Abstract
In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.
Highlights
In the late stages of an epidemic, infections are often sporadic and geographically distributed
Our objective is to develop a spatially structured stochastic model of SARS-CoV-2 transmission, testing, and school and workplace closure in Ontario in order to address three questions: 1) Are closures best lifted at the scale of an entire province or on a county-by-county basis? 2) Does coordination of testing protocols and reopening criteria between counties improve outcomes? 3) How well can a spatially phased approach work in the early stages of the epidemic? We use our model to determine the timing and organizational scale at which school and workplace reopening strategies can minimize both the number of infections and person-days lost to closures, during the latestage and early-stage epidemic
Symptomatic individuals are tested for SARS-CoV-2, and their status becomes ascertained with some probability per day
Summary
In the late stages of an epidemic, infections are often sporadic and geographically distributed. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing In such early and late stages of an epidemic, a stochastic, spatially structured model can capture important features of disease dynamics [10,11,12,13]. As cases continue to decline on the far side of the COVID19 epidemic curve in Ontario, decision makers will make choices about how and when to lift restrictions They will face a very different epidemiological landscape than the middle stages of the outbreak, when infections were numerous. Efforts to contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks have failed in many jurisdictions, including Ontario, Canada, leading decision makers to supplement contact tracing with effective but socioeconomically costly interventions such as school and workplace closure and other means of physical distancing [4, 5]
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