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Global age-structured spatial modeling for emerging infectious diseases like COVID-19.
Xiao, Yixiong; Zhou, Jingbo; Cheng, Qu; Yang, Jun; Chen, Bin; Zhang, Tao; Xu, Lei; Xu, Bo; Ren, Zhehao; Liu, Zhaoyang; Shen, Chong; Wang, Che; Liu, Han; Li, Xiaoting; Li, Ruiyun; Yu, Le; Guan, Dabo; Zhang, Wusheng; Wang, Jie; Hou, Lin; Deng, Ke; Bai, Yuqi; Xu, Bing; Dou, Dejing; Gong, Peng.
Afiliação
  • Xiao Y; Business Intelligence Lab, Baidu Research, Beijing 100193, China.
  • Zhou J; Business Intelligence Lab, Baidu Research, Beijing 100193, China.
  • Cheng Q; Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Yang J; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Chen B; Division of Landscape Architecture, The University of Hong Kong, Hong Kong 999007, China.
  • Zhang T; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Xu L; Vanke School of Public Health, Tsinghua University, Beijing 100084, China.
  • Xu B; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Ren Z; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Liu Z; Center for Statistical Science, Tsinghua University, Beijing 100084, China.
  • Shen C; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
  • Wang C; Center for Statistical Science, Tsinghua University, Beijing 100084, China.
  • Liu H; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
  • Li X; Center for Statistical Science, Tsinghua University, Beijing 100084, China.
  • Li R; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
  • Yu L; Business Intelligence Lab, Baidu Research, Beijing 100193, China.
  • Guan D; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Zhang W; School of Public Health (SPH), Nanjing Medical University, Nanjing 211166, China.
  • Wang J; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Hou L; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Deng K; Department of Computer Science and Technology, Institute of High Performance Computing, Tsinghua University, Beijing 100084, China.
  • Bai Y; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
  • Xu B; AI for Earth Laboratory, Cross-Strait Institute, Tsinghua University, Beijing 100084, China.
  • Dou D; Center for Statistical Science, Tsinghua University, Beijing 100084, China.
  • Gong P; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
PNAS Nexus ; 2(5): pgad127, 2023 May.
Article em En | MEDLINE | ID: mdl-37143866
ABSTRACT
Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: PNAS Nexus Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: PNAS Nexus Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China