Estimating intervention effects on infectious disease control: the effect of community mobility reduction on Coronavirus spread

TL;DR

This paper introduces a spatiotemporal causal model to evaluate how reductions in community mobility impact COVID-19 infection rates, accounting for spatial interference and providing timely insights for intervention effectiveness.

Contribution

It develops a novel causal inference framework based on an approximation of the SIR model that captures direct and spillover effects of mobility reductions on infection rates.

Findings

Mobility reductions lead to 4-7 weeks decrease in COVID-19 cases.

The model estimates both direct and spillover effects of interventions.

Reductions in community mobility significantly impact infection rates.

Abstract

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.