tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2020 Sep 4;202014385.
doi: 10.1073/pnas.2014385117. Online ahead of print.
Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve
Vadim A Karatayev[SUP] 1 [/SUP], Madhur Anand[SUP] 2 [/SUP], Chris T Bauch[SUP] 3 [/SUP]
Affiliations
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.
Keywords: COVID-19; epidemic model; pandemic mitigation; spatially structured model; stochastic model.
. 2020 Sep 4;202014385.
doi: 10.1073/pnas.2014385117. Online ahead of print.
Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve
Vadim A Karatayev[SUP] 1 [/SUP], Madhur Anand[SUP] 2 [/SUP], Chris T Bauch[SUP] 3 [/SUP]
Affiliations
- PMID: 32887803
- DOI: 10.1073/pnas.2014385117
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.
Keywords: COVID-19; epidemic model; pandemic mitigation; spatially structured model; stochastic model.