Rabies Outbreak Prediction Using Deep Learning with Long Short-Term Memory

Abstract

The purpose of this article is to evaluate the Long Short-Term Memory (LSTM) model performance for rabies outbreak prediction (ROP). Successful forecasting of the initial epidemic outbreaks can decrease the incidence of the ailment and save lives, but this type of research is costly, and an erroneous result can trigger false alarms, and the trustworthiness of the warning system will be at stake. As such, biosurveillance system developers are looking for highly sensitive outbreak prediction algorithms that will minimise the number of false alarms. Using the epidemiological data such as those of rabies to forecast novel and vital directions is a significant issue of public health, and it involves the collective attention of the machine learning (ML) communities. In this study, the data are obtained from HealthData.com and utilised for the performance evaluation of the LSTM algorithm. The algorithm performance is evaluated based on Root Mean Square Error (RMSE) and Accuracy, and compared with that of the traditional algorithm– the Autoregressive integrated moving average (ARIMA) model. The results from this research prove that a deep learning LSTM network can predict the disease prevalence, using the rabies datasets, with a good accuracy. The performance of the proposed model is evaluated by comparing with the ARIMA model. The LSTM model attains the best result with 97.10% accuracy, while the traditional ARIMA obtains 72.10%. Moreover, the LSTM model scores the lowest value of RMSE (2.04) compared with the ARIMA model which scores the highest (3.12). Through this study, it is obvious that the LSTM prediction model is an effective method for determining this viral disease, evidenced by a very low RMSE value and a high accuracy score.