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Here are the papers for next week. The first one is the IHME model (pdf attached, webpage of current predictions here … https://www.medrxiv.org/content/10.1101/2020.03.27.20043752v1
The second is the Imperial model … which is a lot more complex. https://spiral.imperial.ac.uk/handle/10044/1/77482
“The best thing for disturbances of the spirit is to learn. That is the only thing that never fails. You may grow old and trembling in your anatomies, you may lie awake at night listening to the disorder of your veins, you may miss your only love and lose your moneys to a monster, you may see the world about you devastated by evil lunatics, or know your honor trampled in the sewers of baser minds. There is only one thing for it then--to learn. Learn why the world wags and what wags it. That is the only thing which the poor mind can never exhaust, never alienate, never be tortured by, never fear or distrust, and never dream of regretting.” ― T.H. White, The Sword in the Stone
The idea is to take the idea that modeling is not specific to a particular domain, and apply it by looking (and implementing) some of the epidemiology models out there.
Our introductory class will begin by discussing the strengths and weaknesses of the major models out there in terms of:
1. data, 2. assumptions, 3. methods
We will share data repositories. Some are listed on the separate page Datasets.
Obvious assumptions are: a. Lack of reliability in numbers: deaths, confirmed cases, recovered cases b. The effect of social measures like distancing c. The effect of epidemiological factors (e.g. density) on spread d. The effect of epidemiological factors (e.g. age of the population) on morbidity rates e. The effect of hospital capacity on morbidity What else?
I think this comment (from one of my many computational uncles) is a good starting point for discussion. The notion of "model" has changed in a far from subtle way within my field. In the past, model meant a theory of something that might then be expressed mathematically for the purposes of statistical estimation. Now, it means equations to estimate with the implicit presumption that some vaguely expressed theory underpins it. If you ask an economics student today, what is your model, the answer will be an equation, not a theory. Does this matter? Yes, because if we want causation about which we can do something we have to know how things happen and what impact interventions have. The complexity of statistical estimation can often blind to the simple, well-known observation that correlation is not causation, and dependent and independent variables are statistical not theoretical terms.
How do you model a whole curve from the rising part of an exponential.
All are welcome - even people who aren't sensory scientists! To join the class 12-12.50 on Thursdays you will need to contact [email protected] for the zoom password (UW people have been sent it already). The other details for the zoom are below. The class will be recorded, and details of where they are stored will be posted shortly.
Ione Fine is inviting you to a scheduled Zoom meeting.
Topic: PSYCH 555 B Sp 20: VJC & ANTG Time: Apr 2, 2020 12:00 PM Pacific Time (US and Canada) Every week on Thu, until Jun 4, 2020, 10 occurrence(s) Apr 2, 2020 12:00 PM Apr 9, 2020 12:00 PM Apr 16, 2020 12:00 PM Apr 23, 2020 12:00 PM Apr 30, 2020 12:00 PM May 7, 2020 12:00 PM May 14, 2020 12:00 PM May 21, 2020 12:00 PM May 28, 2020 12:00 PM Jun 4, 2020 12:00 PM Please download and import the following iCalendar (.ics) files to your calendar system. Weekly: https://washington.zoom.us/meeting/vJYud-GprDItEeL--cyEN6QquHchruKifg/ics?icsToken=98tyKuiqrTsiG9act1_9a7MtW6v9bN_IllRLhu4QnwbALXZicxbeNepQJ5l2Ft-B
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