Health equity calibration

[cmhoove14]

Calibration underway for general transmission dynamics, but also want to calibrate to health disparities by race or at least geography.

Goal is to approximately match case and/or hospitalization rate(s) by racial/geographic group

Parameters to tweak could be race-based testing probabilities Analysis/02-Prep-Par-Inputs ; input_pars$test_pars$race_test_mults. Nothing else in model is explicitly tied to race, so might have to try deeper tweaks too

[cmhoove14]

First step is just to write scripts to assess outcomes produced by model simulations. data/outputs/Test_Run_bta... contains an .rds file with the simulation outputs:

• epi_curve contains number of agents in each step at each time step
• infections contains entries for every infection event
• lineliest_tests contains all tests conducted

So epi curve can be used for general calibration (e.g. to hospitalizations), but finer calibration by race and/or by geographies best done using infections and linelist_tests. Some ideas on how to calibrate/what we're looking for:

• High rates of positivity, but generally less tests conducted in census tracts (variable ct) with low healthy places index/high minority race/low income
• More infections in households with essential workers

[sblumberg]

Can we avoid race-based testing probabilities? I think it would be a stronger analysis to identify if/when racial disparities arise organically via calibration to known data, rather than hard code racial inequity into simulation.

[cmhoove14]

@sblumberg Good point, and I agree. income already affects testing probability and now that synthetic population includes the healthy places index, have a few other variables that could be used (believe there's a healthcare access score, for instance)