来源:Global Health Research and Policy 发布时间:2020/3/11 10:42:21
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中国新型冠状病毒COVID-19疫情爆发起初两月追踪:基于二阶微分模型的实时监测与评估 | BMC Journal

论文标题:First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model

期刊:Global Health Research and Policy

作者:Xinguang Chen,Bin Yu et.al

发表时间:2020/03/02

数字识别码:10.1186/s41256-020-00137-4

微信链接:点击此处阅读微信文章

2020年3月2日,佛罗里达大学流行病学系陈心广教授(杂志主编、武汉大学讲座教授)和俞斌博士在武汉大学《全球健康研究与政策》英文杂志(Global Health Research and Policy)正式发表了《中国新型冠状病毒COVID-19疫情爆发起初两月追踪:基于二阶微分模型的实时监测与评估》一文。陈教授团队采用二阶微分模型,利用2019年12月8日到2020年2月8日每日累计确诊病例数,对疫情进行实时监测,同时评估全国上下干预措施的有效性。陈教授和俞博士研究发现,COVID-19疫情的发生发展具有典型的非线性和混沌灾变特征,无法提前准确预测在什么地方、什么时候、什么人群中发生,关键的是当感染发生后,及时监控,一旦爆发流行确定后, 针对疫情的发生发展,及时采取控制传染源,阻断传播途径和保护易感人群等措施,控制疫情的进一步发展。

图1

本研究采用的二阶微分模型,能够帮助行政和公共卫生决策人员根据每天报道的累计发病人数,实时监测疫情的发生发展,支持优化决策和采取有效干预措施,评估措施的有效性,预测未来疫情的发展变化。模型的结果表明COVID-19疫情的发展很容易受到干预措施的影响,当大规模干预于1月21日开始后,模型立刻就观测到干预的效果,并且提前准确预计,每日新增病例会在一个潜伏期,即14天后,也就是2月4日到达高峰后随之下降。当2月5日发现新增病例开始下降时,模型准确预测, 14天(即一个潜伏期)后,新增病例在2月18日后会出现显著下降。而目前的实际情况也反映了研究模型的有效性。

研究人员特别指出,类似其他传染病如鼠疫、SARS、禽流感,COVID-19的发生、发展和转归是无法事先确定的。它可以发生在任何国家、任何地方、任何人群、任何时候。这类传染病对干预措施非常敏感。因此,任何有利于控制传染源,阻断传播途径和保护易感人群的措施都是有效的。本研究的结果为决策人员、公共卫生和临床专业人员和全社会一起来评估总结这次COVID-19的防控效果提供了重要依据,也为未来传染病防控提供了一个全新的行之有效的监测评估工具。

摘要:

Background

Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively.

Methods

In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases.

Results

Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020.

Conclusions

The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.

(来源:科学网)

 
 
 
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