The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic

Solomon Hsiang; Daniel Allen; Sebastien Annan-Phan; Kendon Bell; Ian Bolliger; Trinetta Chong; Hannah Druckenmiller; Andrew Hultgren; Luna Yue Huang; Emma Krasovich; Peiley Lau; Jaecheol Lee; Esther Rolf; Jeanette Tseng; Tiffany Wu

To construct the dependent variable, we transform location-specific, sub-national time-series of infections into first-differences of their natural logarithm, which is the per day growth rate of infections (see Methods). We use data from first-or second-level administrative units and data on active or cumulative cases, depending on availability (see Appendix Section 2). We then employ widely-used panel regression models 23, 24 to estimate how the daily growth rate of infections changes over time within a location when different combinations of large-scale social policies are enacted (see Methods). Our econometric approach accounts for differences in the baseline growth rate of infections across locations due to differences in demographics, socio-economic status, culture, or health systems across localities within a country; it accounts for systemic patterns in growth rates within countries unrelated to policy, such as the effect of the work-week; it is robust to systematic under-surveillance; and it accounts for changes in procedures to diagnose positive cases (see Methods and Appendix Section 2). The reduced-form statistical techniques we use are designed to measure the total magnitude of the effect of changes in policy, without attempting to explain the origin of baseline growth rates or the specific epidemiological mechanisms linking policy changes to infection growth rates (see Methods). Thus, this approach does not provide the important mechanistic insights generated by process-based models; however, it does effectively quantify the key policyrelevant relationships of interest using recent real-world data when fundamental epidemiological parameters are still uncertain.