Synthetic Control, Synthetic Interventions, and COVID-19 spread: Exploring the impact of lockdown measures and herd immunity

TL;DR

This study uses synthetic control methods to analyze COVID-19 spread, demonstrating that earlier lockdowns and delayed reopenings could have significantly reduced cases and deaths, with lockdowns having a strong impact regardless of herd immunity levels.

Contribution

The paper applies synthetic control and its extensions to COVID-19 data, providing causal insights into the effects of lockdown timing and herd immunity on disease spread.

Findings

Earlier lockdowns could have reduced cases and deaths.

Lockdowns have a strong impact regardless of herd immunity.

Reopening indoor venues earlier increased spread.

Abstract

The synthetic control method is an empirical methodology forcausal inference using observational data. By observing thespread of COVID-19 throughout the world, we analyze the dataon the number of deaths and cases in different regions usingthe power of prediction, counterfactual analysis, and syntheticinterventions of the synthetic control and its extensions. Weobserve that the number of deaths and cases in different re-gions would have been much smaller had the lockdowns beenimposed earlier and had the re-openings been done later, es-pecially among indoor bars and restaurants. We also analyzethe speculated impact of herd immunity on the spread giventhe population of each region and show that lockdown policieshave a very strong impact on the spread regardless of the levelof prior infections. Our most up-to-date code, model, and data can be foundon github: https://github.com/niloofarbayat/COVID19-synthetic-control-analysis