文章基本信息

标题：Effectiveness of the Early Response to COVID-19: Data Analysis and Modelling
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作者：Edoardo Bertone ; Martin Jason Luna Juncal ; Rafaela Keiko Prado Umeno 等
期刊名称：Systems
电子版ISSN：2079-8954
出版年度：2020
卷号：8
期号：2
页码：21-37
DOI：10.3390/systems8020021
出版社：MDPI Publishing
摘要：Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand/model potential second waves of contagions.
关键词：Bayesian Networks; COVID-19; pandemic; system thinking Bayesian Networks ; COVID-19 ; pandemic ; system thinking