Prediction of COVID-19 cases using the weather integrated deep learning
approach for India
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.