Association of Environmental Parameters with COVID-19 in Delhi, India

Abstract

The present study explores the association between weather and COVID-19 pandemic in Delhi, India. The study used the data from daily newspaper releases from the Ministry of Health and Family Welfare, Government of India. Linear regression was run to understand the effect of the number of tests, temperature, and relative humidity on the number of COVID-19 cases in Delhi. The model was significantly able to predict number of COVID-19 cases, F (4,56) = 1213.61, p \ 0.05, accounting for 99.4% of the variation in COVID-19 cases with adjusted R 2 = 98.8%. Maximum Temperature, average temperature and average relative humidity did not show statistical significance. The only number of tests was significantly associated with COVID-19 cases.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID‐19) in a hot, arid climate

“…This study indicates that SARS‐CoV‐2 infection was positively correlated with relative humidity during the period of free movement and negatively correlated with relative humidity during the partial and full lockdown. This result in the present study is congruent with other studies by Kumar and Kumar ( 2020 ), Mousavi et al ( 2020 ), Liu et al ( 2020 ), Sing et al ( 2020 ), Neto and Melo ( 2020 ), and Ladha et al ( 2020 ). Moreover, Şahin ( 2020 ) reported that wind speed is positively correlated with the number of COVID‐19 cases, which was consistent with our results for full lockdown.…”

“…(2020), and Ladha et al (2020) investigated the association between climatic conditions and COVID-19 transmission in different states of India ( e.g., Mumbai, Delhi, Tamil Nadu, Maharashtra, and Gujarat), using the artificial Neural Network (ANN) technique, Spearman rank correlation model, Mann-Kendall method, Karl Pearson's correlation analysis, and Sen's slope estimator. The results found that relative humidity and pressure parameters are more active than the other parameters on the COVID-19 pandemic.…”

“…Asyary and Veruswati ( 2020 ) demonstrated that exposure to sunlight was significantly correlated with patients' recovery from COVID‐19 in Jakarta, Indonesia. Some studies in India by Kumar and Kumar ( 2020 ), Mousavi et al ( 2020 ), Liu et al ( 2020 ), Singh et al ( 2020 ), and Ladha et al ( 2020 ) investigated the association between climatic conditions and COVID‐19 transmission in different states of India (e.g., Mumbai, Delhi, Tamil Nadu, Maharashtra, and Gujarat), using the artificial neural network technique, Spearman's rank correlation model, Mann–Kendall method, Karl Pearson's correlation analysis, and Sen's slope estimator. The results found that relative humidity and pressure parameters are more active than the other parameters on the COVID‐19 pandemic.…”

A new hybrid prediction model of cumulative COVID-19 confirmed data

“…Specifically, the single model can be subdivided into statistical theoretical model and artificial intelligence model. Statistical theoretical model mainly includes the logistic regression model ( Ibrahim and Al-Najafi, 2020 ), linear regression model ( Ladha et al, 2020 ), and multiple linear regression model ( Rath et al, 2020 ), etc. The above statistical theoretical model has the advantages such as fast training speed, few reference factors, simple and easy to understand.…”