Professor Kostas Nikolopoulos kostas.nikolopoulos@durham.ac.uk
Professor
Forecasting the Effective Reproduction Number during a Pandemic: COVID-19 Rt forecasts, Governmental Decisions, and Economic Implications
Nikolopoulos, Kostas; Vasilakis, Chrysovalantis
Authors
Chrysovalantis Vasilakis
Abstract
This research empirically identifies the best-performing forecasting methods for the Effective Reproduction Number Rt of COVID-19, the most used epidemiological parameter for policymaking during the pandemic. Furthermore, based on the most accurate forecasts for the United Kingdom, we model the excess exports and imports during the pandemic (using World Trade Organization data), while simultaneously controlling for governmental decisions, i.e., lockdown(s) and vaccination. We provide empirical evidence that the longer the lockdown lasts, the larger the cost to the economy is, predominantly for international trade. We show that imposing a lockdown leads to exports falling by 16.55% in the United Kingdom; without a lockdown, the respective decrease for the same period would be only 1.57%. On the other hand, efforts towards fast population vaccination improve the economy. We believe our results can help policymakers to make better decisions before and during future pandemics.
Citation
Nikolopoulos, K., & Vasilakis, C. (2024). Forecasting the Effective Reproduction Number during a Pandemic: COVID-19 Rt forecasts, Governmental Decisions, and Economic Implications. IMA Journal of Management Mathematics, 35(1), 65-81. https://doi.org/10.1093/imaman/dpad023
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 6, 2023 |
Online Publication Date | Oct 15, 2023 |
Publication Date | 2024-01 |
Deposit Date | Nov 6, 2023 |
Publicly Available Date | Nov 7, 2023 |
Journal | IMA Journal of Management Mathematics |
Print ISSN | 1471-678X |
Electronic ISSN | 1471-6798 |
Publisher | Oxford University Press |
Peer Reviewed | Peer Reviewed |
Volume | 35 |
Issue | 1 |
Pages | 65-81 |
DOI | https://doi.org/10.1093/imaman/dpad023 |
Public URL | https://durham-repository.worktribe.com/output/1898637 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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