Zach Sullivan Interview (11/3/20) Notes

Works in Centralized Data Management, Burlington Hospital, Vt.

Video can be accessed at: https://drive.google.com/drive/folders/10W5q8s-M1NWm4ME97knXCUozPLwgGO1t?usp=sharing

Notes from the Interview:
  • COVID looked like a normal distribution at first
  • Model was updated based on incoming data from Italy
  • Consider what indicators you will actually use [when it comes to models]
    • Used SIR from SG2
  • Initially interested in ‘big hospital’ resources -beds, ventilators, etc…
  • Models differ when looking at hospitalized patients vs. testing vs. deaths.
  • Vermont had a large spike in deaths due to COVID in nursing homes
  • IHME model (https://covid19.healthdata.org) was inaccurate for Vermont because it looked at deaths
    • Found that hospitalizations were more accurate
  • Average length of stay:
    • Non-ICU: <8 days
    • ICU: ~10 ICU+6 extra
  • As testing capacity increases, they’re becoming more reliant on it when updating their models
  • The hospital saw a base r_0 of 1.5-1.6 (lower than in urban areas)
  • Use longer term ensemble models (3-4 models to see which is most accurate)
    • Single models need to be care of timeframe (even no more than 2 weeks into the future
Supply Chains and Modeling:
  • Lead times on supplies will also determine how long your model should go
  • Determine maximum burn rate
    • Take into consideration political events– stay-at home orders, outbreaks at colleges, etc…
Modeling and Government Action:
  • Adjust r_0 for government action
  • Herd immunity v.s. Government Action –Depends on Rep. or Dem administration
  • SNS stockpile depends on Presidency/Senate for funding/action
  • Would companies ramp up production b/c of the promise of SNS contracts?
  • Or will they do so anyways when demand increases over the winter?

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