How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies - Wang, Chunyu; Deng, Yue; Yuan, Ziheng; Zhang, Chijun; Zhang, Fan; Cai, Qing; Gao, Chao; Kurths, Jurgen (Frontiers in Physics, 2020)
The solutions to supply and allocate medical emergency resources in public health emergencies greatly affect the efficiency of the epidemic prevention and control. Currently, the main problem in the computational epidemiology is how to adjust the allocation scheme by the epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention and the social allocation balance. More specifically, the medical emergency resources demands in metropolitans are varied with different local epidemic situations. Therefore, it is difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity, Shenzhen in China, as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision making process of the Shenzhen center on disease control and prevention, but also enables us to control the potential propagation of COVID-19 and other epidemics.