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\begin{document}

\title{Prediction of COVID-19 cases using the weather integrated deep learning
approach for India}



\author[1]{Kantha Rao   Bhimala}%
\author[1]{GOPAL KRISHNA PATRA}%
\author[2]{Rajashekar  Mopuri}%
\author[2]{Srinivasa Rao Mutheneni}%
\affil[1]{CSIR Fourth Paradigm Institute }%
\affil[2]{Indian Institute of Chemical Technology CSIR}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{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 \textless{} 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.%
\end{abstract}%



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