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Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis

机译:模拟冠状病毒疾病趋势2019年中国大都会医疗服务运作能力恢复:一种机器学习与数学模型的分析

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BackgroundTo contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.MethodsReal-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.ResultsThe number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month.ConclusionThe COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.
机译:背景技术含有2019年冠状病毒疾病的爆发(Covid-19),政府通过了许多前所未有的干预措施。但是,这些措施可能会干扰正常的医疗服务。我们试图建模Covid-19的趋势,并估算中国大都会医疗服务的运作能力恢复。从开放来源提取Covid-19和人口流动数据的方法数据。 SEIR(易感,暴露,传染性,恢复的)和神经网络模型(NNS)建立在武汉,北京,上海和广州的模型疾病趋势。结合公共交通数据,自回归综合移动平均(ARIMA)模型用于估算北京,上海和广州的疫情期间对非局部住院的累计需求。感染者和死亡人数将增加45%和567%鉴于政府只在武汉实施的交通管制,没有额外的医疗专业人士。武汉的流行病(通过累积证实案件衡量)预计将于3月底达到转折点,并于2020年4月延迟结束。预计北京,上海和广州的爆发将在3月底和医疗结束时结束服务可以在4月中旬完全恢复正常。在流行病中,分别在北京,上海和广州分别下跌69.86%,57.41%和66.85%。在疫情结束后,位于这些大都市的医疗中心可能面临58,799(95%CI 48926-67,232)的第一个月额外的住院需求。结论中国的Covid-19流行病已经有效地遏制了全国各地的医疗服务4月份恢复正常。然而,对其他疾病的巨额未满足的医疗需求可能导致患者及其家庭的巨大迁移,为潜在无症状病毒载体的主要大都市和疾病控制带来了巨大挑战。

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