Deep Learning-Based Prediction of Daily Covid-19 Cases Using X (Twitter) Data Publisher Pubmed



Ahmed N¹ ; Saeed K¹ ; Rodrigues JL¹ ; Naeem M¹ ; Correa A¹ ; Sanabboon C¹ ; Rostam Niakan Kalhori S^{2, 3} ; Deserno TM²
Source: Studies in Health Technology and Informatics Published:2024

Abstract

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. © 2024 The Authors.