How to avoid a local epidemic becoming a global pandemic.
Nils Chr Stenseth, Rudolf Schlatte, Xiaoli Liu, Roger Pielke, Ruiyun Li, Bin Chen, Ottar N Bj��rnstad, Dimitri Kusnezov, George F Gao, Christophe Fraser, Jason D Whittington, Yuqi Bai, Ke Deng, Peng Gong, Dabo Guan, Yixiong Xiao, Bing Xu, Einar Broch Johnsen
Author Information
Nils Chr Stenseth: Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo 0316, Norway. ORCID
Rudolf Schlatte: Department of Informatics, University of Oslo, Oslo 0316, Norway.
Xiaoli Liu: Department of Computer Science, University of Helsinki, 00560 Helsinki, Finland.
Roger Pielke: Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo 0316, Norway.
Ruiyun Li: Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo 0316, Norway.
Bin Chen: Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, University of Hong Kong, Hong Kong 999077, China. ORCID
Ottar N Bj��rnstad: Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo 0316, Norway. ORCID
Dimitri Kusnezov: Deputy Under Secretary, Artificial Intelligence & Technology Office, US Department of Energy, Washington, DC 20585.
George F Gao: Chinese Academy of Sciences Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
Christophe Fraser: Pandemic Sciences Institute, University of Oxford, Oxford OX3 7DQ, UK.
Jason D Whittington: Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo 0316, Norway. ORCID
Yuqi Bai: Department of Earth System Science, Tsinghua University, Beijing 100084, China. ORCID
Ke Deng: Center for Statistical Science, Tsinghua University, Beijing 100084, China.
Peng Gong: Department of Earth Sciences, University of Hong Kong, Hong Kong 999077, China. ORCID
Dabo Guan: Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Yixiong Xiao: Business Intelligence Lab, Baidu Research, Beijing 100193, China.
Bing Xu: Department of Earth System Science, Tsinghua University, Beijing 100084, China. ORCID
Einar Broch Johnsen: Department of Informatics, University of Oslo, Oslo 0316, Norway.
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.
