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利用人工神经网络算法对 COVID-19 发病率和死亡率进行时空建模。

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms.

机构信息

Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.

Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.

出版信息

Spat Spatiotemporal Epidemiol. 2022 Feb;40:100471. doi: 10.1016/j.sste.2021.100471. Epub 2021 Nov 11.

DOI:10.1016/j.sste.2021.100471
PMID:35120681
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8580864/
Abstract

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

摘要

冠状病毒病（COVID-19）的爆发已成为近年来全球最具挑战性的问题之一。由于全球范围内对 COVID-19 的时空建模研究不足，本研究旨在使用多层感知器人工神经网络拓扑结构，研究与 COVID-19 流行率和死亡率相关的潜在解释变量（n=75）的相对重要性。我们利用十种变量重要性分析方法来确定解释变量的相对重要性。主要发现表明，在所有时期，有几个变量一直是最具影响力的变量之一。就 COVID-19 的流行率而言，失业和人口密度是最重要的变量，具有最高的重要性得分。而对于 COVID-19 的死亡率，与健康相关的变量，如糖尿病患病率和医院床位数量，则是最重要的变量之一。本研究的结果可能为公共卫生政策制定者提供一般性的见解，以监测疾病的传播并支持决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/1ee4643d85ff/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/7532adc3cfa1/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/041aba4e311e/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/abd112a3081d/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/fc29cad6c4b2/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/5b18c28ea67b/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/163625f5041f/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/4872ef9ceb51/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/91569824d934/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/031705001d19/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/b5eb73aedab0/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/1ee4643d85ff/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/7532adc3cfa1/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/041aba4e311e/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/abd112a3081d/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/fc29cad6c4b2/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/5b18c28ea67b/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/163625f5041f/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/4872ef9ceb51/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/91569824d934/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/031705001d19/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/b5eb73aedab0/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b85/8580864/1ee4643d85ff/gr11_lrg.jpg

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本文引用的文献

[1]
An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science.

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Sustain Cities Soc. 2021-2

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Impact of population density on Covid-19 infected and mortality rate in India.

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