statistically speaking,

0:27:53.200 --> 0:27:55.000
<v Speaker 6>what might have happened there, what might happen there on

0:27:55.040 --> 0:27:58.480
<v Speaker 6>average across a large population. And the other thing we

0:27:58.480 --> 0:28:00.879
<v Speaker 6>can do with models, and why we care about models,

0:28:01.160 --> 0:28:04.440
<v Speaker 6>especially in infectious disease research, is that we only get

0:28:04.480 --> 0:28:07.359
<v Speaker 6>one real world, but we can often in the computer

0:28:07.400 --> 0:28:10.640
<v Speaker 6>we can run many different scenarios with many different variations

0:28:10.640 --> 0:28:13.080
<v Speaker 6>on how we think the simplified world works, and that

0:28:13.119 --> 0:28:16.280
<v Speaker 6>helps us do two things that are really important. One

0:28:16.320 --> 0:28:19.680
<v Speaker 6>is again try to understand stuff that we can't see directly,

0:28:19.720 --> 0:28:22.960
<v Speaker 6>but how it probably works. And then two, it allows

0:28:23.040 --> 0:28:26.040
<v Speaker 6>us to explore different future scenarios based on what we've

0:28:26.040 --> 0:28:28.679
<v Speaker 6>seen so far that may depend on different kinds of

0:28:28.720 --> 0:28:32.199
<v Speaker 6>decisions or different actions. Are also different scientific learnings that

0:28:32.240 --> 0:28:35.119
<v Speaker 6>we haven't yet resolved that will affect how that future

0:28:35.119 --> 0:28:35.600
<v Speaker 6>plays out.

0:28:37.119 --> 0:28:39.560
<v Speaker 2>Yeah, creating a world of parallel universes.

0:28:41.520 --> 0:28:43.880
<v Speaker 6>That's literally how it works on the computers. If you

0:28:43.960 --> 0:28:48.440
<v Speaker 6>might have ten thousand computers on a cloud cluster doing

0:28:48.440 --> 0:28:50.440
<v Speaker 6>the same thing in parallel, each one trying out a

0:28:50.480 --> 0:28:52.720
<v Speaker 6>little different pathway, that's exactly how it works.

0:28:53.080 --> 0:28:57.760
<v Speaker 2>It's amazing. It's amazing. So, talking specifically now about infectious

0:28:57.760 --> 0:29:01.280
<v Speaker 2>disease models, can you walk us through what the basic

0:29:01.360 --> 0:29:05.600
<v Speaker 2>components are of an infectious disease model, like an SIR model?

0:29:06.960 --> 0:29:11.040
<v Speaker 6>Perfect? Yeah, the most common starting model, like the front

0:29:11.040 --> 0:29:14.320
<v Speaker 6>of the textbook, is often what's called an SIR model.

0:29:14.800 --> 0:29:17.960
<v Speaker 6>The SI in R refer to states that a person

0:29:18.040 --> 0:29:21.719
<v Speaker 6>in your model can have S means they're susceptible to

0:29:21.760 --> 0:29:25.800
<v Speaker 6>the disease. I means they're currently infected with the disease,

0:29:26.400 --> 0:29:29.560
<v Speaker 6>and R usually means they've recovered from the disease. And

0:29:29.640 --> 0:29:32.480
<v Speaker 6>in the simplest models, we assume that when you've recovered,

0:29:32.480 --> 0:29:34.440
<v Speaker 6>you have immunity for the rest of your life. That's

0:29:34.440 --> 0:29:37.320
<v Speaker 6>one of those first assumptions that's often not true. And

0:29:37.360 --> 0:29:40.320
<v Speaker 6>then with those people who have these simple states of

0:29:40.360 --> 0:29:43.680
<v Speaker 6>either susceptible, infected, or recovered, we put them together in

0:29:43.720 --> 0:29:46.959
<v Speaker 6>a transmission model and we let them interact in some

0:29:47.120 --> 0:29:50.360
<v Speaker 6>very simplified way. The simplest version is literally sort of

0:29:50.400 --> 0:29:54.280
<v Speaker 6>like everybody's in a conference center. Everybody's shaking everybody's hand,

0:29:54.320 --> 0:29:57.000
<v Speaker 6>everyone's talking to everybody. It's all completely well mixed. Everybody

0:29:57.040 --> 0:30:01.520
<v Speaker 6>gets along and in that context, and we can introduce

0:30:01.560 --> 0:30:04.840
<v Speaker 6>an infected person at the beginning of the epidemic. In

0:30:04.880 --> 0:30:07.920
<v Speaker 6>our model, they're interacting with all these susceptible people, and

0:30:07.960 --> 0:30:11.000
<v Speaker 6>so they can pretty easily transmit the infection. How easily

0:30:11.160 --> 0:30:14.920
<v Speaker 6>is a property both of the pathogen itself and exactly

0:30:14.960 --> 0:30:17.040
<v Speaker 6>how much mixing those people are doing and how close

0:30:17.080 --> 0:30:19.760
<v Speaker 6>they're talking to each other and all that, and then

0:30:19.960 --> 0:30:22.560
<v Speaker 6>it goes from one infected to a few infected, to

0:30:22.640 --> 0:30:25.600
<v Speaker 6>a lot more infected as time goes on. If you

0:30:25.680 --> 0:30:27.760
<v Speaker 6>keep everybody in this little conference room for as long

0:30:27.800 --> 0:30:30.400
<v Speaker 6>as it takes. Some of those infected people start to

0:30:30.440 --> 0:30:34.800
<v Speaker 6>recover now they're no longer susceptible. Transmission continues, but it's

0:30:34.800 --> 0:30:37.560
<v Speaker 6>getting harder to transmit because there's fewer people around that

0:30:37.640 --> 0:30:42.120
<v Speaker 6>aren't already recovered. And eventually the whole thing plays itself

0:30:42.160 --> 0:30:45.239
<v Speaker 6>out and you've had your epidemic come and go.

0:30:46.160 --> 0:30:49.520
<v Speaker 2>Excellent. Yeah, and so you know the data that you

0:30:49.640 --> 0:30:52.959
<v Speaker 2>use to estimate these parameters, so the population or the

0:30:53.000 --> 0:30:55.040
<v Speaker 2>size of each of the states that you mentioned, the

0:30:55.160 --> 0:30:58.400
<v Speaker 2>S and I and are and then also the transmission rate,

0:30:58.720 --> 0:31:01.760
<v Speaker 2>you know, how fast one one person moves from the

0:31:01.840 --> 0:31:05.760
<v Speaker 2>susceptible state to the infected state, and then also maybe

0:31:05.760 --> 0:31:09.360
<v Speaker 2>a recovery rate. Where do the data usually come from

0:31:09.480 --> 0:31:12.400
<v Speaker 2>to estimate those different numbers or parameters?

0:31:13.880 --> 0:31:17.160
<v Speaker 6>Yeah, yet another great question, And thinking about where the

0:31:17.240 --> 0:31:20.600
<v Speaker 6>data comes from will help you really understand that comment

0:31:20.640 --> 0:31:23.200
<v Speaker 6>I made earlier about what parts of modeling is about

0:31:23.240 --> 0:31:26.120
<v Speaker 6>looking backwards to see things you can't measure, and what

0:31:26.160 --> 0:31:29.280
<v Speaker 6>parts are about understanding what's compatible with the data you have.

0:31:30.040 --> 0:31:33.000
<v Speaker 6>So if we focus on the individual part for a second,

0:31:33.160 --> 0:31:36.840
<v Speaker 6>like how long is someone infected for? Which is another

0:31:36.880 --> 0:31:38.520
<v Speaker 6>way of saying, how fast do they go from the

0:31:38.560 --> 0:31:42.080
<v Speaker 6>infected compartment the eye compartment to the r recovered compartment.

0:31:42.600 --> 0:31:45.680
<v Speaker 6>That we can often in the best case scenario, measure

0:31:45.720 --> 0:31:48.040
<v Speaker 6>from people who show up at a hospital, or measure

0:31:48.040 --> 0:31:51.400
<v Speaker 6>from people who participate in a study. We literally measure

0:31:51.520 --> 0:31:54.680
<v Speaker 6>the virus when they start expressing it. They start shedding virus,

0:31:54.680 --> 0:31:56.920
<v Speaker 6>as we usually say, and we can measure when they stop.

0:31:57.200 --> 0:31:59.160
<v Speaker 6>And so that's something that in principle, you can measure

0:31:59.200 --> 0:32:02.680
<v Speaker 6>pretty directly. Individual properties are often like that. Immunity is

0:32:02.720 --> 0:32:06.000
<v Speaker 6>something similar. You can measure people's to anybody change and

0:32:06.160 --> 0:32:09.400
<v Speaker 6>in certain circumstances, you know where if you've measured it

0:32:09.440 --> 0:32:11.880
<v Speaker 6>the right way, you can even measure how protective are

0:32:11.960 --> 0:32:15.440
<v Speaker 6>anybody is about getting infected again. So those kind of stuff,

0:32:15.440 --> 0:32:19.840
<v Speaker 6>the best data come from actually measuring people individually. The

0:32:19.920 --> 0:32:23.520
<v Speaker 6>thing that we very rarely get to measure individually directly

0:32:23.520 --> 0:32:26.680
<v Speaker 6>from people because it's it's the experiments are more difficult,

0:32:26.760 --> 0:32:30.040
<v Speaker 6>they're more invasive, they take a lot more logistics is

0:32:30.040 --> 0:32:34.160
<v Speaker 6>the transmission part itself are not is used to characterize

0:32:34.200 --> 0:32:37.120
<v Speaker 6>sort of on average, how many people an infected person

0:32:37.160 --> 0:32:40.520
<v Speaker 6>transmits to the way we usually figure that out is

0:32:40.560 --> 0:32:42.640
<v Speaker 6>not by measuring it directly, but by looking at like

0:32:42.840 --> 0:32:45.560
<v Speaker 6>the development of infections over time that we measure in

0:32:45.840 --> 0:32:48.719
<v Speaker 6>a population, like we measure at the hospital. And so

0:32:48.800 --> 0:32:50.880
<v Speaker 6>you sort of say, well, I think there's this many people.

0:32:51.560 --> 0:32:54.280
<v Speaker 6>I think their infections kind of look like this, and

0:32:54.320 --> 0:32:58.200
<v Speaker 6>then I've seen two people infected yesterday, four people today,

0:32:58.600 --> 0:33:02.560
<v Speaker 6>eight people, sixteen and so on, and I back calculate that, oh,

0:33:02.600 --> 0:33:05.160
<v Speaker 6>if that's what the data pattern looks like, it looks

0:33:05.200 --> 0:33:09.160
<v Speaker 6>like each infected person maybe causes two more new infections

0:33:09.160 --> 0:33:11.640
<v Speaker 6>on average. And that's how I figure it out. It's

0:33:11.680 --> 0:33:15.400
<v Speaker 6>an inference. It's very rarely a direct measurement, gotcha.

0:33:16.520 --> 0:33:19.080
<v Speaker 2>And so you know, with these Siro models and with

0:33:19.160 --> 0:33:23.200
<v Speaker 2>the basic modeling of a hypothetical or even you know,

0:33:23.280 --> 0:33:27.160
<v Speaker 2>real life epidemic or outbreak, they seem to tend to

0:33:27.200 --> 0:33:30.800
<v Speaker 2>follow what we call this epidemic curve. You talked about

0:33:30.840 --> 0:33:33.040
<v Speaker 2>this a bit in terms of the conference center mixing

0:33:33.400 --> 0:33:36.240
<v Speaker 2>and how eventually that population is going to run out

0:33:36.400 --> 0:33:40.000
<v Speaker 2>of susceptible individuals. And so are those the basic patterns

0:33:40.000 --> 0:33:42.719
<v Speaker 2>that you see for the curve and what are some

0:33:42.720 --> 0:33:46.520
<v Speaker 2>of the other things that determine the shape of that curve.

0:33:47.000 --> 0:33:50.719
<v Speaker 6>Again really relevant to what's going on right now with COVID.

0:33:51.360 --> 0:33:55.400
<v Speaker 6>The simplest assumption that leads to a curve, the common

0:33:55.440 --> 0:33:57.320
<v Speaker 6>one you see in the front of the textbook and

0:33:57.720 --> 0:33:59.920
<v Speaker 6>the one that we think of when we think of

0:34:00.200 --> 0:34:02.720
<v Speaker 6>diseases where we're not trying specifically to control them in

0:34:02.720 --> 0:34:04.600
<v Speaker 6>any way, but we're just sort of letting them play out,

0:34:05.080 --> 0:34:08.319
<v Speaker 6>is that the curve is driven by immunity, which, in

0:34:08.360 --> 0:34:10.600
<v Speaker 6>the language of an SIR model, is driven by the

0:34:10.680 --> 0:34:15.960
<v Speaker 6>interaction between susceptibles becoming eventually recovered and then being no

0:34:16.000 --> 0:34:19.040
<v Speaker 6>longer eligible to be infected again. So if we go

0:34:19.080 --> 0:34:22.040
<v Speaker 6>back to the conference center picture, you know, being more

0:34:22.080 --> 0:34:24.759
<v Speaker 6>specific with like concepts of are not thrown into the thing.

0:34:25.360 --> 0:34:27.560
<v Speaker 6>You know, if the first infected person shows up in

0:34:27.560 --> 0:34:31.040
<v Speaker 6>that conference center and they're sick, the first thing that

0:34:31.239 --> 0:34:34.040
<v Speaker 6>could actually happen is they go wash their hands and

0:34:34.080 --> 0:34:36.200
<v Speaker 6>they don't actually transmit anybody. We don't hear about it.

0:34:36.640 --> 0:34:39.759
<v Speaker 6>But what can also happen probabilistically, you know, is they

0:34:39.880 --> 0:34:41.400
<v Speaker 6>say the person didn't do that, or they did it,

0:34:41.440 --> 0:34:43.479
<v Speaker 6>and we still got unlucky because they needzed on this rimp.

0:34:44.040 --> 0:34:47.239
<v Speaker 6>Then they transmit to a few people and now you've

0:34:47.280 --> 0:34:50.400
<v Speaker 6>had one person turn into a few infections, and a

0:34:50.400 --> 0:34:53.680
<v Speaker 6>few infections turn into more. As long as this are

0:34:53.760 --> 0:34:57.440
<v Speaker 6>not number is above one, each infection makes more than

0:34:57.480 --> 0:35:00.480
<v Speaker 6>one infection. And so that's the process that leads to

0:35:00.480 --> 0:35:03.000
<v Speaker 6>exponential growth early on. If I started with one thing

0:35:03.040 --> 0:35:04.640
<v Speaker 6>and I get more than one thing, it grows and

0:35:04.640 --> 0:35:07.319
<v Speaker 6>grows and grows and growth. But then where the curve

0:35:07.360 --> 0:35:10.279
<v Speaker 6>comes in. As we said in the room, there's only

0:35:10.320 --> 0:35:12.960
<v Speaker 6>a finite number of people. There's not infinite people with

0:35:13.000 --> 0:35:17.640
<v Speaker 6>infinite handshakes, and so eventually there'll be an infected person

0:35:17.640 --> 0:35:20.839
<v Speaker 6>who starts to whose virus wants to transmit, but they're

0:35:20.880 --> 0:35:24.560
<v Speaker 6>actually their contact is not susceptible anymore, and so their

0:35:24.600 --> 0:35:28.800
<v Speaker 6>ability to transmit is reduced. On average, they'll transmit less often.

0:35:29.080 --> 0:35:32.080
<v Speaker 6>This is this effective reproductive number that is now lower

0:35:32.080 --> 0:35:34.799
<v Speaker 6>than the original basic reproduction number because there's some people

0:35:34.840 --> 0:35:38.000
<v Speaker 6>you can't transmit to. And eventually you'll naturally get to

0:35:38.040 --> 0:35:41.759
<v Speaker 6>a point where the effective reproductive number has become below one,

0:35:42.280 --> 0:35:45.640
<v Speaker 6>or which to say, each new infection can only transmit

0:35:45.680 --> 0:35:48.920
<v Speaker 6>to less than one new person, and you do that

0:35:48.960 --> 0:35:50.799
<v Speaker 6>a bunch of times, and it eventually dies out. If

0:35:50.840 --> 0:35:55.320
<v Speaker 6>nothing else happens. That process of exponential growth early followed

0:35:55.360 --> 0:35:59.960
<v Speaker 6>by exponential decay later works itself out as the curve

0:36:00.080 --> 0:36:03.960
<v Speaker 6>that we typically see. What's really important to think about that, though,

0:36:04.000 --> 0:36:06.160
<v Speaker 6>in the context of COVID, is there are lots of

0:36:06.200 --> 0:36:09.000
<v Speaker 6>other ways to produce curves that aren't just driven by

0:36:09.000 --> 0:36:13.080
<v Speaker 6>immunity in a closed population. What's happening right now all

0:36:13.120 --> 0:36:16.840
<v Speaker 6>over the world is we're generating curves by changing our behavior.

0:36:17.280 --> 0:36:19.640
<v Speaker 6>And so instead of by generating immunity and letting it

0:36:19.719 --> 0:36:22.400
<v Speaker 6>run its course, we're actually changing how we interact with

0:36:22.440 --> 0:36:26.319
<v Speaker 6>each other and manipulating the probability of transmission. In the

0:36:26.320 --> 0:36:29.800
<v Speaker 6>first place, we're manipulating the R zero, not just letting

0:36:29.840 --> 0:36:34.359
<v Speaker 6>the effective reproductive number play out uncontained. And so in

0:36:34.360 --> 0:36:38.880
<v Speaker 6>that situation, if you, in the end manipulate contact enough

0:36:38.920 --> 0:36:41.480
<v Speaker 6>so that the transmission rate goes from exponential growth to

0:36:42.280 --> 0:36:46.480
<v Speaker 6>slowly decaying, that'll look like an EPI curve. But the

0:36:46.560 --> 0:36:49.480
<v Speaker 6>difference between this and the immunity story is we haven't

0:36:49.520 --> 0:36:52.640
<v Speaker 6>consumed the resource of the many susceptible people, and so

0:36:52.680 --> 0:36:54.960
<v Speaker 6>if we were to we if and when we change behavior,

0:36:55.280 --> 0:36:58.239
<v Speaker 6>there's the possibility that the contacts will ramp up again,

0:36:58.320 --> 0:37:00.719
<v Speaker 6>and transmission will ramp up again. We'll get something that

0:37:00.719 --> 0:37:02.759
<v Speaker 6>looks very different than a classical curve. It could have

0:37:02.840 --> 0:37:05.719
<v Speaker 6>multiple humps that could go up and down, and much

0:37:05.760 --> 0:37:08.640
<v Speaker 6>of the future of the world dealing with COVID is

0:37:08.680 --> 0:37:12.640
<v Speaker 6>going to be figuring out how to mitigate the potential

0:37:12.640 --> 0:37:15.480
<v Speaker 6>for rebound as we change behavior so we can keep

0:37:15.480 --> 0:37:17.600
<v Speaker 6>the curve of shape that we're okay with given all

0:37:17.640 --> 0:37:20.320
<v Speaker 6>the consequences that it has the society, both the disease

0:37:20.440 --> 0:37:21.680
<v Speaker 6>and what we're doing about it.

0:37:23.120 --> 0:37:27.400
<v Speaker 2>Yeah, that was really well put. How much behavior plays

0:37:27.400 --> 0:37:30.279
<v Speaker 2>a role in shaping these curves is hugely important, I

0:37:30.280 --> 0:37:33.479
<v Speaker 2>think to keep in mind, it's not just a predetermined thing.

0:37:34.719 --> 0:37:38.200
<v Speaker 2>So can you talk us through some of the assumptions

0:37:38.480 --> 0:37:40.719
<v Speaker 2>that you have to make when you're constructing one of

0:37:40.760 --> 0:37:43.960
<v Speaker 2>these models, and how that kind of relates to the

0:37:44.080 --> 0:37:48.399
<v Speaker 2>uncertainty inherent within models, and how that might affect sort

0:37:48.440 --> 0:37:51.959
<v Speaker 2>of interpretation. So just sort of more generally speaking about

0:37:52.040 --> 0:37:54.960
<v Speaker 2>assumptions and uncertainty in mathematical modeling.

0:37:55.560 --> 0:37:59.160
<v Speaker 6>Okay, Yes, So there's a lot a lot of choices

0:37:59.320 --> 0:38:03.360
<v Speaker 6>that can be made for many different purposes, one purpose

0:38:03.440 --> 0:38:05.720
<v Speaker 6>of which being how quickly do you need an answer

0:38:05.760 --> 0:38:08.320
<v Speaker 6>that's better than the seat of your pants, But also,

0:38:08.440 --> 0:38:12.120
<v Speaker 6>what is your scientific objective, What aspect of the disease

0:38:12.200 --> 0:38:15.040
<v Speaker 6>is most important to the question. You're asking so many

0:38:15.120 --> 0:38:18.600
<v Speaker 6>levels of complexity, many different kinds of assumptions. If your

0:38:18.680 --> 0:38:23.760
<v Speaker 6>objective is to estimate something like the effective reproductive number

0:38:23.920 --> 0:38:27.200
<v Speaker 6>on average, and not to look at the details of

0:38:27.239 --> 0:38:30.000
<v Speaker 6>how asymptomatic people do this, and symptomatic people do that,

0:38:30.080 --> 0:38:32.080
<v Speaker 6>and young people do this, and old people do that,

0:38:32.200 --> 0:38:34.000
<v Speaker 6>and all those kind of details. If you don't care

0:38:34.040 --> 0:38:36.040
<v Speaker 6>about that, you just want to get the average to

0:38:36.160 --> 0:38:39.719
<v Speaker 6>characterize what's happening in a large population overall, you can

0:38:39.760 --> 0:38:43.800
<v Speaker 6>make often pretty simple assumptions that are not particularly different

0:38:43.840 --> 0:38:46.680
<v Speaker 6>than the sir model. We've been discussing with the case

0:38:46.719 --> 0:38:49.080
<v Speaker 6>of COVID that you have to add a behavioral component

0:38:49.560 --> 0:38:53.360
<v Speaker 6>that allows the parameters to change over time even if

0:38:53.400 --> 0:38:56.399
<v Speaker 6>you're not sure why, and so models like that are

0:38:56.480 --> 0:38:59.960
<v Speaker 6>useful if you want to sort of provide situational awareness.

0:39:00.360 --> 0:39:01.759
<v Speaker 6>This is one of the things that we work on

0:39:01.800 --> 0:39:04.880
<v Speaker 6>at IDM, where we sort of use a simple model

0:39:04.920 --> 0:39:07.680
<v Speaker 6>to look at the recent past, try to understand how

0:39:07.680 --> 0:39:10.759
<v Speaker 6>to transmission led to the recent past and maybe do

0:39:11.080 --> 0:39:13.640
<v Speaker 6>what we call like a now cast, which is to say,

0:39:13.800 --> 0:39:17.160
<v Speaker 6>not a long term forecast, but like the data we're

0:39:17.160 --> 0:39:18.919
<v Speaker 6>telling us about what happened a week and a half ago,

0:39:19.480 --> 0:39:22.080
<v Speaker 6>and so can we further estimate what's probably happening right

0:39:22.120 --> 0:39:25.120
<v Speaker 6>now or in the very near future based on continuing

0:39:25.120 --> 0:39:27.839
<v Speaker 6>the trends we've seen before. Those kind of models don't

0:39:27.880 --> 0:39:30.319
<v Speaker 6>have that many parts, they don't have that many parameters,

0:39:30.760 --> 0:39:32.600
<v Speaker 6>But what they're good at is answering one type of

0:39:32.680 --> 0:39:37.120
<v Speaker 6>question descriptively, what's happened recently and what might happen soon

0:39:38.400 --> 0:39:40.759
<v Speaker 6>at a different level of complexity. And something else we

0:39:40.800 --> 0:39:44.360
<v Speaker 6>work on at IDM IS, for example, is all this

0:39:44.400 --> 0:39:50.600
<v Speaker 6>conversation now about testing, tracing, isolation quarantine. How using information,

0:39:50.920 --> 0:39:54.120
<v Speaker 6>using better testing is hopefully going to become an option

0:39:54.440 --> 0:39:57.680
<v Speaker 6>increasingly across the world that helps us get out of

0:39:57.719 --> 0:40:00.279
<v Speaker 6>the current situation with COVID while being able to turn

0:40:00.960 --> 0:40:04.520
<v Speaker 6>some increased level of social and economic activity that makes

0:40:04.600 --> 0:40:08.080
<v Speaker 6>us all happier people. And that kind of thing requires

0:40:08.120 --> 0:40:10.680
<v Speaker 6>a lot of details. You have to understand more about

0:40:10.719 --> 0:40:12.640
<v Speaker 6>how many people live in a house, and how many

0:40:12.640 --> 0:40:15.760
<v Speaker 6>people go to different kinds of offices, and it matters

0:40:15.840 --> 0:40:18.160
<v Speaker 6>if you're trying to test people to tell them to

0:40:18.200 --> 0:40:20.960
<v Speaker 6>stay home before they continue to transmit. You have to

0:40:21.000 --> 0:40:23.920
<v Speaker 6>figure out or make assumptions about is most of the

0:40:23.920 --> 0:40:26.319
<v Speaker 6>transmission happen at the beginning of the infection while people

0:40:26.360 --> 0:40:28.440
<v Speaker 6>don't really know that they're sick yet, or does it

0:40:28.480 --> 0:40:31.399
<v Speaker 6>happen throughout, And then you know, you have to think

0:40:31.440 --> 0:40:34.280
<v Speaker 6>more about how they interact because when a contact tracer

0:40:34.320 --> 0:40:37.319
<v Speaker 6>picks up the phone, they're gonna, you know, they have

0:40:37.360 --> 0:40:39.160
<v Speaker 6>to call somebody. Is that somebody mostly going to be

0:40:39.200 --> 0:40:42.680
<v Speaker 6>household members or classroom members or people you work with,

0:40:42.760 --> 0:40:44.520
<v Speaker 6>or is it you have no idea how to track

0:40:44.560 --> 0:40:46.799
<v Speaker 6>down who is on the subway next to you. And

0:40:47.000 --> 0:40:50.480
<v Speaker 6>those different assumptions matter, And often when you're asking that

0:40:50.560 --> 0:40:54.000
<v Speaker 6>kind of really detailed question where the individual details matter,

0:40:54.040 --> 0:40:56.040
<v Speaker 6>you have to make a lot more assumptions. But you

0:40:56.040 --> 0:40:58.799
<v Speaker 6>can also use a lot more data to help you

0:40:58.800 --> 0:41:02.200
<v Speaker 6>know understand some of those asumptions. And in those kind

0:41:02.239 --> 0:41:04.279
<v Speaker 6>of things, you're often your focus is going to be

0:41:04.360 --> 0:41:07.080
<v Speaker 6>less on let me predict exactly what's going to happen,

0:41:07.160 --> 0:41:09.560
<v Speaker 6>because you often can't really know exactly what's going to happen.

0:41:09.640 --> 0:41:11.440
<v Speaker 6>You can never know that. But it's especially hard in

0:41:11.440 --> 0:41:14.200
<v Speaker 6>these complex models. But your questions might be more like,

0:41:15.000 --> 0:41:17.239
<v Speaker 6>am I pretty sure for lots of ranges of on

0:41:17.440 --> 0:41:20.200
<v Speaker 6>things I don't know, lots of uncertainty that option A

0:41:20.400 --> 0:41:23.279
<v Speaker 6>is better than option B? And am I pretty sure

0:41:23.280 --> 0:41:26.480
<v Speaker 6>that if we try option A we can measure how

0:41:26.520 --> 0:41:28.680
<v Speaker 6>well it does work. I can't predict how well it's

0:41:28.719 --> 0:41:30.960
<v Speaker 6>going to work, but we can figure out afterwards how

0:41:30.960 --> 0:41:33.520
<v Speaker 6>well it was working and adjust based on that. And

0:41:33.600 --> 0:41:35.800
<v Speaker 6>so models that have this sort of more detailed and

0:41:35.880 --> 0:41:39.680
<v Speaker 6>adjust picture can be a lot more assumption rich, but

0:41:39.719 --> 0:41:42.359
<v Speaker 6>then correspondingly are going to be weaker at, you know,

0:41:42.440 --> 0:41:44.839
<v Speaker 6>really making sure they've gotten everything right. And you use

0:41:44.880 --> 0:41:47.040
<v Speaker 6>them in a different way. You try to use them

0:41:47.040 --> 0:41:51.440
<v Speaker 6>to understand branked preferences what's better than what else, unless

0:41:51.520 --> 0:41:53.480
<v Speaker 6>try to use them for a long term forecast, at

0:41:53.520 --> 0:41:56.000
<v Speaker 6>least that sort of approach that I tend to take

0:41:56.040 --> 0:41:56.800
<v Speaker 6>in my own work.

0:41:57.200 --> 0:42:01.080
<v Speaker 2>Okay, interesting, So more simple models are used to kind

0:42:01.080 --> 0:42:03.360
<v Speaker 2>of understand what's going on and what might happen in

0:42:03.360 --> 0:42:07.160
<v Speaker 2>the future, and more complex models more about decision making

0:42:08.040 --> 0:42:10.600
<v Speaker 2>in terms of not what is going to happen, but

0:42:10.680 --> 0:42:13.080
<v Speaker 2>what are the different outcomes that could happen I X

0:42:13.120 --> 0:42:14.400
<v Speaker 2>if we chose.

0:42:14.280 --> 0:42:14.840
<v Speaker 1>X, Y or Z.

0:42:16.160 --> 0:42:18.239
<v Speaker 6>Yeah, that's a great rule of thumb because those are

0:42:18.239 --> 0:42:21.480
<v Speaker 6>where they excel. As you look across the many models

0:42:21.520 --> 0:42:23.840
<v Speaker 6>being used in not just right now, but through like

0:42:23.880 --> 0:42:27.279
<v Speaker 6>the history of epidemiological modeling, the boundaries are blurrier than

0:42:27.320 --> 0:42:30.359
<v Speaker 6>I just made it sound. And so that's one thing

0:42:30.360 --> 0:42:33.399
<v Speaker 6>to pay attention to is if you're seeing a very

0:42:33.440 --> 0:42:37.279
<v Speaker 6>simple model being used for a complex prediction, the hair

0:42:37.360 --> 0:42:38.880
<v Speaker 6>on the back of your neck should stand up and go,

0:42:39.560 --> 0:42:42.839
<v Speaker 6>I wonder. And then conversely, if you're seeing a very

0:42:42.880 --> 0:42:46.239
<v Speaker 6>complex model being used for a fairly simple prediction, there's

0:42:46.280 --> 0:42:49.160
<v Speaker 6>a question about how sure am I that they've explored

0:42:49.200 --> 0:42:51.719
<v Speaker 6>what that simple prediction could be, because the universe of

0:42:51.719 --> 0:42:54.600
<v Speaker 6>their model seems potentially a lot bigger than what I'm

0:42:54.640 --> 0:42:57.560
<v Speaker 6>seeing in the output, And so that's another what do

0:42:57.600 --> 0:42:59.919
<v Speaker 6>I think is actually going on? There? Certainly a question

0:43:00.040 --> 0:43:02.319
<v Speaker 6>professional modelers ask each other all the time when they

0:43:02.320 --> 0:43:03.200
<v Speaker 6>review each other's work.

0:43:03.440 --> 0:43:08.360
<v Speaker 2>H that's really interesting. And so then these different models

0:43:08.440 --> 0:43:12.919
<v Speaker 2>might be used at different stages within a pandemic, let's say,

0:43:12.920 --> 0:43:16.520
<v Speaker 2>for example, to guide different public health measures, and so

0:43:16.560 --> 0:43:19.080
<v Speaker 2>can you talk a little bit about how we might

0:43:19.200 --> 0:43:22.760
<v Speaker 2>use a model differently, or use a different model even

0:43:23.040 --> 0:43:25.960
<v Speaker 2>early on in a pandemic versus during the middle of

0:43:26.040 --> 0:43:28.239
<v Speaker 2>one versus at the end of a pandemic.

0:43:29.200 --> 0:43:33.399
<v Speaker 6>Yes, this is very much what we're seeing play out

0:43:33.440 --> 0:43:37.720
<v Speaker 6>around the world and modeling right now, including within IDM

0:43:37.760 --> 0:43:42.239
<v Speaker 6>my own organization. Early on, you often start simple for

0:43:42.320 --> 0:43:45.120
<v Speaker 6>two reasons. One is you don't know that much, and

0:43:45.200 --> 0:43:48.560
<v Speaker 6>so you want to use fewer, more flexible assumptions that

0:43:48.640 --> 0:43:51.080
<v Speaker 6>can capture what you do know and not try to

0:43:51.080 --> 0:43:53.840
<v Speaker 6>say too much about what you don't and characterize the uncertainty.

0:43:53.920 --> 0:43:56.160
<v Speaker 6>Is usually easier to characterize because you like, there's not

0:43:56.200 --> 0:43:58.760
<v Speaker 6>that much. I can only tell it's this good. Okay,

0:43:59.040 --> 0:44:02.160
<v Speaker 6>that's what it is. But then, also, especially early in

0:44:02.160 --> 0:44:05.040
<v Speaker 6>this pandemic, and this is a continual tension that I

0:44:05.080 --> 0:44:08.160
<v Speaker 6>deal with my professional work, as did my colleagues. Is

0:44:09.160 --> 0:44:11.920
<v Speaker 6>a decent answer soon is better than a great answer

0:44:12.280 --> 0:44:14.960
<v Speaker 6>a year from now, because decisions have to be made

0:44:14.960 --> 0:44:17.640
<v Speaker 6>to affect what happens, and we want to be able

0:44:17.680 --> 0:44:20.840
<v Speaker 6>to help inform on those decisions with our expertise, certainly

0:44:20.840 --> 0:44:23.800
<v Speaker 6>not drive them, but are able to provide a different

0:44:23.800 --> 0:44:26.320
<v Speaker 6>way of looking at the same data to public health

0:44:26.360 --> 0:44:30.360
<v Speaker 6>audiences and elected government, and that adds a useful frame

0:44:30.440 --> 0:44:32.839
<v Speaker 6>to what they're already understand and they're already have as

0:44:32.840 --> 0:44:36.400
<v Speaker 6>their deep expertises. So as we start with simple models,

0:44:36.480 --> 0:44:39.160
<v Speaker 6>we learn more, and also the questions change. You know,

0:44:39.200 --> 0:44:41.000
<v Speaker 6>so like a month ago or a month and a

0:44:41.040 --> 0:44:45.440
<v Speaker 6>half ago now, and the question was, okay, when should

0:44:45.440 --> 0:44:47.759
<v Speaker 6>we start doing some physical distancing and how well will

0:44:47.800 --> 0:44:50.480
<v Speaker 6>it work? And then the question was, well, how well

0:44:50.480 --> 0:44:52.440
<v Speaker 6>did it work? And we're starting to find that a

0:44:52.480 --> 0:44:55.520
<v Speaker 6>lot of places all over the world it took exponentially

0:44:55.560 --> 0:44:59.400
<v Speaker 6>going catastrophe and has slowed it down to close to

0:44:59.440 --> 0:45:02.920
<v Speaker 6>something to sort of sustain indefinitely with the effective reproduct

0:45:03.000 --> 0:45:06.360
<v Speaker 6>number goes one. Knowing the reproductive number changed is a

0:45:06.400 --> 0:45:09.200
<v Speaker 6>slightly more complex thing than the first question, but still

0:45:09.200 --> 0:45:11.480
<v Speaker 6>fairly simple, and you can estimate it lots of different

0:45:11.480 --> 0:45:13.960
<v Speaker 6>ways and lots of different groups are doing this. Where

0:45:14.000 --> 0:45:16.319
<v Speaker 6>the questions are going now and where the models are

0:45:16.320 --> 0:45:22.000
<v Speaker 6>going now is how do we better understand why the

0:45:22.040 --> 0:45:24.960
<v Speaker 6>effective reproductive number change the way? It is not just

0:45:25.080 --> 0:45:28.440
<v Speaker 6>that it changed, but what specifically of the many things

0:45:28.480 --> 0:45:31.040
<v Speaker 6>everyone around the world just changed in the last six weeks,

0:45:31.680 --> 0:45:35.719
<v Speaker 6>what specifically had the biggest contributions to the change what

0:45:35.840 --> 0:45:38.120
<v Speaker 6>specifically was no big deal, and we could just let

0:45:38.160 --> 0:45:40.040
<v Speaker 6>it go back to close to normal and it will

0:45:40.040 --> 0:45:43.000
<v Speaker 6>probably be fine. Then okay, if we want to start

0:45:43.080 --> 0:45:47.440
<v Speaker 6>doing newer strategies, strategies that are going to be more specific,

0:45:48.200 --> 0:45:50.000
<v Speaker 6>how do they play through? You know, if you have

0:45:50.040 --> 0:45:53.799
<v Speaker 6>better information, you don't have to have everybody change their

0:45:53.840 --> 0:45:55.680
<v Speaker 6>behavior to the same extent. You might be able to

0:45:55.719 --> 0:45:58.400
<v Speaker 6>have less or more and might be able to respond

0:45:58.520 --> 0:46:01.600
<v Speaker 6>to the virus itself. And so that again adds another

0:46:01.680 --> 0:46:04.600
<v Speaker 6>level of complexity, because it's not just modeling what the

0:46:04.680 --> 0:46:07.919
<v Speaker 6>virus is doing, but it's then modeling how are we

0:46:08.080 --> 0:46:10.439
<v Speaker 6>societally likely to respond to what the virus is doing

0:46:10.440 --> 0:46:13.560
<v Speaker 6>and what are its consequences. And so the complexity goes

0:46:13.640 --> 0:46:16.760
<v Speaker 6>up as the questions go up, and as the time

0:46:16.800 --> 0:46:19.239
<v Speaker 6>moves on, the questions are getting more complex. And also

0:46:19.239 --> 0:46:23.200
<v Speaker 6>we're learning more scientifically. You know, often we learn about

0:46:23.239 --> 0:46:26.680
<v Speaker 6>a disease over many years. Most science moves, you know,

0:46:26.760 --> 0:46:30.360
<v Speaker 6>over the timescale of years, and here we're trying to

0:46:30.560 --> 0:46:33.239
<v Speaker 6>learn over weeks. And so we're trying to ask these

0:46:33.280 --> 0:46:37.640
<v Speaker 6>complicated questions, build complicated models, understand the limitations of our

0:46:37.680 --> 0:46:41.120
<v Speaker 6>simple models that we haven't ever confronted before at the

0:46:41.120 --> 0:46:43.560
<v Speaker 6>same time as everyone's trying to make everything better and

0:46:43.640 --> 0:46:46.680
<v Speaker 6>change what's happening, And so that leads to a whole

0:46:46.719 --> 0:46:50.080
<v Speaker 6>other cloud of uncertainty and challenge that's just inherent to

0:46:50.600 --> 0:46:53.560
<v Speaker 6>where we are at, both scientifically and as a community

0:46:53.600 --> 0:46:54.359
<v Speaker 6>dealing with this thing.

0:46:55.000 --> 0:46:59.759
<v Speaker 2>Yeah, and so you know, talking now shifting more specifically

0:47:00.080 --> 0:47:05.160
<v Speaker 2>into COVID nineteen models and predictions and forecasts, can you

0:47:05.320 --> 0:47:07.920
<v Speaker 2>just kind of walk us through what a basic model

0:47:08.000 --> 0:47:11.719
<v Speaker 2>of COVID nineteen might look like, for instance, like, would

0:47:11.719 --> 0:47:15.600
<v Speaker 2>it follow the same sir model that you described earlier.

0:47:16.440 --> 0:47:23.000
<v Speaker 6>Yes, the simplest models often follow the same SIR framework,

0:47:23.320 --> 0:47:29.040
<v Speaker 6>with one very important exception, which is there's nowhere on

0:47:29.080 --> 0:47:32.440
<v Speaker 6>earth to our knowledge except for maybe some small villages

0:47:32.480 --> 0:47:35.600
<v Speaker 6>here or there that have had really severe epidemics early

0:47:35.640 --> 0:47:40.239
<v Speaker 6>on where immunity is the dominant reason that the transmission

0:47:40.320 --> 0:47:43.239
<v Speaker 6>rate is changing. So we can't just rely on the

0:47:43.360 --> 0:47:45.960
<v Speaker 6>sort of chapter one of the textbook, you know, immunity

0:47:45.960 --> 0:47:49.000
<v Speaker 6>produce as a bell shaped carve. We have to incorporate

0:47:49.080 --> 0:47:52.440
<v Speaker 6>some concept of behavior, and that can be as simple

0:47:52.480 --> 0:47:55.520
<v Speaker 6>as the transmission rate changes over time. In ways that

0:47:55.520 --> 0:47:58.759
<v Speaker 6>I'll estimate but not really understand why. Models like that

0:47:58.800 --> 0:48:02.279
<v Speaker 6>have been useful for COVID for understanding, you know, what

0:48:02.400 --> 0:48:05.319
<v Speaker 6>is changing. Models that are that simple have been also

0:48:05.400 --> 0:48:08.040
<v Speaker 6>useful for sort of understanding in the next few weeks

0:48:08.120 --> 0:48:11.200
<v Speaker 6>what is likely to happen if trends continue as they have.

0:48:12.000 --> 0:48:15.759
<v Speaker 6>That was very useful for hospital utilization predictions. You know,

0:48:16.280 --> 0:48:19.399
<v Speaker 6>how worried should we be about overwhelming healthcare system? And

0:48:19.520 --> 0:48:22.920
<v Speaker 6>many of the early predictions going back to February, we're

0:48:22.960 --> 0:48:25.440
<v Speaker 6>in the focus of Okay, we have no idea what's

0:48:25.480 --> 0:48:28.040
<v Speaker 6>going to happen, but what if we do nothing. A

0:48:28.080 --> 0:48:30.960
<v Speaker 6>simple model, even or a complex model in that moment

0:48:31.480 --> 0:48:34.319
<v Speaker 6>is an exponential growth model, and that's that, and it's

0:48:34.400 --> 0:48:37.360
<v Speaker 6>going to say, you know, if we do nothing with COVID,

0:48:38.000 --> 0:48:41.040
<v Speaker 6>really dire outcomes that we haven't seen in a century

0:48:41.280 --> 0:48:44.080
<v Speaker 6>are from an infectious disease are going to happen. And

0:48:44.160 --> 0:48:48.480
<v Speaker 6>so from there we sort of say, okay, that's one

0:48:48.480 --> 0:48:53.080
<v Speaker 6>of useful prediction. But then, unlike weather prediction, our models

0:48:53.080 --> 0:48:56.239
<v Speaker 6>actually change what happens, which is an important thing to

0:48:56.280 --> 0:49:00.160
<v Speaker 6>understand for epidemiology. Like when modeling and data together, the

0:49:00.320 --> 0:49:03.600
<v Speaker 6>clearly tell a story. It's on us, as you know,

0:49:03.640 --> 0:49:06.840
<v Speaker 6>a learning species, to then act in response to that

0:49:06.920 --> 0:49:09.040
<v Speaker 6>story so that the worst doesn't happen. And one of

0:49:09.080 --> 0:49:12.000
<v Speaker 6>the things that's been super gratifying for me, just as

0:49:12.040 --> 0:49:15.279
<v Speaker 6>a person, forget about as a professional with COVID, is

0:49:15.280 --> 0:49:18.120
<v Speaker 6>watching like so much of the world actually make major

0:49:18.239 --> 0:49:22.120
<v Speaker 6>changes to save lots of lives that have completely changed

0:49:22.160 --> 0:49:24.080
<v Speaker 6>with those early outcomes could have been to where they

0:49:24.080 --> 0:49:27.239
<v Speaker 6>are right now. Models that can adapt to that continual

0:49:27.320 --> 0:49:29.839
<v Speaker 6>process are going to do better in the future then

0:49:30.160 --> 0:49:32.040
<v Speaker 6>models that were more rigid about what we thought we

0:49:32.160 --> 0:49:34.280
<v Speaker 6>understood early on and are just trying to keep shoving

0:49:34.320 --> 0:49:34.760
<v Speaker 6>it forward.

0:49:36.320 --> 0:49:39.240
<v Speaker 2>You brought up a very good point about models telling

0:49:39.280 --> 0:49:42.440
<v Speaker 2>a story that is sort of a choose your own adventure,

0:49:43.080 --> 0:49:45.080
<v Speaker 2>like a snake with his tail in his mouth sort

0:49:45.120 --> 0:49:46.480
<v Speaker 2>of a story there.

0:49:47.000 --> 0:49:50.480
<v Speaker 6>Yeah, And that's like one of the things that I,

0:49:50.880 --> 0:49:53.800
<v Speaker 6>you know, I certainly want us to be really careful about,

0:49:53.800 --> 0:49:56.720
<v Speaker 6>and I try my best and I probably don't always succeed,

0:49:58.000 --> 0:50:01.160
<v Speaker 6>is to be really mindful of the difference between like

0:50:01.200 --> 0:50:05.040
<v Speaker 6>a prediction and a scenario. And what I think the

0:50:05.080 --> 0:50:07.760
<v Speaker 6>difference is, right is we're often like again using weather

0:50:07.840 --> 0:50:11.480
<v Speaker 6>as the modeling system that almost everybody's familiar with. That's

0:50:11.520 --> 0:50:13.640
<v Speaker 6>a prediction system where we have an enormous amount of

0:50:13.719 --> 0:50:16.160
<v Speaker 6>understanding of the physics. We have a lot of measurements

0:50:16.160 --> 0:50:19.520
<v Speaker 6>happening all over the world, and on the scale of

0:50:19.680 --> 0:50:23.880
<v Speaker 6>weather days, not centuries or at least years, we don't

0:50:23.920 --> 0:50:26.239
<v Speaker 6>do anything that changes the weather, and so we can

0:50:26.280 --> 0:50:28.799
<v Speaker 6>get better and better at predicting it, and it'll play

0:50:28.800 --> 0:50:30.799
<v Speaker 6>out as we get better predicting it, it'll play out

0:50:30.800 --> 0:50:32.840
<v Speaker 6>like we said it was going to happen. And modeling

0:50:33.000 --> 0:50:35.960
<v Speaker 6>is in that context really a prediction tool. In something

0:50:36.000 --> 0:50:39.840
<v Speaker 6>like COVID, I think of it more for the future

0:50:40.200 --> 0:50:45.880
<v Speaker 6>as a scenario exploration tool, because the predictions would depend

0:50:46.120 --> 0:50:50.200
<v Speaker 6>entirely on the future behavior of the community that the

0:50:50.320 --> 0:50:53.600
<v Speaker 6>COVID is transmitting towards, at least until far in the

0:50:53.600 --> 0:50:57.719
<v Speaker 6>future where the stronger effects of hopefully you know, some

0:50:57.760 --> 0:51:00.319
<v Speaker 6>significant immunity, which is itself still on certain and as

0:51:00.320 --> 0:51:02.319
<v Speaker 6>to what that's going to look like, we'll kick in

0:51:02.360 --> 0:51:05.960
<v Speaker 6>and make some of this story simpler, and so certainly

0:51:05.960 --> 0:51:09.319
<v Speaker 6>in our science communication we try to emphasize that, like,

0:51:09.680 --> 0:51:12.200
<v Speaker 6>here's what could happen in the next few weeks if

0:51:12.239 --> 0:51:15.319
<v Speaker 6>everything stays the same, and here's what could happen if

0:51:15.360 --> 0:51:18.239
<v Speaker 6>things change to make transmission a little less or they

0:51:18.320 --> 0:51:21.160
<v Speaker 6>change to make transmission a little more. And so that's why,

0:51:21.200 --> 0:51:24.680
<v Speaker 6>again the emphasis on scenarios is to help visualize how

0:51:25.280 --> 0:51:29.200
<v Speaker 6>choices of some change could lead to different outcomes. And

0:51:29.239 --> 0:51:32.239
<v Speaker 6>that's different than prediction in my mind, because in the end,

0:51:32.280 --> 0:51:34.400
<v Speaker 6>it's the choices will affect the scenario that happens, and

0:51:34.440 --> 0:51:35.600
<v Speaker 6>we don't know that in advance.

0:51:36.320 --> 0:51:39.160
<v Speaker 2>Yeah, that's such a good point. And I think, you know,

0:51:39.520 --> 0:51:42.279
<v Speaker 2>I've seen a little bit here and there people saying, oh, well,

0:51:42.280 --> 0:51:44.600
<v Speaker 2>why do we have to just severe lockdown if the

0:51:44.640 --> 0:51:46.839
<v Speaker 2>cases are so low, And it's sort of like, well,

0:51:46.960 --> 0:51:49.400
<v Speaker 2>that's the cases are so low because we had a

0:51:49.480 --> 0:51:52.360
<v Speaker 2>severe lockdown. Likes, it goes hand in hand.

0:51:53.360 --> 0:51:55.480
<v Speaker 6>Yeah, I wish I remember where I saw this on

0:51:55.480 --> 0:51:58.799
<v Speaker 6>Twitter first, is that that's why you take your medicine, Like,

0:51:58.880 --> 0:52:01.719
<v Speaker 6>you start feeling sick, and then you take medicine to

0:52:01.800 --> 0:52:05.160
<v Speaker 6>make you not get really sick and potentially die. Physical

0:52:05.239 --> 0:52:09.160
<v Speaker 6>distancing is the medicine for a community transmissible disease at

0:52:09.160 --> 0:52:11.040
<v Speaker 6>this point in time, one for which we don't have

0:52:11.080 --> 0:52:14.400
<v Speaker 6>other good options. And so yeah, we took the medicine,

0:52:14.600 --> 0:52:17.040
<v Speaker 6>things are getting better, and like you know, not taking

0:52:17.080 --> 0:52:18.960
<v Speaker 6>your full course of antibiotics, if we stop taking the

0:52:19.000 --> 0:52:20.439
<v Speaker 6>medicine too soon, it could get worse again.

0:52:20.760 --> 0:52:25.680
<v Speaker 2>Exactly, A great point. Yeah, it's definitely, definitely true. So

0:52:26.080 --> 0:52:28.040
<v Speaker 2>one of the questions I have is a little bit

0:52:28.080 --> 0:52:32.640
<v Speaker 2>specific as regards to sort of building these COVID nineteen models.

0:52:32.960 --> 0:52:36.560
<v Speaker 2>And I was just wondering, you know, whether whether some

0:52:36.719 --> 0:52:39.839
<v Speaker 2>models use just lab confirmed cases, so like people who

0:52:39.880 --> 0:52:43.640
<v Speaker 2>have tested positive or have been tested and tested positive,

0:52:44.040 --> 0:52:47.200
<v Speaker 2>or whether there are any models that are also extrapolating,

0:52:47.520 --> 0:52:51.480
<v Speaker 2>you know, based on the number of asymptomatic individuals or

0:52:52.160 --> 0:52:55.480
<v Speaker 2>the people who seem to be clinically diagnosed positive just

0:52:55.520 --> 0:52:59.000
<v Speaker 2>based on symptoms alone. Whether these models are using just

0:52:59.400 --> 0:53:03.880
<v Speaker 2>lab diagnos cases or also clinically diagnosed cases of COVID

0:53:03.960 --> 0:53:06.160
<v Speaker 2>nineteen as well.

0:53:06.280 --> 0:53:09.560
<v Speaker 6>Yeah, it's another really important question, and the answer is

0:53:09.640 --> 0:53:13.239
<v Speaker 6>there are models that are using just clinically confirmed case

0:53:13.280 --> 0:53:16.400
<v Speaker 6>are just the lab confirmed cases. There are models using

0:53:16.840 --> 0:53:21.719
<v Speaker 6>multiple case definitions, there are models using not just case definitions.

0:53:21.719 --> 0:53:24.719
<v Speaker 6>But also you know, oh, we learned this thing from

0:53:24.760 --> 0:53:27.280
<v Speaker 6>a paper and send Jen and we think it's probably

0:53:27.320 --> 0:53:29.960
<v Speaker 6>the same in such and such city, So let's just

0:53:30.000 --> 0:53:32.680
<v Speaker 6>copy that part over and use it until we learn

0:53:32.719 --> 0:53:34.840
<v Speaker 6>something better about the city that we're looking at the moment.

0:53:35.400 --> 0:53:39.120
<v Speaker 6>Lots of different data sources. I think the way to

0:53:39.200 --> 0:53:42.319
<v Speaker 6>think about this is again, what is the objective of

0:53:42.440 --> 0:53:45.440
<v Speaker 6>the model and also where what kind of data is

0:53:45.480 --> 0:53:48.520
<v Speaker 6>most reliable, because that's also super important right now with COVID,

0:53:49.160 --> 0:53:52.760
<v Speaker 6>we make assumptions about how those different data streams represent

0:53:52.800 --> 0:53:56.520
<v Speaker 6>a sample of the total population. Models can be more

0:53:56.640 --> 0:53:59.200
<v Speaker 6>or less complex in how they handle those assumptions, and

0:53:59.360 --> 0:54:02.000
<v Speaker 6>they can all feel together to tell one story about

0:54:02.040 --> 0:54:06.320
<v Speaker 6>what's happening underneath with the population prevalence. And one of

0:54:06.400 --> 0:54:08.160
<v Speaker 6>the exciting things too, is there's also starting to be

0:54:08.160 --> 0:54:11.640
<v Speaker 6>more data, more projects that really set out to learn

0:54:11.800 --> 0:54:13.879
<v Speaker 6>about the parts of the population that don't just show

0:54:13.920 --> 0:54:16.640
<v Speaker 6>up in clinical case reporting or lab come firm case reporting.

0:54:17.040 --> 0:54:18.960
<v Speaker 6>And as that kind of data becomes more available, these

0:54:19.000 --> 0:54:23.040
<v Speaker 6>sort of surveys, both zerological surveys that look for immunity history,

0:54:23.400 --> 0:54:27.239
<v Speaker 6>also shedding surveys that look for actively shedding virus and

0:54:27.320 --> 0:54:30.000
<v Speaker 6>people who didn't show up at the hospital are giving

0:54:30.080 --> 0:54:32.440
<v Speaker 6>us yet another type of data stream that again tells

0:54:32.440 --> 0:54:35.359
<v Speaker 6>a story about the population. And depending on the model

0:54:35.400 --> 0:54:37.560
<v Speaker 6>ors objective and what data they have access to, you

0:54:37.600 --> 0:54:39.800
<v Speaker 6>have more or less complex pieces that you put together

0:54:39.840 --> 0:54:42.080
<v Speaker 6>to tell a coherent story about the whole population.

0:54:44.000 --> 0:54:48.760
<v Speaker 2>Yeah, looking back on these earlier models of COVID nineteen,

0:54:48.800 --> 0:54:51.279
<v Speaker 2>so let's say, like a month ago, what can we

0:54:51.360 --> 0:54:54.319
<v Speaker 2>take away from the performance of a model. Like if

0:54:54.360 --> 0:54:57.160
<v Speaker 2>we evaluate a model a month or two months after

0:54:57.239 --> 0:55:00.840
<v Speaker 2>it was first created and we evalue, wait, how well

0:55:00.920 --> 0:55:04.120
<v Speaker 2>it actually measured up to what we saw? What does

0:55:04.160 --> 0:55:05.879
<v Speaker 2>that tell us? What does that? What does that mean

0:55:05.960 --> 0:55:07.319
<v Speaker 2>to us to.

0:55:07.320 --> 0:55:10.640
<v Speaker 6>Evaluate a model prediction or a model even a model

0:55:10.680 --> 0:55:12.920
<v Speaker 6>result of any kind from a few months ago or

0:55:12.920 --> 0:55:16.600
<v Speaker 6>a couple months ago. The most important thing from viewing

0:55:16.640 --> 0:55:19.040
<v Speaker 6>it as a as a modeling scientist, right, so viewing

0:55:19.040 --> 0:55:22.480
<v Speaker 6>it by professional lens is what was the objective that

0:55:22.480 --> 0:55:24.960
<v Speaker 6>that model set out to do? And then how do

0:55:25.040 --> 0:55:28.920
<v Speaker 6>we judge it against that objective? So one example we

0:55:28.920 --> 0:55:31.759
<v Speaker 6>talked about earlier is like models that in you know,

0:55:31.840 --> 0:55:38.200
<v Speaker 6>early February predicted you know, millions of deaths with unmitigated outcome. Well,

0:55:38.400 --> 0:55:40.719
<v Speaker 6>so far that hasn't happened because we didn't have an

0:55:40.800 --> 0:55:43.840
<v Speaker 6>unmitigated world. But we might be able to judge that

0:55:43.920 --> 0:55:47.440
<v Speaker 6>prediction on how did it, you know, capture what was

0:55:47.480 --> 0:55:52.280
<v Speaker 6>known at the time, how did it influence decision making

0:55:52.320 --> 0:55:55.239
<v Speaker 6>in a direction that epidemiologists collectively think is the right

0:55:55.239 --> 0:55:59.440
<v Speaker 6>direction or not. Was the presenter of the model, you know,

0:55:59.480 --> 0:56:01.359
<v Speaker 6>sort of humble about what they were trying to do

0:56:01.400 --> 0:56:03.759
<v Speaker 6>and clear about what they were trying to do or

0:56:04.239 --> 0:56:07.560
<v Speaker 6>did they overreach based on like I started here and

0:56:07.760 --> 0:56:10.439
<v Speaker 6>actually what what I tried to talk about was three

0:56:10.480 --> 0:56:12.960
<v Speaker 6>other things that not really what I focused on. That's

0:56:12.960 --> 0:56:16.680
<v Speaker 6>sort of a scientific integrity component. Then there are models

0:56:16.680 --> 0:56:18.399
<v Speaker 6>that looked at, well, what if you make this change

0:56:18.480 --> 0:56:21.400
<v Speaker 6>or that change or the other, and then something we

0:56:21.440 --> 0:56:25.560
<v Speaker 6>can judge is working backwards both which scenario seems to

0:56:25.560 --> 0:56:27.960
<v Speaker 6>be what played out. That's useful because it helps us

0:56:28.000 --> 0:56:30.160
<v Speaker 6>anchor what we've seen to what we were what we

0:56:30.200 --> 0:56:33.839
<v Speaker 6>were expecting in the past. But then we can also

0:56:33.880 --> 0:56:35.759
<v Speaker 6>go further into the model if the model has the

0:56:35.800 --> 0:56:38.319
<v Speaker 6>details and say did it get the right answer for

0:56:38.360 --> 0:56:40.640
<v Speaker 6>the right reasons based on new science that we've learned,

0:56:40.719 --> 0:56:43.160
<v Speaker 6>or did it get lucky? This is how I view

0:56:43.160 --> 0:56:45.760
<v Speaker 6>it as a professional. I think if I was just viewing,

0:56:45.800 --> 0:56:48.239
<v Speaker 6>like when I watch the news at night or on

0:56:48.320 --> 0:56:52.719
<v Speaker 6>my phone, there's more of a sense of can I

0:56:52.880 --> 0:56:56.840
<v Speaker 6>see how the narrative that's being spun around this model

0:56:57.280 --> 0:57:00.719
<v Speaker 6>connects to what the figure the graph actually looks like,

0:57:01.320 --> 0:57:04.520
<v Speaker 6>and if it does, I feel better about the coherence

0:57:04.560 --> 0:57:07.680
<v Speaker 6>between the two. Even if the prediction doesn't necessarily play

0:57:07.680 --> 0:57:10.160
<v Speaker 6>out correctly, because then the next thing I'm looking for

0:57:10.239 --> 0:57:12.360
<v Speaker 6>is if the prediction wasn't correct, how did that model

0:57:12.440 --> 0:57:15.279
<v Speaker 6>or that model or address that discrepancy and did we

0:57:15.360 --> 0:57:18.720
<v Speaker 6>learn something from that discrepancy or not. If we do,

0:57:18.840 --> 0:57:22.160
<v Speaker 6>and if it's communicated in a learning way, and we

0:57:22.200 --> 0:57:24.120
<v Speaker 6>can point to like, oh, this was this assumption that

0:57:24.160 --> 0:57:25.840
<v Speaker 6>didn't play out the same way we thought and that's

0:57:25.840 --> 0:57:28.160
<v Speaker 6>why the outcome was different, then I think that's a

0:57:28.200 --> 0:57:32.360
<v Speaker 6>really successful effort. But it's a it's different than the

0:57:32.560 --> 0:57:35.400
<v Speaker 6>communication question. Everybody wants to know what's going to happen,

0:57:35.440 --> 0:57:38.240
<v Speaker 6>and I come back to I don't think that's quite

0:57:38.280 --> 0:57:39.880
<v Speaker 6>the right way to think about what these models are

0:57:39.880 --> 0:57:42.360
<v Speaker 6>capable of doing. At least maybe for a few weeks,

0:57:42.480 --> 0:57:44.480
<v Speaker 6>you can guess that things don't change that fast and

0:57:44.560 --> 0:57:47.520
<v Speaker 6>it'll be predictive. But beyond that, again, it comes down

0:57:47.560 --> 0:57:50.360
<v Speaker 6>to the choices we're making a society, and that's going

0:57:50.440 --> 0:57:52.240
<v Speaker 6>to make it hard to really use prediction as the

0:57:52.280 --> 0:57:52.840
<v Speaker 6>right lens.

0:57:53.640 --> 0:57:57.360
<v Speaker 2>Yeah, So I won't ask you then to predict what's

0:57:57.400 --> 0:58:00.760
<v Speaker 2>going to happen, but I will ask there is any

0:58:00.880 --> 0:58:04.560
<v Speaker 2>agreement among models as to what policies might be the

0:58:04.600 --> 0:58:07.200
<v Speaker 2>best for the ideal scenario, which is, you know, the

0:58:07.280 --> 0:58:10.520
<v Speaker 2>least number of cases as possible and the you know,

0:58:10.760 --> 0:58:11.840
<v Speaker 2>fewest deaths.

0:58:12.400 --> 0:58:16.880
<v Speaker 6>Yeah, and ideally with with some sort of relatively tolerable

0:58:16.920 --> 0:58:21.439
<v Speaker 6>societal cost, which is often an additional layer of complication here.

0:58:21.760 --> 0:58:24.200
<v Speaker 6>So you asked like, is there a consensus from of

0:58:24.240 --> 0:58:27.040
<v Speaker 6>different things from the models, And I would actually put

0:58:27.080 --> 0:58:28.840
<v Speaker 6>it as I think there's more of a consensus among

0:58:28.880 --> 0:58:32.200
<v Speaker 6>the model earths, and the difference is that aspect of

0:58:32.240 --> 0:58:35.800
<v Speaker 6>how fast everything's moving. Of you know, often those of

0:58:35.880 --> 0:58:38.240
<v Speaker 6>us who've made a career out of thinking about epidemiological

0:58:38.280 --> 0:58:41.520
<v Speaker 6>modeling can think through things that we've not yet had

0:58:41.560 --> 0:58:43.480
<v Speaker 6>time or our colleagues have not yet had time to

0:58:43.520 --> 0:58:46.800
<v Speaker 6>actually turn into real math. You can run that multiverse

0:58:46.840 --> 0:58:50.000
<v Speaker 6>on your computer cluster and really play out, and so

0:58:50.200 --> 0:58:52.680
<v Speaker 6>you will see pieces of stories that are out there

0:58:52.720 --> 0:58:54.640
<v Speaker 6>now and over the next few months, it will continue

0:58:54.640 --> 0:58:57.800
<v Speaker 6>to be more and more And I think the consensus

0:58:57.880 --> 0:59:02.400
<v Speaker 6>at the moment is something like the following, at least

0:59:02.800 --> 0:59:05.120
<v Speaker 6>certainly I should say more carefully, but I'm not sure

0:59:05.120 --> 0:59:07.400
<v Speaker 6>if it's the consensus. It's the camp that I fall into.

0:59:07.520 --> 0:59:11.160
<v Speaker 6>That's probably the more safe way to say. So, we

0:59:11.320 --> 0:59:15.920
<v Speaker 6>do expect that to keep deaths under control and to

0:59:16.000 --> 0:59:19.400
<v Speaker 6>keep hospitals from being overwhelmed, that there will be some

0:59:19.480 --> 0:59:22.720
<v Speaker 6>physical distancing for a very long time. I have a

0:59:22.800 --> 0:59:25.240
<v Speaker 6>very long time. Could be months, it could be more

0:59:25.280 --> 0:59:28.000
<v Speaker 6>than a year. It could It will depend likely on

0:59:28.080 --> 0:59:32.040
<v Speaker 6>the availability of an effective vaccine, but that to meet

0:59:32.080 --> 0:59:35.440
<v Speaker 6>the goal of not letting COVID rip and hit everybody

0:59:35.480 --> 0:59:38.320
<v Speaker 6>it's going to hit, we're likely to still need some

0:59:38.360 --> 0:59:43.120
<v Speaker 6>physical distancing over time. But added to that, there's a

0:59:43.120 --> 0:59:47.160
<v Speaker 6>lot of interest in interventions that are more specific to

0:59:47.200 --> 0:59:50.840
<v Speaker 6>control the transmission. The popular, you know talk of the

0:59:50.880 --> 0:59:55.160
<v Speaker 6>moment is test, trace, isolate, and quarantine, those kind of interventions.

0:59:55.240 --> 0:59:59.360
<v Speaker 6>Contact tracing interventions. They look for people who have the

0:59:59.400 --> 1:00:02.080
<v Speaker 6>disease and then try to get ahead of where the

1:00:02.120 --> 1:00:05.720
<v Speaker 6>disease is transmitting by interviewing them about their social network

1:00:06.120 --> 1:00:08.240
<v Speaker 6>and connecting to the people that they were likely to

1:00:08.280 --> 1:00:11.280
<v Speaker 6>have transmitted to and ask those people to change their behavior,

1:00:11.360 --> 1:00:13.800
<v Speaker 6>to stay home that they might be sick, to get

1:00:13.800 --> 1:00:17.640
<v Speaker 6>a test, find out if they are, and otherwise make

1:00:17.680 --> 1:00:19.520
<v Speaker 6>it so that it's harder for those people to continue

1:00:19.560 --> 1:00:22.680
<v Speaker 6>to transmit on and that will prone transmission trains and

1:00:22.760 --> 1:00:25.720
<v Speaker 6>keep things under control. To do that is a really

1:00:25.760 --> 1:00:29.600
<v Speaker 6>resource intensive thing. And so most countries, although not all,

1:00:30.000 --> 1:00:32.640
<v Speaker 6>are in a position where we weren't. We weren't sitting

1:00:32.680 --> 1:00:34.320
<v Speaker 6>on a squad that was ready to do this for

1:00:34.360 --> 1:00:36.920
<v Speaker 6>everywhere in the world for a global pandemic, and so

1:00:37.000 --> 1:00:39.600
<v Speaker 6>there's a resource question about how feasible that will be.

1:00:40.200 --> 1:00:43.080
<v Speaker 6>And in the modeling, one of the very active areas

1:00:43.080 --> 1:00:46.880
<v Speaker 6>of research is how do you trade off the blanket

1:00:46.880 --> 1:00:50.000
<v Speaker 6>physical distancing which is required when you don't know where

1:00:50.040 --> 1:00:54.920
<v Speaker 6>the disease is, and the contact tracing based interventions that

1:00:55.280 --> 1:00:57.720
<v Speaker 6>will be more effective as you have better information about

1:00:57.760 --> 1:00:59.960
<v Speaker 6>who's getting sick and as you're missing fewer and fewer

1:01:00.040 --> 1:01:02.920
<v Speaker 6>people with that information. I think, and I think a

1:01:02.960 --> 1:01:05.800
<v Speaker 6>lot of my colleagues think that like the path forward

1:01:05.880 --> 1:01:09.360
<v Speaker 6>is going to be the most realistic to be determined

1:01:09.440 --> 1:01:14.160
<v Speaker 6>based on resources and coordination and politics and behavior of

1:01:14.200 --> 1:01:17.120
<v Speaker 6>how does that trade off play out with the ideal

1:01:17.200 --> 1:01:20.520
<v Speaker 6>that we get to better and better information that makes

1:01:20.680 --> 1:01:25.000
<v Speaker 6>less and less physical distancing necessary. But one caveat I

1:01:25.000 --> 1:01:26.960
<v Speaker 6>want to add that's intrinsic to COVID that we think

1:01:26.960 --> 1:01:29.120
<v Speaker 6>we've learned in the last few months is that there's

1:01:29.120 --> 1:01:32.920
<v Speaker 6>definitely some COVID transmission that happens before people are showing symptoms,

1:01:33.160 --> 1:01:36.480
<v Speaker 6>and there's definitely some people who show negligible or really

1:01:36.520 --> 1:01:39.280
<v Speaker 6>no symptoms, And so there's likely to be a fundamental

1:01:39.320 --> 1:01:42.439
<v Speaker 6>limit on even if you had infinite resources being able

1:01:42.480 --> 1:01:44.920
<v Speaker 6>to track down every infection and stop it from transmitting,

1:01:45.360 --> 1:01:47.680
<v Speaker 6>just because they'll be transmission events for which there's nothing

1:01:47.720 --> 1:01:50.480
<v Speaker 6>you can observe. And so that feedback is why we

1:01:50.520 --> 1:01:54.600
<v Speaker 6>think it's not likely to literally go back to normal

1:01:54.680 --> 1:01:58.520
<v Speaker 6>plus contact tracing if we want to control the transmission

1:01:58.560 --> 1:02:00.520
<v Speaker 6>to the levels that we've you know, are hoping to

1:02:00.560 --> 1:02:02.920
<v Speaker 6>do it now. So I think it's something like that

1:02:03.040 --> 1:02:08.000
<v Speaker 6>is the short term consensus. A really important uncertainty is

1:02:08.000 --> 1:02:10.680
<v Speaker 6>that how does this play out, you know, two years

1:02:10.680 --> 1:02:15.760
<v Speaker 6>from now or three years from now. Is how durable

1:02:16.160 --> 1:02:19.400
<v Speaker 6>is the immunity that COVID nineteen generates and people who

1:02:19.400 --> 1:02:22.160
<v Speaker 6>get infected, and we don't know where COVID is in

1:02:22.200 --> 1:02:25.200
<v Speaker 6>this space, and it's it's reasonable that it could be on,

1:02:25.520 --> 1:02:27.920
<v Speaker 6>you know, sort of towards either extreme, because on one hand,

1:02:27.960 --> 1:02:30.320
<v Speaker 6>it's a coronavirus like the common cold ones for which

1:02:30.320 --> 1:02:32.840
<v Speaker 6>immunity is not that durable. But on the other hand,

1:02:32.880 --> 1:02:34.840
<v Speaker 6>it causes a much more severe infection in a lot

1:02:34.840 --> 1:02:36.960
<v Speaker 6>of people, so the immune response may be quite different,

1:02:37.240 --> 1:02:39.280
<v Speaker 6>and so maybe it'll be more durable than a typical

1:02:39.400 --> 1:02:43.160
<v Speaker 6>you know, common cold coronavirus. Those all matter because it

1:02:43.200 --> 1:02:46.200
<v Speaker 6>really matters as to like, does this you know, this

1:02:46.280 --> 1:02:50.000
<v Speaker 6>COVID nineteen disappear from Earth once we have a vaccine

1:02:50.160 --> 1:02:52.760
<v Speaker 6>or you know, or does it become a thing that

1:02:53.240 --> 1:02:55.880
<v Speaker 6>if you're vaccinated, you're probably safe, but you need to

1:02:55.880 --> 1:02:58.840
<v Speaker 6>get vaccinated every year or two. How does that play

1:02:58.840 --> 1:03:02.320
<v Speaker 6>out in the future? Exactly? Those parts the interactions of

1:03:02.360 --> 1:03:06.640
<v Speaker 6>immunity transmission is the stuff that like really clouds what

1:03:06.800 --> 1:03:08.920
<v Speaker 6>could happen two, three, four years from now?

1:03:09.400 --> 1:03:13.520
<v Speaker 2>Mm hmm. Yeah. I'd like to ask you when I

1:03:13.560 --> 1:03:16.640
<v Speaker 2>see a headline that says, oh, this model has just

1:03:16.680 --> 1:03:19.280
<v Speaker 2>come out and it predicts this many things, it can

1:03:19.320 --> 1:03:24.480
<v Speaker 2>be really difficult to evaluate whether that model is reliable

1:03:24.720 --> 1:03:27.520
<v Speaker 2>or what I should take away from that model. And

1:03:27.560 --> 1:03:30.120
<v Speaker 2>so do you have any suggestions for how we should

1:03:30.120 --> 1:03:32.640
<v Speaker 2>think about these models and how we should evaluate them

1:03:33.400 --> 1:03:34.720
<v Speaker 2>or compare them.

1:03:36.160 --> 1:03:39.840
<v Speaker 6>Yeah, a couple come to mind. The first one and

1:03:39.840 --> 1:03:42.480
<v Speaker 6>it's one that's frustrating, and it's frustrating to me again

1:03:42.840 --> 1:03:45.920
<v Speaker 6>as a just a person who's afraid of COVID. You know,

1:03:47.240 --> 1:03:52.200
<v Speaker 6>is that be very wary of absolute predictions for the

1:03:52.200 --> 1:03:54.680
<v Speaker 6>many reasons we've discussed about how they're not laws of

1:03:54.720 --> 1:03:57.120
<v Speaker 6>physics or in this situation, they're behavior dependent and we

1:03:57.200 --> 1:04:00.000
<v Speaker 6>get to choose that. And so that would be one

1:04:00.080 --> 1:04:02.200
<v Speaker 6>rule of thumb is if I'm hearing a modeling result

1:04:02.720 --> 1:04:06.240
<v Speaker 6>that says, like, you know, with high confidence, something is

1:04:06.320 --> 1:04:09.600
<v Speaker 6>going to happen, you know, in August, and that's that,

1:04:10.320 --> 1:04:12.840
<v Speaker 6>I am very wary of it. And then immediately ask

1:04:12.920 --> 1:04:16.680
<v Speaker 6>myself under what assumptions about the future is that likely

1:04:16.760 --> 1:04:20.040
<v Speaker 6>to be true? And so that's one rule of thumb.

1:04:20.400 --> 1:04:22.520
<v Speaker 6>Now I'll soften that and say if it says here's

1:04:22.560 --> 1:04:25.040
<v Speaker 6>what's likely to happen in the next two weeks, I

1:04:25.320 --> 1:04:28.400
<v Speaker 6>get much less critical because that's, you know, we don't

1:04:28.400 --> 1:04:31.320
<v Speaker 6>think society changes that fast most of the time, and

1:04:31.360 --> 1:04:33.720
<v Speaker 6>so that's a that's a more reliable thing to predict.

1:04:34.520 --> 1:04:37.680
<v Speaker 6>Maybe another rule of thumb is, you know, again, one

1:04:37.720 --> 1:04:40.480
<v Speaker 6>that might be frustrating and one that I've probably done

1:04:40.520 --> 1:04:41.880
<v Speaker 6>a lot in this interview, at least I hope I've

1:04:41.880 --> 1:04:45.400
<v Speaker 6>done a lot in this interview is does the communication

1:04:45.440 --> 1:04:49.480
<v Speaker 6>around the model keep hedging what it says? And if

1:04:49.480 --> 1:04:52.800
<v Speaker 6>it does, that's a good thing. The hedging verbally is

1:04:52.840 --> 1:04:57.320
<v Speaker 6>a challenge of translating mathematics into into conversation. So when

1:04:57.360 --> 1:04:59.960
<v Speaker 6>we talk about uncertainty in our models, there's a very

1:05:00.160 --> 1:05:03.320
<v Speaker 6>precise way to sort of define uncertainty, and you know,

1:05:03.400 --> 1:05:05.960
<v Speaker 6>we can make a graph that shows, you know, a

1:05:06.040 --> 1:05:09.000
<v Speaker 6>range of estimates and has some principal reason that happens.

1:05:09.360 --> 1:05:12.720
<v Speaker 6>But then when you translate it to the written written media,

1:05:12.760 --> 1:05:17.000
<v Speaker 6>that's not technical or a conversation trying to communicate that

1:05:17.360 --> 1:05:20.680
<v Speaker 6>like here's what I think I know confidently versus here's

1:05:20.800 --> 1:05:24.360
<v Speaker 6>where I'm not so sure. Is something that if people

1:05:24.440 --> 1:05:27.360
<v Speaker 6>can tune their ear to that that will help them

1:05:27.440 --> 1:05:30.040
<v Speaker 6>understand what they can and can't believe about what they're hearing.

1:05:30.480 --> 1:05:34.800
<v Speaker 6>And conversely, if they don't hear that again, they should

1:05:34.840 --> 1:05:37.440
<v Speaker 6>be wary. That's certainly how I think of it as

1:05:37.440 --> 1:05:39.960
<v Speaker 6>a as a member of the community. When I watch

1:05:39.960 --> 1:05:42.120
<v Speaker 6>a model on the news or read a tweet, but

1:05:42.200 --> 1:05:44.600
<v Speaker 6>not the paper, that's sort of how I approach it.

1:05:45.440 --> 1:05:49.400
<v Speaker 2>Mhmm. Yeah, those are great, great tips for sure. So

1:05:49.520 --> 1:05:53.280
<v Speaker 2>I have one final question for you, and it's more

1:05:53.320 --> 1:05:56.680
<v Speaker 2>on a personal note. Is there any positive change you

1:05:56.760 --> 1:05:59.880
<v Speaker 2>hope to see come out of this pandemic, whether it

1:06:00.040 --> 1:06:02.440
<v Speaker 2>relates to just sort of you know, you as a

1:06:02.560 --> 1:06:05.040
<v Speaker 2>member of the community or you as a as a

1:06:05.120 --> 1:06:08.080
<v Speaker 2>modeler in your professional life, anything that you a little

1:06:08.120 --> 1:06:10.080
<v Speaker 2>silver lining maybe hope for the future.

1:06:11.000 --> 1:06:16.480
<v Speaker 6>Oh yeah, absolutely. You know, COVID is revealing something we

1:06:16.560 --> 1:06:20.720
<v Speaker 6>all should know. We are all in it together. Infectious

1:06:20.760 --> 1:06:23.080
<v Speaker 6>disease makes that clear because you're there is no individual

1:06:23.120 --> 1:06:26.960
<v Speaker 6>decision that doesn't have consequences, but we're all in it together.

1:06:27.240 --> 1:06:31.680
<v Speaker 6>And something that's been mostly gratifying. Something I've been really

1:06:32.240 --> 1:06:34.440
<v Speaker 6>you know, continually, like can tear up if I let

1:06:34.440 --> 1:06:38.080
<v Speaker 6>myself think about it, is how much from the end,

1:06:38.120 --> 1:06:41.800
<v Speaker 6>you know, middle of February and forward to now, all

1:06:41.840 --> 1:06:44.960
<v Speaker 6>over the world, people have made dramatic changes to how

1:06:45.080 --> 1:06:51.400
<v Speaker 6>they live inconvenient changes, personally damaging changes in many cases

1:06:52.000 --> 1:06:54.880
<v Speaker 6>because they're trying to say, you know, save themselves, but

1:06:54.920 --> 1:06:58.240
<v Speaker 6>also save the lives of their neighbor or their you know,

1:06:58.320 --> 1:07:02.920
<v Speaker 6>friend's grandmother that they accidentally transmit to. And that, to

1:07:03.000 --> 1:07:08.000
<v Speaker 6>me is is really remarkable. And and also you know,

1:07:08.480 --> 1:07:10.560
<v Speaker 6>moving that to a professional scale one of the things

1:07:10.560 --> 1:07:14.000
<v Speaker 6>that's also been I think, really promising and really I

1:07:14.000 --> 1:07:17.080
<v Speaker 6>mean again just gratifying. Is like, you know, I was

1:07:17.240 --> 1:07:20.800
<v Speaker 6>prior to this three months ago, most of my work

1:07:20.880 --> 1:07:25.720
<v Speaker 6>was on polio transmission with applications largely towards developing world,

1:07:25.720 --> 1:07:28.480
<v Speaker 6>and I didn't have close relationships for the most part

1:07:28.920 --> 1:07:31.600
<v Speaker 6>with public health officials. And you know, I had a

1:07:31.600 --> 1:07:33.840
<v Speaker 6>peer group of different modelers, but we often would talk

1:07:33.880 --> 1:07:36.240
<v Speaker 6>more to each other, and you know, and if I

1:07:36.280 --> 1:07:38.760
<v Speaker 6>had relationships, they were in place as far away that

1:07:38.800 --> 1:07:41.400
<v Speaker 6>I wasn't you know, intellectually was trying to help, but

1:07:41.440 --> 1:07:44.680
<v Speaker 6>wouldn't didn't feel close to And I've watched, you know,

1:07:44.760 --> 1:07:48.880
<v Speaker 6>my the fact that I'm here right now because so

1:07:49.000 --> 1:07:51.600
<v Speaker 6>many people from so many different organizations, with so many

1:07:51.600 --> 1:07:55.280
<v Speaker 6>different backgrounds are like all just bent towards good and

1:07:55.320 --> 1:07:58.720
<v Speaker 6>we're like, let's work together. I don't care how we

1:07:58.800 --> 1:08:01.840
<v Speaker 6>used to do it. Well, there's something of value here,

1:08:02.440 --> 1:08:04.160
<v Speaker 6>let's figure out how to make it work, and that

1:08:04.320 --> 1:08:06.520
<v Speaker 6>like we're in it together and figuring out how to

1:08:06.520 --> 1:08:11.320
<v Speaker 6>make it work has just been awesome. And it makes

1:08:11.400 --> 1:08:13.439
<v Speaker 6>me sad that it takes something like this to really

1:08:13.520 --> 1:08:16.439
<v Speaker 6>make that crystal clear. But boy, I hope we remember

1:08:16.479 --> 1:08:19.360
<v Speaker 6>it when COVID is under control are hopefully gone.

1:08:51.280 --> 1:08:55.320
<v Speaker 2>Thank you again so much to doctor Mike Famulary for

1:08:55.400 --> 1:08:59.599
<v Speaker 2>giving us the lowdown on math models. Yeah, it was great.

1:08:59.600 --> 1:09:02.280
<v Speaker 2>We cover so much ground in that interview too.

1:09:02.240 --> 1:09:06.920
<v Speaker 5>Did another phenomen interview erin loved it. I learned a

1:09:06.920 --> 1:09:10.200
<v Speaker 5>lot getting to listen to it, really truly, I.

1:09:10.280 --> 1:09:12.920
<v Speaker 2>Thank you, thank you. I mean, he did such an

1:09:12.920 --> 1:09:16.120
<v Speaker 2>awesome job. Though I thought of explaining. I mean, this

1:09:16.160 --> 1:09:18.519
<v Speaker 2>is such a complex topic and so to break it

1:09:18.560 --> 1:09:21.320
<v Speaker 2>down in this really accessible way, like that's not an

1:09:21.360 --> 1:09:25.040
<v Speaker 2>easy thing to do. So we appreciate it, we really do.

1:09:25.120 --> 1:09:27.519
<v Speaker 5>I think a lot of people get very scared when

1:09:27.560 --> 1:09:31.000
<v Speaker 5>they hear about math, and I feel like that made

1:09:31.080 --> 1:09:32.320
<v Speaker 5>math not so scary.

1:09:32.960 --> 1:09:36.599
<v Speaker 2>Yeah. Absolutely, Okay, So Aarin, what did we learn?

1:09:36.920 --> 1:09:38.599
<v Speaker 5>We've learned so very much.

1:09:38.840 --> 1:09:41.479
<v Speaker 2>Okay, what are the top five things we learned, then

1:09:41.920 --> 1:09:43.519
<v Speaker 2>tot of five things. Okay.

1:09:44.040 --> 1:09:49.040
<v Speaker 5>Number one, math models of infectious disease can help us

1:09:49.200 --> 1:09:53.280
<v Speaker 5>ask and answer all kinds of questions, and they come

1:09:53.320 --> 1:09:57.000
<v Speaker 5>in all different shapes and sizes, but in general they're

1:09:57.120 --> 1:10:01.200
<v Speaker 5>used for two basic purposes. Number One, models can allow

1:10:01.320 --> 1:10:05.559
<v Speaker 5>us to imagine a multiverse of possible outcomes, and this

1:10:05.560 --> 1:10:08.280
<v Speaker 5>can help us make decisions about which course of action

1:10:08.439 --> 1:10:11.639
<v Speaker 5>to take or which policy to put into place. Aaron,

1:10:11.680 --> 1:10:15.120
<v Speaker 5>I think you said it's like an endgame possibility.

1:10:15.160 --> 1:10:18.120
<v Speaker 2>I'm sorry, Infinity War. It's like when doctor Strange is like,

1:10:18.800 --> 1:10:21.200
<v Speaker 2>what are all the possibilities? Let me just go through

1:10:21.240 --> 1:10:23.000
<v Speaker 2>the six billion of them.

1:10:24.160 --> 1:10:27.240
<v Speaker 5>Oh wow, I just got called out hard for saying

1:10:27.240 --> 1:10:27.960
<v Speaker 5>the wrong movie.

1:10:28.080 --> 1:10:30.519
<v Speaker 2>Sorry, get your Marvel movies right.

1:10:30.520 --> 1:10:36.680
<v Speaker 5>Erin Ooh okay. This second thing that models can do

1:10:36.880 --> 1:10:40.760
<v Speaker 5>is help us to understand what happened retrospectively, which is

1:10:40.840 --> 1:10:45.040
<v Speaker 5>really useful since some things we can't measure directly. And

1:10:45.160 --> 1:10:49.040
<v Speaker 5>there's also this inherent trade off between making models more

1:10:49.120 --> 1:10:54.960
<v Speaker 5>complex or keeping them very simple. Complex models allow us

1:10:55.000 --> 1:10:59.640
<v Speaker 5>to ask complex questions, but you often will sacrifice accuracy

1:10:59.800 --> 1:11:02.160
<v Speaker 5>for that because of all of the assumptions that you

1:11:02.240 --> 1:11:04.920
<v Speaker 5>have to make in those models. You end up using

1:11:04.960 --> 1:11:08.360
<v Speaker 5>these more complex models to make decisions about which option

1:11:08.520 --> 1:11:12.519
<v Speaker 5>is better, whereas simpler models might be used to actually

1:11:12.560 --> 1:11:15.679
<v Speaker 5>forecast what might happen, at least in the short term.

1:11:16.439 --> 1:11:23.360
<v Speaker 2>Yes, definitely, very cool. Yeah. Number two, the modeling that

1:11:23.400 --> 1:11:27.120
<v Speaker 2>most of us are probably familiar with is weather forecasting.

1:11:27.600 --> 1:11:29.240
<v Speaker 5>This blew my mind truly.

1:11:30.920 --> 1:11:33.720
<v Speaker 2>I mean, I think it's a really good it's a

1:11:33.760 --> 1:11:35.160
<v Speaker 2>really good way to put it. It's a really good

1:11:35.200 --> 1:11:38.800
<v Speaker 2>way to think about it. These comparisons, Yeah, I mean.

1:11:38.840 --> 1:11:41.960
<v Speaker 2>And so in weather forecasting, of course, you get these

1:11:42.000 --> 1:11:46.080
<v Speaker 2>predictions for what's going to happen later today or tomorrow,

1:11:46.280 --> 1:11:48.120
<v Speaker 2>or this week or next week.

1:11:48.680 --> 1:11:49.839
<v Speaker 4>But there are.

1:11:49.680 --> 1:11:53.080
<v Speaker 2>Several big differences between modeling the weather and modeling in

1:11:53.160 --> 1:11:56.800
<v Speaker 2>epidemic or a pandemic. The first is that we have

1:11:56.960 --> 1:12:00.400
<v Speaker 2>a wealth of incredibly detailed and long term day on

1:12:00.479 --> 1:12:03.960
<v Speaker 2>weather patterns, whereas is something like COVID nineteen. We're still

1:12:04.120 --> 1:12:08.559
<v Speaker 2>very much learning as we go. Another huge difference is that,

1:12:08.720 --> 1:12:12.879
<v Speaker 2>unlike weather prediction, these models of infectious disease can actually

1:12:13.080 --> 1:12:17.280
<v Speaker 2>change what happens in the future. So we really shouldn't

1:12:17.280 --> 1:12:21.160
<v Speaker 2>think of infectious disease modeling as making predictions, but it's

1:12:21.760 --> 1:12:25.519
<v Speaker 2>more about imagining a bunch of different scenarios that could

1:12:25.560 --> 1:12:28.640
<v Speaker 2>happen depending on the choices we make now. And I

1:12:28.640 --> 1:12:31.880
<v Speaker 2>think this is particularly important to remember as we revisit

1:12:31.920 --> 1:12:35.080
<v Speaker 2>some of the earlier models of COVID nineteen, under what

1:12:35.280 --> 1:12:39.200
<v Speaker 2>circumstances were they predicting this or that amount of deaths.

1:12:39.800 --> 1:12:42.920
<v Speaker 2>Many of those models may have been estimating the intensity

1:12:42.960 --> 1:12:45.479
<v Speaker 2>of the pandemic if we did nothing to control it.

1:12:45.960 --> 1:12:48.720
<v Speaker 2>So the fact that the case numbers or deaths are

1:12:48.800 --> 1:12:52.920
<v Speaker 2>below right now what was predicted in those scenarios does

1:12:52.960 --> 1:12:57.599
<v Speaker 2>not mean that the physical distancing or the shutdowns that

1:12:57.640 --> 1:12:59.720
<v Speaker 2>these measures that we've taken. It doesn't mean that they

1:12:59.760 --> 1:13:03.880
<v Speaker 2>are to extreme, but rather it's more that their evidence

1:13:04.120 --> 1:13:07.200
<v Speaker 2>that they are working to actually slow the pandemic and

1:13:07.360 --> 1:13:11.200
<v Speaker 2>prevent those worst case scenarios from happening. Right Yeah.

1:13:11.240 --> 1:13:13.479
<v Speaker 5>I feel like that's such an important point because it's

1:13:13.479 --> 1:13:15.720
<v Speaker 5>really easy to look at it and say, oh, well,

1:13:16.160 --> 1:13:20.080
<v Speaker 5>what's happening now doesn't match those models, But that's not

1:13:20.120 --> 1:13:25.080
<v Speaker 5>really the point of those models. Number Three. As we've

1:13:25.120 --> 1:13:28.679
<v Speaker 5>talked about before on this podcast, epidemics tend to follow

1:13:28.720 --> 1:13:31.759
<v Speaker 5>a curve where we have a steep increase in cases,

1:13:32.439 --> 1:13:37.080
<v Speaker 5>a peak followed by a sharp decline. Often that decline

1:13:37.120 --> 1:13:40.320
<v Speaker 5>in cases happens because you run out of susceptible people

1:13:40.439 --> 1:13:45.519
<v Speaker 5>to infect. However, with COVID nineteen, we still have an

1:13:45.840 --> 1:13:49.639
<v Speaker 5>enormous amount of susceptible people that we need to protect

1:13:49.720 --> 1:13:53.519
<v Speaker 5>from infection, so we can't necessarily expect to see that

1:13:53.720 --> 1:13:58.000
<v Speaker 5>sharp decline. Our collective behavior will be the thing that

1:13:58.080 --> 1:14:01.400
<v Speaker 5>determines the shape of the curve, not just the transmission

1:14:01.479 --> 1:14:06.480
<v Speaker 5>dynamics of the virus. By practicing physical distancing, we're manipulating

1:14:06.600 --> 1:14:10.280
<v Speaker 5>that are not remember and we're driving it down as

1:14:10.400 --> 1:14:14.080
<v Speaker 5>much as we possibly can. If we lift these measures,

1:14:14.720 --> 1:14:17.920
<v Speaker 5>the effective are not could climb back up, and we

1:14:17.960 --> 1:14:20.479
<v Speaker 5>could end up creating an epidemic curve that looks more

1:14:20.600 --> 1:14:23.760
<v Speaker 5>like a camel with multiple humps.

1:14:24.640 --> 1:14:29.160
<v Speaker 2>We don't want a camel curve. No offense to camel,

1:14:29.400 --> 1:14:34.679
<v Speaker 2>No offense to camels. Hands are very cool. Yeah, number four.

1:14:35.400 --> 1:14:39.120
<v Speaker 2>It seems that physical distancing might have to continue for

1:14:39.160 --> 1:14:42.759
<v Speaker 2>a very long time in order to keep that effective

1:14:42.760 --> 1:14:46.800
<v Speaker 2>reproductive rate very low. But we're still learning so much

1:14:46.800 --> 1:14:50.040
<v Speaker 2>about COVID nineteen that could change the exact nature of

1:14:50.120 --> 1:14:53.600
<v Speaker 2>these physical distancing measures, and one of the areas that

1:14:53.680 --> 1:14:56.960
<v Speaker 2>modelers are looking at is teasing apart which measures seem

1:14:57.040 --> 1:14:59.680
<v Speaker 2>to be most effective and which may not be that

1:14:59.720 --> 1:15:03.439
<v Speaker 2>af FEC and exactly what kinds of resources we would

1:15:03.479 --> 1:15:06.240
<v Speaker 2>need to control the spread of infection once a case

1:15:06.320 --> 1:15:09.840
<v Speaker 2>is detected, so sort of like a ramped up test

1:15:10.080 --> 1:15:15.000
<v Speaker 2>trace isolate quarantine strategy. And based on what we learn,

1:15:15.320 --> 1:15:19.200
<v Speaker 2>there might be adjustments to the current everybody physical distance

1:15:19.479 --> 1:15:23.760
<v Speaker 2>strategy to only having certain people or certain places do

1:15:23.880 --> 1:15:27.960
<v Speaker 2>physical distancing. But because of what we've learned so far

1:15:28.200 --> 1:15:32.519
<v Speaker 2>about asymptomatic and pre symptomatic individuals and their ability to

1:15:32.560 --> 1:15:37.240
<v Speaker 2>transmit the virus, contact tracing alone is probably not going

1:15:37.280 --> 1:15:40.840
<v Speaker 2>to be enough. So some physical distancing seems like it's

1:15:40.880 --> 1:15:43.000
<v Speaker 2>going to remain for at least a good amount of

1:15:43.320 --> 1:15:44.200
<v Speaker 2>time in the future.

1:15:45.040 --> 1:15:48.960
<v Speaker 5>Yeah, Like we're in this for the long haul, it seems.

1:15:48.960 --> 1:15:51.000
<v Speaker 2>Yeah, or at least a long haul. Who knows what

1:15:51.040 --> 1:15:54.320
<v Speaker 2>the long haul? Yeah.

1:15:54.400 --> 1:15:59.160
<v Speaker 5>Number five. In general, if you are looking and thinking

1:15:59.280 --> 1:16:03.400
<v Speaker 5>about whether to trust a model or not, there are

1:16:03.479 --> 1:16:07.719
<v Speaker 5>a couple of rules of them. Number One, be wary

1:16:07.880 --> 1:16:12.439
<v Speaker 5>of absolute predictions, especially if they are long term ones.

1:16:13.080 --> 1:16:16.080
<v Speaker 5>If someone says they're almost certainly going to be x

1:16:16.160 --> 1:16:20.439
<v Speaker 5>number of cases in September. Maybe take that prediction with a.

1:16:20.360 --> 1:16:23.240
<v Speaker 2>Grain of salt, also because apparently they're from the nineteen

1:16:23.280 --> 1:16:26.479
<v Speaker 2>twenties exactly, And who trusts that kind of a voice,

1:16:26.520 --> 1:16:26.720
<v Speaker 2>you know?

1:16:28.280 --> 1:16:31.840
<v Speaker 5>Number two, listen to how the model is described and

1:16:31.920 --> 1:16:36.760
<v Speaker 5>whether uncertainty is acknowledged. If a person describes or acknowledges

1:16:36.800 --> 1:16:39.240
<v Speaker 5>the uncertainty in the model, that's actually a good thing.

1:16:39.800 --> 1:16:43.280
<v Speaker 5>If someone says, well, this is one possible outcome based

1:16:43.320 --> 1:16:46.040
<v Speaker 5>on XYZ, but we don't know how much of a

1:16:46.120 --> 1:16:51.240
<v Speaker 5>role ABC plays. That's good. Knowing and discussing the limits

1:16:51.240 --> 1:16:53.920
<v Speaker 5>of a model is a matter of scientific integrity, and

1:16:53.960 --> 1:16:56.920
<v Speaker 5>we should be wary of someone overstating what their model

1:16:56.920 --> 1:16:59.520
<v Speaker 5>can do. I think that's good general practice.

1:17:00.160 --> 1:17:02.120
<v Speaker 2>Gonna say, yeah, it's a pretty good like life rule.

1:17:02.240 --> 1:17:05.160
<v Speaker 2>Someone says I'm an expert, I know everything. Don't question

1:17:05.800 --> 1:17:07.720
<v Speaker 2>my knowledge or authority.

1:17:07.320 --> 1:17:10.720
<v Speaker 5>Like, well, see, yeah, I know everything there is to

1:17:10.760 --> 1:17:11.559
<v Speaker 5>know about everything.

1:17:11.640 --> 1:17:13.120
<v Speaker 2>You see, that's the kind of voice.

1:17:12.880 --> 1:17:13.360
<v Speaker 5>You know what I mean?

1:17:14.600 --> 1:17:25.880
<v Speaker 2>Yep, Yeah, it's okay, Well, yeah, I mean those are

1:17:25.880 --> 1:17:29.519
<v Speaker 2>the top five things, but there's definitely a lot more

1:17:30.240 --> 1:17:32.840
<v Speaker 2>that you could pick out of that interview incredible.

1:17:33.160 --> 1:17:35.679
<v Speaker 5>Hopefully you learned that math is kind of fun because

1:17:35.760 --> 1:17:37.200
<v Speaker 5>I think it. I think it's fun.

1:17:37.760 --> 1:17:41.760
<v Speaker 2>It is so powerful what you can do, it's amazing. Yeah,

1:17:41.760 --> 1:17:45.320
<v Speaker 2>I love it. Yeah. And if you want to learn

1:17:45.439 --> 1:17:48.000
<v Speaker 2>more about math, or maybe get a little bit deeper

1:17:48.040 --> 1:17:51.920
<v Speaker 2>of a dive into infectious disease and how it's modeled

1:17:51.920 --> 1:17:57.679
<v Speaker 2>in by math, I watched an amazing lecture by Robin

1:17:57.720 --> 1:18:02.120
<v Speaker 2>Thompson at Oxford Mathematics and this is on YouTube. It's

1:18:02.160 --> 1:18:06.479
<v Speaker 2>titled how do Mathematicians Model Infectious Disease Outbreaks? And he

1:18:06.560 --> 1:18:09.640
<v Speaker 2>did such a great job of again sort of like

1:18:09.720 --> 1:18:12.600
<v Speaker 2>taking you through you know what a model is, you

1:18:12.640 --> 1:18:15.720
<v Speaker 2>know all of these different aspects. And there's also a

1:18:15.800 --> 1:18:19.240
<v Speaker 2>visual component which really might help you to see some

1:18:19.400 --> 1:18:23.280
<v Speaker 2>of these different numbers and figures that we talked about,

1:18:23.320 --> 1:18:25.640
<v Speaker 2>like you know, on your actual computer screen. So we

1:18:25.680 --> 1:18:29.000
<v Speaker 2>will post a link to that on our website. And

1:18:29.439 --> 1:18:33.120
<v Speaker 2>if there are any modeling books like for the lay

1:18:33.160 --> 1:18:36.439
<v Speaker 2>person that anyone wants to suggest or send our way,

1:18:36.520 --> 1:18:39.880
<v Speaker 2>please please do so we will share them. Most definitely,

1:18:40.800 --> 1:18:43.480
<v Speaker 2>another thing that I wanted to call out not necessarily

1:18:43.520 --> 1:18:45.680
<v Speaker 2>a resource, but just a fun little thing that I

1:18:45.760 --> 1:18:50.839
<v Speaker 2>found is a book that we got an advanced reader's

1:18:50.960 --> 1:18:56.320
<v Speaker 2>copy of called The Down Days by Ilsa Hugo, and

1:18:56.439 --> 1:18:59.960
<v Speaker 2>I really liked it. So it's a it's a fiction book.

1:19:00.560 --> 1:19:04.680
<v Speaker 2>And the timing of this could not be like spookier

1:19:05.120 --> 1:19:08.439
<v Speaker 2>because it first of all, it do you remember the

1:19:09.160 --> 1:19:12.240
<v Speaker 2>Lake Tanganika laughter epidemic that we talked about in The

1:19:12.320 --> 1:19:13.000
<v Speaker 2>Dancing Plague?

1:19:13.280 --> 1:19:14.800
<v Speaker 5>I remember you talking about it.

1:19:14.880 --> 1:19:20.200
<v Speaker 2>Okay, Well, it's sort of like a fictionalized account of that,

1:19:20.400 --> 1:19:24.280
<v Speaker 2>but in like the future or like current times. And

1:19:24.960 --> 1:19:27.120
<v Speaker 2>but it's like goes on for a long time. Everyone's

1:19:27.160 --> 1:19:30.960
<v Speaker 2>wearing masks all over the place. Uh huh, everyone wears

1:19:31.000 --> 1:19:34.800
<v Speaker 2>gloves everywhere. There's like full on quarantine all the time.

1:19:35.360 --> 1:19:38.280
<v Speaker 2>And one of the wild things too that happened was

1:19:38.360 --> 1:19:42.400
<v Speaker 2>that like people were like drinking bleach because someone told

1:19:42.479 --> 1:19:45.960
<v Speaker 2>them it was going to clean their insides. Yeah.

1:19:46.560 --> 1:19:49.120
<v Speaker 5>I think when fiction is so close to real life

1:19:49.160 --> 1:19:50.679
<v Speaker 5>that you're like, why.

1:19:51.479 --> 1:19:54.880
<v Speaker 2>It's it's spooky. But I really enjoyed the book. And

1:19:55.160 --> 1:19:58.360
<v Speaker 2>it comes out like in early May or early June,

1:19:58.400 --> 1:20:00.160
<v Speaker 2>I can't remember, But we're gonna put it on our

1:20:00.160 --> 1:20:04.040
<v Speaker 2>bookshop and our good Reads list. But yeah, if you

1:20:04.160 --> 1:20:07.599
<v Speaker 2>want to kind of like even dive deeper into the

1:20:07.640 --> 1:20:11.280
<v Speaker 2>world of like fictional into the world of pandemics. Here's

1:20:11.320 --> 1:20:15.719
<v Speaker 2>a fictional one you can try out. And then one

1:20:16.040 --> 1:20:19.719
<v Speaker 2>final thing that I want to shout out is that

1:20:20.120 --> 1:20:25.720
<v Speaker 2>our lovely lovely herd the herd on Reddit started a

1:20:25.800 --> 1:20:29.240
<v Speaker 2>silver linings thread. So if you want to add your

1:20:29.240 --> 1:20:33.120
<v Speaker 2>silver lining, go on reddit and check out the subreddit

1:20:33.160 --> 1:20:37.280
<v Speaker 2>tpwky and add your silver lining. It's really wonderful and

1:20:37.320 --> 1:20:39.679
<v Speaker 2>it really like, it makes my heart happy to see

1:20:39.680 --> 1:20:40.680
<v Speaker 2>all those.

1:20:41.160 --> 1:20:43.679
<v Speaker 5>If you need just like a little a little mood booster,

1:20:43.760 --> 1:20:45.759
<v Speaker 5>you could just go on and read everyone else's silver

1:20:45.840 --> 1:20:46.759
<v Speaker 5>linings because.

1:20:46.520 --> 1:20:48.680
<v Speaker 2>It's very happy. It's excellent.

1:20:49.640 --> 1:20:53.519
<v Speaker 5>Yeah. Well that was a really fun episode. Thank you

1:20:53.600 --> 1:20:56.720
<v Speaker 5>again so much, doctor Famulari for spending the time to

1:20:56.800 --> 1:20:58.919
<v Speaker 5>chat with us and all of our listeners.

1:20:58.920 --> 1:21:00.400
<v Speaker 4>We really really appreciate it.

1:21:00.479 --> 1:21:04.040
<v Speaker 2>Yes we do. And thank you to Bloodmobile for providing

1:21:04.040 --> 1:21:06.760
<v Speaker 2>the music for this episode and all of our episodes.

1:21:07.200 --> 1:21:10.960
<v Speaker 5>And thank you to you listeners for listening and sticking along.

1:21:11.000 --> 1:21:13.599
<v Speaker 5>We hope that you enjoyed this math heavy episode.

1:21:14.520 --> 1:21:20.799
<v Speaker 2>Yeah, let us know. Yeah, and also yes, thank you. Okay,

1:21:21.320 --> 1:21:23.599
<v Speaker 2>until next time, wash your hands.

1:21:23.840 --> 1:21:24.960
<v Speaker 5>You filthy animals,