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首页> 外文期刊>Journal of Korean medical science. >Mass Infection Analysis of COVID-19 Using the SEIRD Model in Daegu-Gyeongbuk of Korea from April to May, 2020
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Mass Infection Analysis of COVID-19 Using the SEIRD Model in Daegu-Gyeongbuk of Korea from April to May, 2020

机译:4月至5月至5月,韩国大邱 - 古孔湾的Covid-19大规模感染分析

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BACKGROUND:The novel coronavirus (coronavirus disease 2019 [COVID-19]) outbreak began in China in December last year, and confirmed cases began occurring in Korea in mid-February 2020. Since the end of February, the rate of infection has increased greatly due to mass (herd) infection within religious groups and nursing homes in the Daegu and Gyeongbuk regions. This mass infection has increased the number of infected people more rapidly than was initially expected; the epidemic model based on existing studies had predicted a much lower infection rate and faster recovery.METHODS:The present study evaluated rapid infection spread by mass infection in Korea and the high mortality rate for the elderly and those with underlying diseases through the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model.RESULTS:The present study demonstrated early infection peak occurrence (-6.3 days for Daegu and -5.3 days for Gyeongbuk) and slow recovery trend (= -1,486.6 persons for Daegu and -223.7 persons for Gyeongbuk) between the actual and the epidemic model for a mass infection region compared to a normal infection region.CONCLUSION:The analysis of the time difference between infection and recovery can help predict the epidemic peak due to mass (or normal) infection and can also be used as a time index to prepare medical resources.? 2020 The Korean Academy of Medical Sciences.
机译:背景:新型冠状病毒(2019年12月在中国爆发的冠状病毒(2019年(Covid-19])爆发,确认案件于2020年2月中旬在韩国开始发生。自2月底以来,感染率大大增加由于大邱和大邱和京湾地区的宗教团体和养老院的质量(群体)感染。这种肿块感染增加了感染者的数量比最初预期的更快;基于现有研究的流行病模型预测了更低的感染率和更快的恢复。方法:本研究评估了韩国大规模感染的快速感染以及通过易感暴露的老年人的死亡率和患有潜在疾病的高死亡率 - 活化 - 回收 - 死亡(Seird)模型。结果:本研究表明了早期感染峰值发生(Daegu的-6.3天和Gyeongbuk的-5.3天)和缓慢的恢复趋势(Daegu的= -1,486.6人和-223.7人Gyeongbuk)与正常感染区的实际和疫情模型的肿块感染区之间的疫情模型。结论:感染和恢复之间的时间差的分析可以有助于预测由于质量(或正常)感染导致的疫情峰值用作准备医疗资源的时间指数。 2020韩国医学科学院。

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