The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model.

Amir Jamshidnezhad,Hediye Mousavi,Leila Ibrahimi Ghavamabadi,Behzad Fouladi Dehaghi,Marzieh Zilae,Seyed Mahdi Hossaeini Marashi,Seyed Ahmad Hosseini

doi:10.3233/wor-210463

Abstract

In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

Highlights

The COVID-19 pandemic is originated from a type of beta-coronavirus called SARS-CoV-2 and was first identified in Wuhan, China
According to a study by Poirier et al, temperature and humidity alone cannot indicate the exact trend of coronavirus outbreaks, and further studies are recommended to investigate the effects of environmental factors on coronavirus outbreaks [20]
In a similar study by Pirouz et al, the results of the multiple linear regression model showed that the frequency of positive cases of COVID-19 was decreased in Wuhan 14 days after quarantine, and fluctuations in increasing frequency of positive cases may be influenced by environmental factors [19]

Summary

Introduction

The COVID-19 pandemic is originated from a type of beta-coronavirus called SARS-CoV-2 and was first identified in Wuhan, China. Some recent studies have declared a relationship between changes in ambient temperature/relative humidity and reported positive COVID-19 cases [9,10,11]. The purpose of this study is to provide a statistical analysis to assess the relationship between temperature conditions and the number of cases of COVID-19 in Iran. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. This study shows the role of closed-air cycle condition in the indoor environment of tropical cities

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Discussion

Conclusion