Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Nikolopoulos, Konstantinos; Punia, Sushil; Schäfers, Andreas; Tsinopoulos, Christos; Vasilakis, Chrysovalantis

doi:10.1016/j.ejor.2020.08.001

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Nikolopoulos, Konstantinos; Punia, Sushil; Schäfers, Andreas; Tsinopoulos, Christos; Vasilakis, Chrysovalantis

Authors

Professor Kostas Nikolopoulos kostas.nikolopoulos@durham.ac.uk
Professor

Sushil Punia

Andreas Schäfers

Christos Tsinopoulos

Chrysovalantis Vasilakis

Abstract

Policymakers during 1COVID-19 operate in uncharted 2territory and must make tough decisions. Operational Research - the ubiquitous ‘science of better’ - plays a vital role in supporting this decision-making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID-19 growth rates with statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown). Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.

Citation

Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99-115. https://doi.org/10.1016/j.ejor.2020.08.001

Journal Article Type	Article
Acceptance Date	Aug 3, 2020
Online Publication Date	Aug 8, 2020
Publication Date	2020
Deposit Date	Aug 4, 2020
Publicly Available Date	Aug 8, 2022
Journal	European Journal of Operational Research
Print ISSN	0377-2217
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	290
Issue	1
Pages	99-115
DOI	https://doi.org/10.1016/j.ejor.2020.08.001
Public URL	https://durham-repository.worktribe.com/output/1295464

Files

Accepted Journal Article (848 Kb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
© 2020 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/