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

TL;DR

This paper presents a risk stratified SIR model that explicitly accounts for local reopening policies, improving forecasting of COVID-19 hospitalizations and deaths, and enabling counterfactual policy analysis at a local level.

Contribution

It introduces a novel model that incorporates local contact behaviors and policy effects, providing more accurate forecasts and policy impact simulations for COVID-19.

Findings

Model outperformed competing models in forecasting accuracy.

Reopening policies significantly affected local mortality and hospitalization rates.

The approach enables explicit and explainable analysis of policy impacts.

Abstract

Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materials and Methods: We collected datasets of daily reported hospitalization and cumulative morality of COVID 19 in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council COVID 19 report, TMC daily news, and New York Times county level mortality reporting). Our model, risk stratified SIR HCD uses separate variables to model the dynamics of local contact (e.g., work from home) and high contact (e.g., work on site) subpopulations while sharing parameters to control their respective $R_0(t)$ over time. Results: We evaluated our models forecasting performance in Harris County, TX (the most populated county in the Greater Houston area) during the Phase I and Phase II reopening. Not only did our model outperform other competing models, it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. Discussion: Local mortality and hospitalization are significantly impacted by quarantine and reopening policies. No existing model has directly accounted for the effect of these policies on local trends in infections, hospitalizations, and deaths in an explicit and explainable manner. Our work is an attempt to close this important technical gap to support decision making. Conclusion: Despite several limitations, we think it is a timely effort to rethink about how to best model the dynamics of pandemics under the influence of reopening policies.