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Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey

Author

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  • Xiang Ren

    (Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
    Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA)

  • Clifford P. Weisel

    (Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
    Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ 08854, USA
    Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA)

  • Panos G. Georgopoulos

    (Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
    Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
    Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ 08854, USA
    Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA)

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.

Suggested Citation

  • Xiang Ren & Clifford P. Weisel & Panos G. Georgopoulos, 2021. "Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey," IJERPH, MDPI, vol. 18(22), pages 1-25, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:11950-:d:678789
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    References listed on IDEAS

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    1. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    2. Nikolaos P. Rachaniotis & Thomas K. Dasaklis & Filippos Fotopoulos & Platon Tinios, 2021. "A Two-Phase Stochastic Dynamic Model for COVID-19 Mid-Term Policy Recommendations in Greece: A Pathway towards Mass Vaccination," IJERPH, MDPI, vol. 18(5), pages 1-21, March.
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