Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in Iowa

TL;DR

This paper introduces a fast, simulation-free estimation method for individual-based SEIR models to evaluate nonpharmaceutical interventions during COVID-19, enabling quick policy impact assessments on contact networks.

Contribution

The paper presents a novel recursive estimation algorithm for IBMs that significantly reduces computational time, facilitating rapid evaluation of interventions without extensive simulations.

Findings

Efficient estimation of epidemiological outcomes on large contact networks.

Application to Iowa shows potential effects of relaxing social distancing.

Method available as R code for broader use.

Abstract

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents an estimation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of relaxing current social distancing measures in Iowa, USA, at various times and to various degrees. \verb!R! code for our method is provided in the supplementary material, thereby allowing others to utilize our approach for other regions.