Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi.

Meteorological parameters show a strong influence on disease transmission in urban localities. The combined influence of factors such as daily mean temperature, absolute humidity and average wind speed on the attack rate and mortality rate of COVID-19 rise in Delhi, India has been investigated in this case study. A Random forest regression algorithm has been utilized to compare the epidemiological and meteorological parameters. The performance of the model has been evaluated using statistical performance metrics. The random forest model shows a strong positive correlation between the predictor parameters on the attack rate (96.09%) and mortality rate (93.85%). On both the response variables, absolute humidity has been noted to be the variable of highest influence. In addition, both temperature and wind speed have shown moderate positive influence on the transmission and survival of coronavirus during the study period. The synergistic effect of absolute humidity with temperature and wind speed contributing towards the increase in the attack and mortality rate has been addressed. The inhibition to respiratory droplet evaporation, increment in droplet size due to hygroscopic effect and the enhanced duration of survival of coronavirus borne in respiratory droplets are attributed to the increase in coronavirus infection under the observed weather conditions. [Display omitted] • Combined effect of urban climatic parameters on COVID-19 dissemination. • Random forest regression model built to understand the synergistic effect. • Increase in absolute humidity aids in increase of COVID-19 transmission in Delhi. • Inhibition to respiratory droplet evaporation attributed high humidity. • High correlation coefficient (>92%) achieved in the chosen machine learning model. [ABSTRACT FROM AUTHOR]