The COVID-19 epidemic has spread worldwide, infected more than 0.6 billion people, and led to about 6 million deaths. Conducting large-scale COVID-19 nucleic acid testing is an effective measure to cut off the transmission chain of the COVID-19 epidemic, but it calls for deploying numerous nucleic acid testing sites effectively. In this study, we aim to optimize the large-scale nucleic acid testing with a dynamic testing site deployment strategy, and we propose a multiperiod location-allocation model, which explicitly considers the spatial–temporal distribution of the testing population and the time-varied availability of various testing resources. Several comparison models, which implement static site deployment strategies, are also developed to show the benefits of our proposed model. The effectiveness and benefits of our model are verified with a real-world case study on the Chenghua district of Chengdu, China, which indicates that the optimal total cost of the dynamic site deployment strategy can be 15% less than that of a real plan implemented in practice and about 2% less than those of the other comparison strategies. Moreover, we conduct sensitivity analysis to obtain managerial insights and suggestions for better testing site deployment in field practices. This study highlights the importance of dynamically deploying testing sites based on the target population’s spatial–temporal distribution, which can help reduce the testing cost and increase the robustness of producing feasible plans with limited medical resources.
Medical overuse is the leading cause of high expenditure among healthcare systems worldwide, with the degree varying from region to region. There is increasing evidence to indicate that in China, National Healthcare Security Administration (NHSA) supervision plays the most crucial role in decreasing medical overuse. For medical overuse, traditional studies focus on empirical researches and qualitative analysis, most of which ignore how the two important participants, i.e., medical institutions and NHSA, affect the strategy of each other. To reduce the losses incurred by insufficient supervision, this study starts from bounded rationality, builds an evolutionary game model to study the relations between the NHSA and medical institutions, and reveals the dynamic evolution process of the supervision of NHSA and overuse of medical institutions. Through stable evolutionary strategy analysis, numerical simulation results, and sensitive experiments under diverse scenarios, we found that when profit gap of medical overuse is high or low, medical institution will adopt fixed strategy, which is medical overuse or appropriate medical use. Only when the profit gap is at a medium level will NHSA’s choice affects medical institutions’ strategy. Furthermore, NHSA’s strategy is affected by the profit gap between medical use and supervision cost. Our work enriches the understanding of supervision for medical overuse and provides theoretical support for the NHSA to make decisions to reach an ideal condition, i.e., to supervise without exertion.
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