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

The 2019 novel coronavirus 1 pandemic is forcing societies around the world to make consequential policy decisions with limited information. After containment of the initial outbreak failed, attention turned to implementing large-scale social policies designed to slow contagion of the virus, 6 with the ultimate goal of slowing the rate at which life-threatening cases emerge so as to not exceed the capacity of existing medical systems. In general, these policies aim to decrease opportunities for virus transmission by reducing contact among individuals within or between populations, such as by closing schools, limiting gatherings, and restricting mobility. Such actions are not expected to halt contagion completely, but instead are meant to slow the spread of COVID-19 to a manageable rate. These large-scale policies are developed using epidemiological simulations 2, 4, 7-17 and a small number of natural experiments in past epidemics. 18 However, the actual impacts of these policies on infection rates in the ongoing pandemic are unknown. Because the modern world has never experienced a pandemic from this pathogen, nor deployed anti-contagion policies of such scale and scope, it is crucial that direct measurements of policy impacts be used alongside numerical simulations in current decision-making.