Stenseth, NC;
Schlatte, R;
Liu, X;
Pielke, R;
Li, R;
Chen, B;
Bjørnstad, ON;
... Johnsen, EB; + view all
(2023)
How to avoid a local epidemic becoming a global pandemic.
Proceedings of the National Academy of Sciences of the United States of America (PNAS)
, 120
(10)
, Article e2220080120. 10.1073/pnas.2220080120.
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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.
| Type: | Article |
|---|---|
| Title: | How to avoid a local epidemic becoming a global pandemic |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1073/pnas.2220080120 |
| Publisher version: | https://doi.org/10.1073/pnas.2220080120 |
| Language: | English |
| Additional information: | Copyright © 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). See: https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Keywords: | Disease X, coupled simulation model, data science, digital twin model, epidemiology, Humans, Pandemics, COVID-19, SARS-CoV-2, Disease Outbreaks, Air Travel |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166890 |
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