Today we heard from experts (morning) and practitioners (afternoon). The experts were Codilia McGehee (MD/PhD, U Minnesota), Jack O’Brian (biomath, stat, Bowdoin College), Pauline van den Driessche (math epidemiology, U Victoria), Jianhong Wu (math epidemiology, York U). The practitioners were Andrew Roberts and Nick Ma (health care IT, Cerner).
What are the important questions?
We heard many suggestions. Here are a few (in no particular order).
- Be aware of the different ways clinical trial data are reported.
- How can we capture resilience, for example of an infrastructure, in a mathematical model, and what type of data do we need to assess resilience?
- Can we model the influence of human activities (e.g., land use changes) on the spread of zoonotic diseases?
- There seems to be a one-week cycle in daily infection rates; is there a possible correlation with weekend sociability (stochastic forcing)?
- Develop granular (meta)population models for specific communities: long-term care facilities, homeless people, prison population, etc.
- Introduce spatial variation, group differences, and delays in ODE models.
- Design and evaluate strategies for de-escalation: social distancing, re-opening the economy, stratified lockdown, school reopening.
- What have we learned? Epidemics will continue to occur. Develop strategies for avoiding future epidemics.
- Design data collection schemes for global monitoring of infectious diseases (citizen science?). Use livestock for pilot project.
- Study the effect of multiple simultaneous epidemics, co-infection models.
- Investigate scenarios for the timing of recovery transitions for long-term care facilities, separate from general population.
- Different diseases may have shared symptoms. How do we distinguish?
Covid forecasting with Cerner Intelligence
- Curve fitting, informed by SIR models, limited success.
- IHME model, granularity down to state level.
- SIR, SEIR models, parameter estimation, role of \beta, uncertainty.
- HospitalIQ, based on curve fitting.
- Penn Medical CHIME, SIR model, individual hospital data.
- Follow medical rule: stabilize, followed by triage, followed by treatment.
- Economics: uncertainty of new Medicaid enrollments next year.
- Reopening Risk Index (low, medium, high, very high), determined from daily case count at local level.
- Upscaling from death rate, downscaling from daily case rate.
Try another simulator from the shared spreadsheet.