Climate-dependent effectiveness of nonpharmaceutical interventions on COVID-19 mitigation.

Autor: Ji J; Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; Interdisciplinary Lab for Mathematical Ecology and Epidemiology, University of Alberta, Edmonton, AB T6G 2R3, Canada., Wang H; Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; Interdisciplinary Lab for Mathematical Ecology and Epidemiology, University of Alberta, Edmonton, AB T6G 2R3, Canada. Electronic address: hao8@ualberta.ca., Wang L; Department of Mathematics and Statistics, University of New Brunswick, Fredericton, NB E3B 5A3, Canada., Ramazi P; Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada., Kong JD; Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada., Watmough J; Department of Mathematics and Statistics, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.
Jazyk: angličtina
Zdroj: Mathematical biosciences [Math Biosci] 2023 Dec; Vol. 366, pp. 109087. Date of Electronic Publication: 2023 Oct 18.
DOI: 10.1016/j.mbs.2023.109087
Abstrakt: Environmental factors have a significant impact on the transmission of infectious diseases. Existing results show that the novel coronavirus can persist outside the host. We propose a susceptible-exposed-presymptomatic-infectious-asymptomatic-recovered-susceptible (SEPIARS) model with a vaccination compartment and indirect incidence to explore the effect of environmental conditions, temperature and humidity, on the transmission of the SARS-CoV-2 virus. Using climate data and daily confirmed cases data in two Canadian cities with different atmospheric conditions, we evaluate the mortality rates of the SARS-CoV-2 virus and further estimate the transmission rates by the inverse method, respectively. The numerical results show that high temperature or humidity can be helpful in mitigating the spread of COVID-19 during the warm summer months. Our findings verify that nonpharmaceutical interventions are less effective if the virus can persist for a long time on surfaces. Based on climate data, we can forecast the transmission rate and the infection cases up to four weeks in the future by a generalized boosting machine learning model.
Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest concerning the publication of this manuscript.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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