Analytics for Resource Allocation in Pandemic Management

As the COVID-19 pandemic demonstrated, having the right containment resources, such as testing kits, vaccines, and personal protective equipment like masks, at the right time and location to achieve effective pandemic containment and to mitigate pandemic spread is both critical and challenging. High-quality predictions are essential for allocating such scarce resources, and, as our narrative review will demonstrate, substantial work has been conducted on descriptive and predictive analytics during the COVID-19 pandemic. Policymakers attempted to incorporate the resulting information into immediate decision making in reaction to the predicted spread. However, to be prepared for the next pandemic, it is crucial to focus on prescriptive analytics that account for the dynamic interaction between predicted pandemic development and prescribed resource allocation. As we will show, this integrated view has been limited in the literature. To this end, we analyze analytics research conducted during and after the COVID-19 pandemic and its impact on resource allocation. Furthermore, we identify future research streams for resource allocation management, especially those that require close interaction of descriptive and predictive analytics.

History: This paper was refereed.

Funding: This work was supported by the Austrian Science Fund (FWF) [Grant I 5908-G] (M. Bicher, J. F. Ehmke, P. Ghasemi, N. Popper), the German Research Foundation (DFG) [Grants 494812908 (J. Haferkamp) and 444657906 (M. W. Ulmer)], and the state of Saxony-Anhalt through the NACHOS graduate school and co-financed by ESF+ funds [funding number ZS/2023/12/182225 (J. Haferkamp)].