Suchergebnisse

AbstractWe study the problem of designing vaccine distribution strategies that maximally mitigate the negative impact of an infectious disease outbreak. This is achieved through a multiperiod optimization‐based framework that embeds important subject‐specific risk and contact information into the decision‐making process. By analyzing the structure of the resulting optimization problem, we identify key structural properties which we use to construct a globally convergent solution scheme (suitable for smaller problem instances) and two, more scalable, heuristic schemes. We demonstrate the benefits of the considered framework through a case study on COVID‐19 in Texas. Our results highlight the importance of considering risk and contact information as doing so substantially reduces the total expected number of fatalities over conventional compartmental‐based approaches. These findings indicate that customization can have a significant benefit, particularly for community‐scale planning.

AbstractWe study the problem of designing optimal targeted mass screening of non‐uniform populations. Mass screening is an essential tool that is widely utilized in a variety of settings, for example, preventing infertility through screening programs for sexually transmitted diseases, ensuring a safe blood supply for transfusion, and mitigating the transmission of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget. In this paper, we address this problem by proposing a proactive optimization‐based framework that factors in population heterogeneity, limited budget, different testing schemes, the availability of multiple assays, and imperfect assays. By analyzing the resulting optimization problem, we take advantage of the structure of the problem as a multi‐dimensional fractional knapsack problem and identify an efficient globally convergent threshold‐style solution scheme that fully characterizes an optimal solution across the entire budget spectrum. Using real‐world data, we conduct a geographic‐based nationwide case study on targeted COVID‐19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications while utilizing the same amount of budget. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions.

AbstractMotivated by the COVID‐19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time‐varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi‐wave transmission dynamics. We evaluate the performance of various information‐sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro‐rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze‐period constraints. For the case of a multi‐wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro‐rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro‐rata policy. Thus our results support the conclusion that the widely used pro‐rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines.

AbstractTesting provides essential information for managing infectious disease outbreaks, such as the COVID‐19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease‐positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false‐negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data‐driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID‐19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID‐19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.