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The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model
Amir Jamshidnezhad1, Seyed Ahmad Hosseini2, Leila Ibrahimi Ghavamabadi3
1Department of Health Information Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Background:
In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation.
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.
Method:
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.
Results:
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.
Conclusion:
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.
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