Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models

Yogesh Gupta, Ghanshyam Raghuwanshi, Abdullah Ali H. Ahmadini, Utkarsh Sharma, Amit Kumar Mishra, Wali Khan Mashwani, Pinar Goktas, Shokrya S. Alshqaq, Oluwafemi Samson Balogun and Hiroki Sayama

Complexity, 2021, vol. 2021, 1-11

Abstract: Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5520663

DOI: 10.1155/2021/5520663

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