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. 2021;51(5):2727-2739.
doi: 10.1007/s10489-020-01997-6. Epub 2020 Nov 4.

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India

Lokesh Kumar Shrivastav  1   2 , Sunil Kumar Jha  3   4
Affiliations

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India

Lokesh Kumar Shrivastav et al. Appl Intell (Dordr). 2021.

Abstract

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS), etc. The present study targets to explore the association between the coronavirus disease 2019 (COVID-19) transmission rates and meteorological parameters. For this purpose, the meteorological parameters and COVID-19 infection data from 28th March 2020 to 22nd April 2020 of different states of India have been compiled and used in the analysis. The gradient boosting model (GBM) has been implemented to explore the effect of the minimum temperature, maximum temperature, minimum humidity, and maximum humidity on the infection count of COVID-19. The optimal performance of the GBM model has been achieved after tuning its parameters. The GBM results in the best accuracy of R2 = 0.95 for prediction of active cases in Maharashtra, and R2 = 0.98 for prediction of recovered cases of COVID-19 in Kerala and Rajasthan, India.

Keywords: Artificial intelligence; Atmospheric factors; COVID-19; Gradient boosting model; Predictive modeling.

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Conflict of interest statement

Conflicts of interest/competing interestsAuthors declares no conflict of interest.

Figures

Fig. 1
Fig. 1
The variation in the minimum and the maximum temperature
Fig. 2
Fig. 2
The variation in the minimum and the maximum humidity
Fig. 3
Fig. 3
The variation in the active and recovered cases of COVID-19
Fig. 4
Fig. 4
Performance of GBM in the prediction of active cases of COVID-19 using the combined dataset of all states of India
Fig. 5
Fig. 5
Performance of GBM in the prediction of recovered cases of COVID-19 using the combined dataset of all states of India
Fig. 6
Fig. 6
Performance of GBM in the prediction of active cases of COVID-19 of Delhi, India
Fig. 7
Fig. 7
Performance of GBM in the prediction of recovered cases of COVID-19 of Delhi, India
Fig. 8
Fig. 8
Performance of GBM in the prediction of active cases of COVID-19 of Maharashtra, India
Fig. 9
Fig. 9
Performance of GBM in the prediction of recovered cases of COVID-19 of Maharashtra, India
Fig. 10
Fig. 10
Performance of GBM in the prediction of active cases of COVID-19 of Gujarat, India
Fig. 11
Fig. 11
Performance of GBM in the prediction of recovered cases of COVID-19 of Gujarat, India
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