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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 13.20063628 doi: medRxiv preprint shows that peak growth in these counties occurred three days before the order went into effect (17.2%) and turned negative after just three weeks. Growth rates begin to decline following the orders. However, a number of factors might confound this association in the raw data. For example, stay-at-home orders might closely follow earlier targeted mitigation measures at the national level (such as travel restrictions issued by the State Department or recommendations by the CDC on mass gatherings). There might also exist spurious correlation between local factors (such as susceptibility to the disease or the capacity of the health system) and the timing of stay-at-home orders. To control for these factors, we apply the fixed-effects model in equation (3) to the data. Table 1 shows four versions of the model. The main parameter that creates some uncertainty is d , the number of days an infected person remains contagious. Recent research suggests this period is about two weeks, so that is our assumption in Model 1. In Model 2 we show results if we set the value of d to 7 and in Model 3 we set d to 21. There is also some concern that measures of growth in cases of COVID-19 might be affected by the rate in growth in the availability of tests for the disease, so in Model 4 we include that variable as a control. Parameter estimates for all models are similar, so we will focus on Model 1 for the remainder of this analysis. All models include coefficients for days prior to the order that suggest differences in the case growth rate do not predict the timing of stay-in-place orders (Models 1-3 shown in Figure 3 ).