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.

气象参数是过去传染病的关键和有效因素，如流感和严重急性呼吸系统综合症 (SARS) 等。本研究旨在探索 2019 年冠状病毒病 (COVID-19) 传播率与气象参数之间的关系。为此，对印度不同州 2020 年 3 月 28 日至 2020 年 4 月 22 日的气象参数和 COVID-19 感染数据进行了汇编和分析。已实施梯度提升模型 (GBM)，以探索最低温度、最高温度、最低湿度和最高湿度对 COVID-19 感染数的影响。GBM模型在调整参数后达到了最优性能。GBM 导致 R ^{2的最佳精度} = 0.95 用于预测马哈拉施特拉邦的活跃病例，R ²  = 0.98 用于预测印度喀拉拉邦和拉贾斯坦邦的 COVID-19 康复病例。