传染病流行事件下医疗资源需求模型构建与仿真试验

Construction and simulation of medical resources demand model during epidemic events of infectious diseases

摘要:

目的 构建重大传染病流行事件下包含普通病床、重症监护病房(ICU)病床、呼吸机、医护人力四类医疗资源的需求模型,同时对不同医疗资源紧缺时传染病救治情况进行模拟分析。方法 以传染病动力学模型(SEIR)为基础,考虑感染者传染性、人群易感性和康复者免疫性,同时结合新型冠状病毒感染(COVID-19)无症状感染者和不同临床分型特点扩展构建"COVID-19传染-住院模型",收集设定疾病传播、临床病程和医疗资源紧缺情景各项参数,依托Anylogic平台初步形成传染病流行事件下城市医疗资源配置供给分析模型,分析不同场景传染病事件下医疗资源供给需求情况。结果 无干预情景下,COVID-19床位资源需求峰值时间为第107天,峰值为160.92张/千人;呼吸机资源需求峰值时间为第122天,峰值为5.61台/千人;ICU床位需求峰值时间为第117天,峰值为12.78张/千人;医护人力资源需求峰值时间为第109天,峰值为151.12人/千人。仿真试验结果提示不同医疗资源对医疗救治结局的影响存在一定的差异。结论 本研究构建了传染病流行事件下城市医疗资源配置供给的分析工具,多个仿真试验结果提示,床位资源和医护人力资源对于医疗救治结局有着更为重要的作用。

Abstract:

Objective To construct the demand model of four types of medical resources including beds in hospital, beds in intensive care unit (ICU), ventilators and medical human resources during the major infectious disease epidemic events, simulate and analyze the treatment of infectious diseases when different medical resources are in short supply. Methods Based on the susceptible-exposed-infectious-recovered (SEIR) model, considering the infectivity of infected persons, the susceptibility of the population and the immunity of convalescents, the characteristics of asymptomatic COVID-19 patients and different clinical types, the "COVID-19 infection-hospitalization model" was constructed. By collecting and setting the parameters of disease transmission, clinical course and medical resource shortage scenarios, an analysis model of allocation and supply of urban medical resources during infectious di-sease epidemic events was initially formed based on Anylogic platform, the supply and demand of medical resources during infectious disease events in different scenarios were analyzed. Results In the non-intervention scenario, the peak time of bed demand was on the 107th day, and the peak value was 160.92 beds per thousand people; the peak time of ventilator demand was on the 122nd day, and the peak value was 5.61 units per thousand people; the peak time of ICU bed demand was on the 117th day, and the peak value was 12.78 beds per thousand people; the peak time of the demand for medical human resources was on the 109th day, and the peak value was 151.12 persons per thousand persons. The simulation Results suggested that there were some differences in the impact of different medical resources on the outcome of medical treatment. Conclusion This study constructs an analytical tool for the allocation and supply of urban medical resources under the epidemic events of infectious diseases, and the Results of multiple simulation experiments suggest that bed resources and medical human resources play more important roles in the outcome of medical treatment.