欧洲COVID-19感染与天气因素的估计和人口分析

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Business Analytics Pub Date : 2020-07-02 DOI:10.1080/2573234x.2020.1832866

Reza Gharoie Ahangar, R. Pavur, Mahdis Fathi, A. Shaik

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引用次数: 8

摘要

本研究的主要目的是调查欧洲人口最多和工业化国家的COVID-19与天气因素之间的关系，并提出预测COVID-19日病例数的最佳数学模型。为了找出COVID-19与西班牙、法国、意大利、德国和英国的绝对湿度和温度等天气因素之间的关系，我们进行了泊松分析。我们还使用通用线性神经网络(GRNN)模型预测了这些欧洲国家每日COVID-19病例的趋势和数量。结果显示，新冠肺炎感染人数与气温、绝对湿度等天气因素呈显著负相关。此外，研究结果表明，COVID-19与绝对湿度的负相关关系强于温度。在我们提出的GRNN方法中，我们发现相对于本研究中其他欧洲国家，意大利的COVID-19病例具有更好的兼容性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

ABSTRACT The main objective of this study is to investigate the relationship between the COVID-19 and the weather factors of the most populated and industrialised countries in Europe and propose the best mathematical model to forecast the daily number of COVID-19 cases. To find the relationship between the COVID-19 and the weather factors of absolute humidity and temperature in Spain, France, Italy, Germany, and the United Kingdom, we conducted a Poisson analysis. We also used the General Linear Neural Network (GRNN) model to forecast the trend and number of daily COVID-19 cases in these European countries. The results reveal a statistically significant negative relationship between the number of COVID-19 infections and weather factors of temperature & absolute humidity. Furthermore, the results show a stronger negative relationship between COVID-19 and absolute humidity than temperature. In our proposed GRNN method, we find better compatibility for the COVID-19 cases in Italy relative to the other European countries in this study.

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来源期刊

Journal of Business Analytics Business, Management and Accounting-Management Information Systems

CiteScore

2.50

自引率

0.00%

发文量