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Stenseth NC, Schlatte R, Liu X, Pielke R, Li R, Chen B, Bjørnstad ON, Kusnezov D, Gao GF, Fraser C, Whittington JD, Bai Y, Deng K, Gong P, Guan D, Xiao Y, Xu B, Johnsen EB. How to avoid a local epidemic becoming a global pandemic. Proc Natl Acad Sci U S A 2023; 120:e2220080120. [PMID: 36848570 PMCID: PMC10013804 DOI: 10.1073/pnas.2220080120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/10/2023] [Indexed: 03/01/2023] Open
Abstract
Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.
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Affiliation(s)
- Nils Chr. Stenseth
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Rudolf Schlatte
- Department of Informatics, University of Oslo, Oslo0316, Norway
| | - Xiaoli Liu
- Department of Computer Science, University of Helsinki, 00560Helsinki, Finland
| | - Roger Pielke
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Environmental Studies, University of Colorado Boulder, Boulder, CO80309
| | - Ruiyun Li
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Bin Chen
- Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, University of Hong Kong, Hong Kong999077, China
- Department of Geography, Urban Systems Institute, University of Hong Kong, Hong Kong999077, China
- HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong999077, China
| | - Ottar N. Bjørnstad
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA16802
| | - Dimitri Kusnezov
- Deputy Under Secretary, Artificial Intelligence & Technology Office, US Department of Energy, Washington,DC20585
| | - George F. Gao
- Chinese Academy of Sciences Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing100101, China
- Chinese Center for Disease Control and Prevention, Beijing102206, China
| | - Christophe Fraser
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7DQ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford0X3 7LFUK
| | - Jason D. Whittington
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Yuqi Bai
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing100084, China
| | - Peng Gong
- Department of Earth Sciences, University of Hong Kong, Hong Kong999077, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Dabo Guan
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing100193, China
| | - Bing Xu
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
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