Managing equitable contagious disease testing: A mathematical model for resource optimization

• Optimizing equitable contagious disease testing resources in a pandemic. • Comparison between scenarios such as ‘quarantine’ and ‘non-quarantine’. • Maximizing the reward of patients and minimizing the number of unserved patients. • Using MOVNS and NSGA-II methods to solve multi-period team orienteering problem. • Apply a real case study for Vienna, Austria. All nations in the world were under tremendous economic and logistical strain as a result of the advent of COVID-19. Early in the epidemic, getting COVID-19 diagnostic tests was a significant difficulty. Furthermore, logistical challenges arose from the restricted transportation infrastructure and disruptions in international supply chains in the distribution of these testing kits. In the face of such obstacles, it is critical to give patients' needs top priority in order to provide fair access to testing. In order to manage contagious disease testing, this work proposes a bi-objective and multi-period mathematical model with an emphasis on mobile tester route plans and testing resource allocation. In order to optimize patient scores and reduce the likelihood of patients going untreated, the suggested team orienteering model takes into account issues like resource limitations, geographic clustering, and testing capacity limitations. To this aim, we present a comparison between quarantine and non-quarantine scenarios, introduce an equitable categorization based on disease backgrounds into “standard” and “risky” groups, and cluster geographical locations according to average age and contact rate. We use a Multi-Objective Variable Neighborhood Search (MOVNS) and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve our problem. Due to the superiority of MOVNS, it is applied to a case study in Vienna, Austria. The results demonstrate that, over the course of several weeks, the average number of unserved risky patients in the prioritizing scenario is consistently lower than the usual number of patients. In the absence of prioritization, the average number of high-risk patients who remain untreated rises sharply and exceeds that of regular patients, though. Furthermore, it is clear that waiting times are greatly impacted by demand volume when comparing scenarios with and without quarantine.