Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models
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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5520663 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849404781290848256 |
|---|---|
| author | Yogesh Gupta Ghanshyam Raghuwanshi Abdullah Ali H. Ahmadini Utkarsh Sharma Amit Kumar Mishra Wali Khan Mashwani Pinar Goktas Shokrya S. Alshqaq Oluwafemi Samson Balogun |
| author_facet | Yogesh Gupta Ghanshyam Raghuwanshi Abdullah Ali H. Ahmadini Utkarsh Sharma Amit Kumar Mishra Wali Khan Mashwani Pinar Goktas Shokrya S. Alshqaq Oluwafemi Samson Balogun |
| author_sort | Yogesh Gupta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a71f71949694868b0fd64f4672d855f |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9a71f71949694868b0fd64f4672d855f2025-08-20T03:36:53ZengWileyComplexity1099-05262021-01-01202110.1155/2021/5520663Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning ModelsYogesh Gupta0Ghanshyam Raghuwanshi1Abdullah Ali H. Ahmadini2Utkarsh Sharma3Amit Kumar Mishra4Wali Khan Mashwani5Pinar Goktas6Shokrya S. Alshqaq7Oluwafemi Samson Balogun8Department of Computer Science and EngineeringDepartment of Computer and Communication EngineeringDepartment of MathematicsDepartment of Computer Science and EngineeringSchool of ComputingInstitute of Numerical SciencesDepartment of EconomicsDepartment of EconomicsSchool of ComputingNowadays, 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.http://dx.doi.org/10.1155/2021/5520663 |
| spellingShingle | Yogesh Gupta Ghanshyam Raghuwanshi Abdullah Ali H. Ahmadini Utkarsh Sharma Amit Kumar Mishra Wali Khan Mashwani Pinar Goktas Shokrya S. Alshqaq Oluwafemi Samson Balogun Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models Complexity |
| title | Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models |
| title_full | Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models |
| title_fullStr | Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models |
| title_full_unstemmed | Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models |
| title_short | Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models |
| title_sort | impact of weather predictions on covid 19 infection rate by using deep learning models |
| url | http://dx.doi.org/10.1155/2021/5520663 |
| work_keys_str_mv | AT yogeshgupta impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT ghanshyamraghuwanshi impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT abdullahalihahmadini impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT utkarshsharma impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT amitkumarmishra impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT walikhanmashwani impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT pinargoktas impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT shokryasalshqaq impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels AT oluwafemisamsonbalogun impactofweatherpredictionsoncovid19infectionratebyusingdeeplearningmodels |