The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States

James H. Fowler; Seth J. Hill; Nick Obradovich; Remy Levin

is the (which was not peer-reviewed) The copyright holder for this preprint . Table 1 can be interpreted as percentage point changes in the rate of growth of COVID-19 infections associated with the number of days since a stay-at-home order has gone into effect. Negative numbers indicate a slowing of rate of growth though it is important to note that a smaller rate of growth still means an increasing number of total cases. For example, by Day 2, counties with stay-at-home orders achieve their first statistically meaningful reduction in the case growth rate (2.7 percentage points, 95% CI 0.2 to 5.2). After a week the reduction in the rate is 3.9 percentage points (1.0 to 6.9). At two weeks the reduction in the rate is 6.9 percentage points (2.4 to 11.5). And by Day 27, the expected reduction in the infection growth rate (22.6 percentage points, CI 14.8 to 30.5) has surpassed the average magnitude of growth rate at its peak (17.2%). When the growth rate turns negative, the number of new daily infections will start to decline and the epidemic will eventually come to a halt. Figure 3 shows these estimates along with the estimates for each day prior to the day a stay-at-home order goes into effect. Each panel of the figure displays the results given different assumptions about the number of days cases remain infectious. Unlike the raw data shown in Figure 2b , the estimates here are adjusted for unobserved factors that vary over time and between counties that can influence the course of the disease. Notice that the estimates in Figure 3 before the order goes into effect stay very close to zero. This suggests that differences in case growth are not influencing the timing of stay-at-home orders, helping to rule out the possibility that the later associations we see are driven by reverse-causality or differential trends.