Masks and COVID-19: a causal framework for imputing value to public-health interventions

TL;DR

This paper introduces a causal, data-driven framework to evaluate the effects of public health interventions during COVID-19, using counterfactual simulations to quantify their impact on infection rates.

Contribution

It presents a novel retrospective causal analysis method for assessing intervention effects, applicable to sparse cause-effect scenarios in epidemiology.

Findings

Using masks could prevent approximately 170,000 cases in three US states.

The framework accurately estimates intervention impacts from observational data.

Counterfactual simulations provide a new way to quantify public health intervention effects.

Abstract

During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the Rt parameter. Then, we postulate each jump in Rt as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing Rt to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 170000 (95% CI 160000 to 180000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.