- Daily cumulative confirmed cases of COVID-19 over time (solid black line, left axis)
- Deaths over time (dashed black line)
- Vertical lines are deployments of anti-contagion policies, for which the height indicates the number of administrative units that instituted a policy that day (right axis)
- Maps of cumulative confirmed cases by administrative unit on the last date of each sample (right)

University of Michigan - Ann Arbor

- Governments around the world have responded to SARS-CoV-2 with unprecedented policies designed to slow the growth of infections.
- Many policies impose large and visible costs on society, but their benefits cannot be directly observed and are only currently understood through process-based simulations. 
- The objective of this study is to measure the direct health benefits of anti-contagion policies, specifically, how much of these policies slowed the growth rate of  infections. 

Effectiveness of Large-Scale Anti-contagion Policies

Hsiang, S., Allen, D., Annan-Phan, S. et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 584, 262–267 (2020). https://doi.org/10.1038/s41586-020-2404-8

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

Data on COVID-19 infections and large-scale anti-contagion policies

- Researchers estimated the effects of 1,700 local, regional and national policies on the growth rate of infections across localities within China, South Korea, Italy, Iran, France and the United States.

- Subnational data was compiled on daily infection rates, changes in case definitions and the timing of policy deployments, including:
(1) travel restrictions
(2) social distancing through the cancellations of events and suspensions of educational, commercial and religious activities
(3) quarantines and lockdowns
(4) additional policies such as emergency declarations and expansions of paid sick leave, from the earliest available dates to 6 April 2020.

- The daily growth rate of infections in each locality in response to the collection of ongoing policies applied to that locality on that day was examined. 

Data Collection

- It is estimated that in the absence of policies, early infection rates of COVID-19 grow 43% per day on average across China, South Korea, Italy, Iran, France, and the United States. This implies a doubling time of approximately 2 days.
- Country specific estimates range from 34% per day in the US to 68% per day in Iran. 
- However, it cannot be determined whether the high estimate for Iran results from true epidemiological differences, data-quality issues, or the concurrence of the initial outbreak with a major religious holiday and pilgrimage. 
- Excluding Iran, the average growth rate is 38% per day in across the five other countries. 
- Growth rates in all five other countries are independently estimated to be very near the average growth rate. These estimated values differ from observed average growth rates because the latter are confounded by the effects of policies. 
- The growth rates are not driven by the expansion of testing or increasing rates of case detection. 

Estimates for Growth Rates

Researchers employed well-established, reduced-form econometric techniques—commonly used to evaluate the effects of events on economic growth rates—to measure the impact of large-scale anti-contagion policies on the growth rate of COVID-19 infections. This technique estimates the total magnitude of policy changes without requiring explicit prior information about fundamental epidemiological parameters, empirically recovering the collective influence of these factors directly from the data. The approach accounts for differences in baseline infection growth rates across subnational locations (affected by time-invariant characteristics like demographics and health systems), systematic temporal patterns unrelated to policy (such as workweek effects), systematic under-surveillance, and changes in diagnostic procedures.

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