The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estim...The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.展开更多
基金funding from the National Science and Technology Major Project of the Ministry of Science and Technology of China[grant number 2017YFB0503605]the National Natural Science Foundation of China[grant number 41771478]+3 种基金the Fundamental Research Funds for the Central Universities[grant number 2019B02514]Natural Science Foundation of Beijing,China[grant number 8172046]the China Scholarship Council(CSC)Queen Mary University of London.
文摘The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.
