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Prediction of COVID-19 cases using the weather integrated deep learning approach for India
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  • Kantha Rao Bhimala,
  • GOPAL KRISHNA PATRA,
  • Rajashekar Mopuri,
  • Srinivasa Rao Mutheneni
Kantha Rao Bhimala
CSIR Fourth Paradigm Institute
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GOPAL KRISHNA PATRA
CSIR Fourth Paradigm Institute
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Rajashekar Mopuri
Indian Institute of Chemical Technology CSIR
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Srinivasa Rao Mutheneni
Indian Institute of Chemical Technology CSIR
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Abstract

Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved its capability in time series forecasting of the non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed and also developed a forecasting model using long short term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature and positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed cases data (1st April-30th June 2020) was used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the COVID-19 cases for the period 1st July 2020 to 31st July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short term (1day lead) forecast of COVID-19 cases (relative error < 20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7days) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.

Peer review status:Published

10 Nov 2020Submitted to Transboundary and Emerging Diseases
10 Nov 2020Submission Checks Completed
10 Nov 2020Assigned to Editor
13 Nov 2020Reviewer(s) Assigned
26 Feb 2021Review(s) Completed, Editorial Evaluation Pending
06 Mar 2021Editorial Decision: Revise Major
25 Mar 20211st Revision Received
25 Mar 2021Assigned to Editor
25 Mar 2021Submission Checks Completed
26 Mar 2021Reviewer(s) Assigned
26 Mar 2021Review(s) Completed, Editorial Evaluation Pending
28 Mar 2021Editorial Decision: Revise Minor
31 Mar 20212nd Revision Received
31 Mar 2021Submission Checks Completed
31 Mar 2021Assigned to Editor
31 Mar 2021Reviewer(s) Assigned
03 Apr 2021Review(s) Completed, Editorial Evaluation Pending
04 Apr 2021Editorial Decision: Accept
10 Apr 2021Published in Transboundary and Emerging Diseases. 10.1111/tbed.14102