Open AccessThe role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model
Author(s) -
Amir Jamshidnezhad,
Seyed Ahmad Hosseini,
Leila Ibrahimi Ghavamabadi,
Seyed Mahdi Marashi,
Hediye Mousavi,
Marzieh Zilae,
Behzad Fouladi Dehaghi
Publication year - 2021
Publication title -
work
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.5
H-Index - 50
eISSN - 1875-9270
pISSN - 1051-9815
DOI - 10.3233/wor-210463
Subject(s) - covid-19 , outbreak , transmission (telecommunications) , air temperature , meteorology , artificial neural network , environmental science , air conditioning , statistics , geography , simulation , demography , computer science , machine learning , mathematics , medicine , engineering , telecommunications , disease , virology , infectious disease (medical specialty) , mechanical engineering , sociology , pathology
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.