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Objective: We study the influence of local reopening policies on thecomposition of the infectious population and their impact on futurehospitalization and mortality rates. Materials and Methods: We collecteddatasets of daily reported hospitalization and cumulative morality of COVID 19in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets arefrom multiple sources (USA FACTS, Southeast Texas Regional Advisory CouncilCOVID 19 report, TMC daily news, and New York Times county level mortalityreporting). Our model, risk stratified SIR HCD uses separate variables to modelthe dynamics of local contact (e.g., work from home) and high contact (e.g.,work on site) subpopulations while sharing parameters to control theirrespective $R_0(t)$ over time. Results: We evaluated our models forecastingperformance in Harris County, TX (the most populated county in the GreaterHouston area) during the Phase I and Phase II reopening. Not only did our modeloutperform other competing models, it also supports counterfactual analysis tosimulate the impact of future policies in a local setting, which is uniqueamong existing approaches. Discussion: Local mortality and hospitalization aresignificantly impacted by quarantine and reopening policies. No existing modelhas directly accounted for the effect of these policies on local trends ininfections, hospitalizations, and deaths in an explicit and explainable manner.Our work is an attempt to close this important technical gap to supportdecision making. Conclusion: Despite several limitations, we think it is atimely effort to rethink about how to best model the dynamics of pandemicsunder the influence of reopening policies.

Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates