https://www.socictopen.socict.org/files/original/3dc149a441a1408878195803a4375941.pdf 00cb06e2301bc749b470aa2fd29410df Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies Creator An entity primarily responsible for making the resource Jürgen Kurths, Chunyu Wang, Yue Deng, Ziheng Yuan, Chijun Zhang, Fan Zhang, Qing Cai, Chao Gao, Chao Gao, Jurgen Kurths Description An account of the resource The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore 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 in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, Multi-objective optimization, computational epidemiology, epidemic propagation, emergence management, medical emergency resources Identifier An unambiguous reference to the resource within a given context 10.3389/fphy.2020.00383 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Physics