Mark Lurie, PhD, MPH, on How the Stay-At-Home Orders Impacted the Spread of COVID-19
By the end of April 2020, the United States accounted for 33% of all reported COVID-19 infections.1 Despite having not yet seen the peak number of cases, many states took measures to slow the spread of infection, including implementing stay-at-home orders.
In a recent study, researchers examined how the stay-at-home orders and lack thereof have affected the pandemic doubling time to better understand the role these orders had in slowing the spread of COVID-19.2
Results showed that the national pandemic doubling time was 2.68 days before mitigation measures were put into place, increasing to an average of 15 days after these measures were implemented.
While the doubling time increased across the board, the rate of increase varied from state to state. The researchers found that the states without stay-at-home orders fared far worse, on average, than states that had stay-at-home orders. The 45 states with this mitigation measure in place added an average of 12.27 days to their doubling time while the other 5 states only added about 6 days to their doubling time.
Lead researcher Mark Lurie, PhD MPH, who is an associate professor of epidemiology at Brown University in Providence, Rhode Island, answered our questions about these findings and their implications.
Infectious Diseases Consultant: Was the significant difference in epidemic doubling time for states with and without stay-at-home orders surprising to you, or did you anticipate this?
Mark Lurie: I did expect that states without stay-at-home orders would have epidemics that were growing faster than those with stay-at-home orders, but I did not expect that the differences would be so large. Stay at home orders, when followed, should result in less transmission because the epidemiological impact of stay-at-home orders is that people have fewer contacts, therefore fewer opportunities to transmit the virus to others, resulting in smaller transmission chains.
The fact that the few states without stay-at-home orders fared so poorly was surprising to me. There were 5 states (Arizona, Iowa, North Dakota, Nebraska, and South Dakota) that did not have stay-at-home orders, and 4 of them were the bottom 4 states nationally in terms of slowing the epidemic doubling time (South Dakota was the outlier). So, the differences were stark and added to the evidence that stay-at-home orders were associated with slowing the epidemic.
ID CON: Aside from the stay-at-home orders, what could be some of the other contributing factors to the significant jump in the media epidemic doubling time in April 2020?
ML: We only looked at stay-at-home orders, admittedly a large category that encompasses many things, including limiting the size of social gatherings, mandating masks, closing stores, and potentially multiple other restrictions. Future research will unpack which aspects of stay-at-home orders were most effective. I suspect social distancing and mask wearing will turn out to have been particularly important predictors of slowing epidemics, but no doubt there will be others that we do not expect. For example: Will cities with Democratic or Republican governors be more or less successful at stemming the epidemic?
ID CON: Do you believe your findings would differ if the study period extended into more recent months? If so, how?
ML: We are already analyzing the more recent data, though it is too early to say what we will learn. The analysis will be more complicated but potentially more interesting as well, as you essentially have a natural experiment where the tap is turned off, then on, then opened slowly, sometimes closing again. This on again/off again approach will allow for a more sophisticated analysis, including identifying a potential dose-response should it, as I suspect, exist. That would add another layer of evidence supporting the study’s hypothesis.
ID CON: What knowledge gaps involving the epidemic doubling time and stay-at-home orders still need to be addressed?
ML: There is still much to learn about the impact of specific aspects of stay-at-home orders. For example, was it people staying home or people wearing masks or bars being shut (or multiple other actions) that precipitated the increase in doubling time? With more data and more analysis, we could isolate key aspects of stay-at-home orders and evaluate which of these strategies were most effective.
References:
- Stay-at-home orders significantly associated with reduced spread of COVID-19, study finds. News release. Brown University. August 13, 2020. Accessed September 2, 2020. https://www.brown.edu/news/2020-08-12/stay-home
- Lurie MN, Silva J, Yorlets RR, Tao J, Chan PA. COVID-19 epidemic doubling time in the United States before and during stay-at-home restrictions. J Infect Dis. Published online August 1, 2020. doi:0.1093/infdis/jiaa491