Start from the End: A Framework for Computational Policy Exploration to Inform Effective and Geospatially Consistent Interventions applied to COVID-19 in St. Louis

TL;DR

This paper introduces a framework using emulator models for efficient exploration of intervention strategies in infectious disease models, demonstrated through COVID-19 in St. Louis, enabling better policy decision-making.

Contribution

The authors develop a parameter exploration framework with emulator models to efficiently analyze high-dimensional intervention scenarios in infectious disease modeling.

Findings

Identified numerous effective response strategies for COVID-19 in St. Louis.

Found interventions that reduce geospatial variation in disease spread.

Demonstrated the framework's ability to handle complex, high-dimensional models.

Abstract

Mathematical models are a powerful tool to study infectious disease dynamics and intervention strategies against them in social systems. However, due to their detailed implementation and steep computational requirements, practitioners and stakeholders are typically only able to explore a small subset of all possible intervention scenarios, a severe limitation when preparing for disease outbreaks. In this work, we propose a parameter exploration framework utilizing emulator models to make uncertainty-aware predictions of high-dimensional parameter spaces and identify large numbers of feasible response strategies. We apply our framework to a case study of a large-scale agent-based disease model of the COVID-19 ``Omicron wave'' in St. Louis, Missouri that took place from December 2021 to February 2022. We identify large numbers of response strategies that would have been estimated to have reduced disease spread by a substantial amount. We also identify policy interventions that would have been able to reduce the geospatial variation in disease spread, which has additional implications for designing thoughtful response strategies.