Abstract
COVID-19 created an unprecedented global public health crisis during 2020–2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.
Highlights
IntroductionCOVID-19 presents the greatest public health challenge humanity has faced in over a century
The local variations of the death rates in April 2020 are significantly associated with the proximity to the “epicenter”, that is, the closer a county is to New York City (NYC), the higher was the death rate
This article presents a hybrid application of systems science, employing a mechanistic stochastic SEIR modeling framework, combined with computational data science, using a Sequential Quasi-Monte Carlo framework for optimal estimation of multiple model parameters, for quantifying the dynamics of COVID-19 within each of the 21 counties of New
Summary
COVID-19 presents the greatest public health challenge humanity has faced in over a century. In the initial phases of the pandemic, before effective preventive and therapeutic pharmacological interventions became available, controlling population exposures to the novel coronavirus SARS-CoV-2 was the only public health option available for mitigating the disease. Developing rational and effective exposure intervention strategies, especially during the first half of 2020, was substantially hindered by complexities and uncertainties associated with the physical and biological processes affecting the spread of the disease.
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