Study population and setting
Using US county-level reported cases and human mobility data, the authors developed a metapopulation model (which segments populations of interest into sub-populations, usually based on location) to estimate changes in COVID-19 transmission following stay-at-home measures introduced between March 15 and May 3, 2020. Authors used age-stratified infection fatality rates in the model and generated daily confirmed cases and deaths by county. There were 311 counties with over 400 cases by May 3, 2020; these were modeled separately from the remaining counties, which were further segmented into 16 smaller groups. The authors simulated case numbers and fatalities if stay-at-home measures had been implemented one to two weeks earlier.
Summary of Main Findings
If stay-at-home measures had been implemented one week earlier, the authors estimated that 56.5% (95% CI: 48.1% to 65.9%) of cases and 54% (95% CI: 43.6% to 63.8%) of deaths in the US would have been avoided. Under a scenario in which stay-at-home measures were implemented two weeks earlier, the estimate of cases averted rose to 84.0% (95% CI: 78.7% to 88.4%), and the estimate of deaths averted rose to 82.7% (95% CI: 76.1% to 87.6%).
Authors calibrated the model against known county-level incidence and mortality data from the month prior to and including their timeframe of interest (February 21 to May 3, 2020). In general, metapopulation models allow for the inclusion of more realistic spatial transmission dynamics in epidemic models.
The changes in population behavior modeled by the authors are treated as if they are a monolithic response to a broad aggregate of non-pharmaceutical interventions by federal, state, and local governments; they also assume that all behavior changes are a consequence of these NPIs rather than individual decision-making. Assuming that stay-at-home measures could have been implemented one to two weeks earlier with the same effect on infection dynamics is problematic. First, the public may not have complied as readily with stay-at-home measures earlier, as the perceived threat from COVID-19 may have been lower. Second, the timing of control measure implementation was influenced by other considerations, such as economic concerns. Furthermore, assumptions on inter-county movement were based on 2011-2015 data and may not reflect mobility between counties today. Due to the number of parameters that needed to be estimated as part of the model, the authors assumed a fixed value for the latency period, how long an individual was contagious, the infectiousness of unreported infections, and the proportion of movements that were not work-related. Estimated daily death rates incorporated variation in age structure by county but, given the potential impact of sex and race on case-fatality proportions, it is unclear why other demographic factors were not included. Finally, authors focused on metropolitan areas (e.g., New York, Chicago) and study results may not be applicable to less densely populated US counties.
As our knowledge about the incidence and transmission of COVID-19 evolves, this study contributes to the growing body of literature assessing intervention methods at finer spatial scales. Quantifying the impact of earlier implementation of stay-at-home measures provides a compelling case for early, aggressive intervention.
This review was posted on: 21 July 2020