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.

Keywords: COVID-19; India; LSTM; SARS-CoV-2; prediction; specific humidity; temperature.

FIGURE 1

Keras implementation of multi‐parameter LSTM (a) The basic LSTM structure (b) Unrolled representation of LSTM (c) Architecture of an LSTM cell (d) Internal structure of a cell gate

FIGURE 2

Spatial maps of monthly cumulated COVID‐19 cases over different states in India during pre‐monsoon (April and May) and monsoon season (June and July) of the year 2020

FIGURE 3

Spatial‐temporal variation of surface meteorological parameters (2m‐specific humidity, 2m‐mean temperature, 2m‐maximum temperature, and 2m‐minimum temperature) during the pre‐monsoon and monsoon season over India

FIGURE 4

Correlation between confirmed COVID‐19 cases and meteorological parameters (2m‐specific humidity, 2m‐mean temperature, 2m‐maximum temperature, and 2m‐minimum temperature) during the period 01 April to 31 July 2020

FIGURE 5

Skill (Average relative error) of univariate (CTL) and multivariate (CTL_SH, CTL_Tmax, CTL_Tmin, CTL_Tmean) LSTM models during the test period (1 July to 31 July 2020) for the states of Andhra Pradesh, Karnataka, Delhi, Bihar, Odisha and Uttar Pradesh. Where L1 to L14 represents the 1 to 14 days of lag data utilized for forecasting of the next day COVID‐19 cases

FIGURE 6

Time series data of COVID‐19 cases forecasted by univariate (CTL) and multivariate (CTL_SH, CTL_Tmax, CTL_Tmin, CTL_Tmean) LSTM models during the test period (1 July to 31 July 2020) for the states of Andhra Pradesh, Karnataka, Delhi, Bihar, Odisha and Uttar Pradesh

FIGURE 7

Skill (Average relative error) of univariate (CTL) and multivariate (CTL_SH, CTL_Tmax, CTL_Tmin, CTL_Tmean) LSTM models during the test period (1 July to 31 July 2020) for the states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Haryana and Punjab. Where L1 to L14 represents the 1 to 14 days of lag data utilized for forecasting of the next day COVID‐19 cases

FIGURE 8

Time series data of COVID‐19 cases forecasted by univariate (CTL) and multivariate (CTL_SH, CTL_Tmax, CTL_Tmin, and CTL_Tmean) LSTM models during the test period (1 July to 31 July 2020) for the states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Haryana and Punjab

FIGURE 9

Skill (Average relative error) of univariate (CTL) and multivariate (CTL_SH, CTL_Tmax, CTL_Tmin, CTL_Tmean) LSTM models during the test period (1 July to 31 July 2020) for the states of Tamil Nadu, West Bengal, and Kerala. Where L1 to L14 represents the 1 to 14 days of lag data utilized for forecasting of the next day COVID‐19 cases

FIGURE 10

Spatial‐temporal variation of potential evaporation rate (W/m²) during pre‐monsoon and monsoon season over India for the year 2020