COVID-19疫情的实时区域传播分析、预测与预警

Real-time Regional Spread Analysis,Prediction and Early Warning of COVID-19 Epidemic

自2019年12月首个病例在中国武汉出现以来,新型冠状病毒肺炎(简称新冠肺炎或COVID-19)的确诊病例数以前所未有的速度席卷全国,并蔓延至世界160多个国家和地区.截止3月18日,国内针对新冠肺炎的战"役"已接近尾声,除境外输入病例外,全国除港澳台新增确诊病例已经多日为个位数.在新形势下,复工生产已经是迫在眉睫的任务.然而,随着疫情在意大利、西班牙等欧洲各国以及伊朗、韩国、美国的蔓延,再次提醒大家病毒传播威力极大,疫情防控仍然紧迫.本文首先基于从1月25日到2月23日丁香园实时疫情网站的确诊病例数据建立数学模型,对国内各省和主要城市的疫情趋势和关键时间节点做出分析和预测,并将结果与3月18日的疫情状况进行比较,证明该预测符合疫情的发展趋势,可以对未来的疫情趋势做出预测.由此,基于实时发布的国内外疫情数据,我们建立针对COVID-19的疫情预警系统,从多个方面展示全球疫情的区域传播趋势,并且实时监控可能出现的区域蔓延态势.

Since the first case appeared in Wuhan,China in December 2019,the number of confirmed cases of the 2019 novel coronavirus(referred to as Corona Virus Disease 2019,or COVID-19) has swept the country at an unprecedented rate and spread to more than 160 countries and regions in the world.As of March 18,the domestic battle against COVID-19 had come to an end.Apart from imported cases abroad,there are only a few newly confirmed cases in the main land of China for several days.Under the new situation,work resumption becomes an urgent task.However,with the spread of the epidemic in Italy,Spain and other European countries,as well as Iran,South Korea,and United States,we are reminded once again that the spread of the virus is extremely powerful,and the prevention and control of the epidemic is still urgent.We first adopted a mathematical model based on the confirmed case data from January 25 to February 23,analyzed and predicted the epidemic trends and key time points in domestic provinces and major cities.A comparative analysis of those predictions and the status of the epidemic on March 18 proved that this forecast is in line with the trend of the epidemic situation and is capable to make predictions about the future epidemic situation.Therefore,based on the domestic and foreign epidemic data released in real time,we established an early warning system for the epidemic COVID-19,showing the regional spreading trends of the global epidemic from multiple aspects and monitoring of possible regional pandemic trends in real-time.