Univariate and Multivariate Long Short Term Memory (LSTM) Model to Predict Covid-19 Cases in Malaysia Using Integrated Meteorological Data

Abstract

The rate of transmission of coronavirus disease (COVID-19) has been very fast since the first reported case in December 2019 in Wuhan, China. The disease has infected more than 3 million people worldwide and resulted in more than 224 thousand deaths as of May 1, 2020, reported by The World Health Organization (WHO). In the past, meteorological parameters such as temperature and humidity were essential and effective factors against serious infectious diseases such as influenza and Severe Acute Respiratory Syndrome (SARS). Therefore, exploring the relationship between meteorological factors and active COVID-19 cases is essential. This study employs the long-short term memory (LSTM) method to predict Covid-19 Cases in Malaysia. We propose a univariate and multivariate model using Covid-19 cases and meteorology data. The univariate LSTM model uses Covid-19 active cases data in a year as a control attribute for model development. The multivariate LSTM model uses the integrated Covid-19 cases, and meteorology data consists of attributes: minimum, maximum, and average values of Humidity, Temperature, Windspeed, and Pressure from 13 states of Malaysia. The model's performance is evaluated using errors such as MAE, RMSE, MAPE, and the R2 Score. The low errors and higher R2 score indicate the model's excellent performance. We observed that the univariate LSTM model gives the least error in five states, indicating that those states' daily active cases are the main contributing factors. In the multivariate LSTM model, the daily cases and humidity, temperature, and windspeed are the main factors in several different states. The result of the study is to help the government to prevent and manage the spread of the COVID-19 and other upcoming pandemic better.