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Assessing eco-geographic influences on COVID-19 transmission: a global analysis.
Pan, Jing; Villalan, Arivizhivendhan Kannan; Ni, Guanying; Wu, Renna; Sui, ShiFeng; Wu, Xiaodong; Wang, XiaoLong.
Afiliación
  • Pan J; Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province, Heilongjiang Province, Harbin, 150040, People's Republic of China.
  • Villalan AK; College of Wildlife and Protected Area, Northeast Forestry University, Heilongjiang Province, Harbin, 150040, People's Republic of China.
  • Ni G; Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province, Heilongjiang Province, Harbin, 150040, People's Republic of China.
  • Wu R; College of Wildlife and Protected Area, Northeast Forestry University, Heilongjiang Province, Harbin, 150040, People's Republic of China.
  • Sui S; HaiXi Animal Disease Control Center, Qinghai Province, Delingha, 817099, People's Republic of China.
  • Wu X; HaiXi Animal Disease Control Center, Qinghai Province, Delingha, 817099, People's Republic of China.
  • Wang X; Zhaoyuan Forest Resources Monitoring and Protection Service Center, Shandong Province, Zhaoyuan, 265400, People's Republic of China.
Sci Rep ; 14(1): 11728, 2024 05 22.
Article en En | MEDLINE | ID: mdl-38777817
ABSTRACT
COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido