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 their capability in time series forecasting 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 a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 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 (1 day lead) forecast of COVID‐19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium‐range forecast skill (1–7 days lead) 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.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic

“…The model uses recursive Long Short-Term Memory (LSTM) networks to adapt to the nonlinearity of a given COVID-19 data set, which can overcome the limitations of traditional time series prediction techniques and produce the latest results on time data. Bhimala et al ( Bhimala and Patra, 2021 ) assessed the relationship between weather factors and COVID-19 cases, and established a predictive model using deep learning model LSTM. The results show that the multivariate LSTM model based on temperature time series data performs well in the high humidity regions of Kerala, Tamil Nadu and West Bengal.…”

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

“…Different models have been used to predict COVID-19 prevalence and mortality rate in recent studies. For example, multiple linear regression [ 8 ], Artificial Neural Network [ 9 ], multilayer perceptron [ 10 ] grey prediction model [ 11 ], simulation model [ 12 ], Holt model [ 13 ], LSTM model [ 14 ], and support vector regression [ 15 , 16 ]. However, the spread of epidemic disease is random and will be affected by many factors [ 17 , 18 ].…”

Predicting COVID-19 future trends for different European countries using Pearson correlation

“…Researchers developed a simple ARIMA model to estimate how many people would be infected and recover from SARS-COV2 once lockdown measures in Italy were eased (Chintalapudi et al 2020 ). They used a variety of techniques to make their predictions, such as relevant Google trends of particular search terms that were associated with the COVID-19 pandemic (Prasanth et al 2021 ), in addition to artificial intelligence (AI) techniques for nonlinear time series prediction problems (Bhimala et al 2021 ).…”