Analytics for Resource Allocation in Pandemic Management
The COVID 19 pandemic highlighted the importance and difficulty of allocating scarce containment resources based on accurate predictions of disease spread. While extensive research has focused on descriptive and predictive analytics to support decision making, much less attention has been given to prescriptive analytics that connects pandemic forecasts with resource allocation decisions. This review examines existing analytics research and identifies future opportunities for more integrated approaches to pandemic resource management.
Authors
- Martin Bicher (ORCID: https://orcid.org/0000-0002-1362-6868)
- Jarmo Haferkamp (ORCID: https://orcid.org/0000-0002-5914-7539)
- Jan Fabian Ehmke (ORCID: https://orcid.org/0000-0001-8474-7483)
- Peiman Ghasemi (ORCID: https://orcid.org/0000-0002-1085-0776)
- Nikolas Popper
- Marlin W. Ulmer (ORCID: https://orcid.org/0000-0003-2499-6570)
Institutions
- Vienna University of Economics and Business (AT)
- TU Wien (AT)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- INFORMS Journal on Applied Analytics
- Published
- 2026-06-08
- DOI
- https://doi.org/10.1287/inte.2025.0275
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00