调查法国COVID-19数据的强有力现象学方法

IF 0.4 Q4 MATHEMATICS, APPLIED Mathematics in applied sciences and engineering Pub Date : 2021-02-12 DOI:10.1101/2021.02.10.21251500
Q. Griette, J. Demongeot, P. Magal
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引用次数: 20

摘要

本文提出了一种新的分析COVID-19累计报告病例数据的方法,该方法基于两步过程:首先使用考虑时间段地方性或流行病性质的现象学模型对数据进行正则化,然后使用精确再现流行病的数学模型。这使我们能够获得关于流行病参数的新信息,并每天计算有效基本生殖比率。我们的方法的优点是确定了新感染病例数量的稳健趋势,并产生了非常顺利的流行病重建。该方法所需的参数数量非常少:对于2020年2月至2021年1月期间的法国疫情,我们总共只使用了11个参数。
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A robust phenomenological approach to investigate COVID-19 data for France
We provide a new method to analyze the COVID-19 cumulative reported cases data based on a two-step process: first we regularize the data by using a phenomenological model which takes into account the endemic or epidemic nature of the time period, then we use a mathematical model which reproduces the epidemic exactly. This allows us to derive new information on the epidemic parameters and to compute the effective basic reproductive ratio on a daily basis. Our method has the advantage of identifying robust trends in the number of new infectious cases and produces an extremely smooth reconstruction of the epidemic. The number of parameters required by the method is parsimonious: for the French epidemic between February 2020 and January 2021 we use only 11 parameters in total.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
0
审稿时长
21 weeks
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