tetano
Editor, Senior Moderator
Math Biosci
. 2023 Oct 17:109087.
doi: 10.1016/j.mbs.2023.109087. Online ahead of print. Climate-dependent effectiveness of nonpharmaceutical interventions on COVID-19 mitigation
Juping Ji[SUP] 1 [/SUP], Hao Wang[SUP] 2 [/SUP], Lin Wang[SUP] 3 [/SUP], Pouria Ramazi[SUP] 4 [/SUP], Jude Dzevela Kong[SUP] 5 [/SUP], James Watmough[SUP] 3 [/SUP]
Affiliations
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.
Keywords: Environmental conditions; Generalized boosting model; Inverse method; Machine learning; Nonpharmaceutical interventions; Transmission rate.
. 2023 Oct 17:109087.
doi: 10.1016/j.mbs.2023.109087. Online ahead of print. Climate-dependent effectiveness of nonpharmaceutical interventions on COVID-19 mitigation
Juping Ji[SUP] 1 [/SUP], Hao Wang[SUP] 2 [/SUP], Lin Wang[SUP] 3 [/SUP], Pouria Ramazi[SUP] 4 [/SUP], Jude Dzevela Kong[SUP] 5 [/SUP], James Watmough[SUP] 3 [/SUP]
Affiliations
- PMID: 37858753
- DOI: 10.1016/j.mbs.2023.109087
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.
Keywords: Environmental conditions; Generalized boosting model; Inverse method; Machine learning; Nonpharmaceutical interventions; Transmission rate.