Developing a dengue forecast model using Long Short Term Memory neural networks method

Abstract

BackgroundDengue Fever (DF) is a tropical mosquito-borne disease that threatens public health and causes enormous economic burdens worldwide. In China, DF expanded from coastal region to inner land, and the incidence sharply increased in the last few years. In this study, we conduct the analysis of dengue using the Long Short Term Memory (LSTM) recurrent neural networks. This is an artificial intelligence technology, to develop a precise dengue forecast model.\n\nMethodology/Principal FindingsThe model is developed from monthly dengue cases and local meteorological data of 2005-2018 among top 20 Chinese cities with a record of the highest dengue incidence. The first 13 year data were used to construct the LSTM and to predict the dengue outbreaks in 2018. The results are compared with the estimated dengue cases of other previously published models. Model performance and prediction accuracy were assessed using Root Mean Square Error (RMSE). With the LSTM method, the prediction measurements of average RMSE drop by 54.79% and 34.76% as compared with the Susceptible Infected Recovered (SIR) model and Zero Inflated Generalized Additive Model (ZIGAM). Our results showed that if only local data were used to develop forecast models, the LSTM neural networks would fail to capture the transmission characteristics of dengue virus in areas with fewer dengue cases. Contrarily, transfer learning (TL) can improve the accuracy of prediction of the LSTM neural network model in areas with fewer dengue incidences.\n\nConclusion and significanceThe LSTM model is beneficial in predicting dengue incidence as compared with other previously published forecasting models. The findings provide a more precise forecast dengue model, which can help the local government and health-related departments respond early to dengue epidemics.\n\nAuthor summaryIn China, DF is a public health concern that poses a great economic burden on local governments. However, the incidence has sharply increased in recent years with growth in the sub-regions. With this issue, it will be challenging to develop an accurate and timely dengue forecast model. LSTM recurrent neural networks, deep learning methods and virus propagation rules by learning from observational data offer more advantages in predicting the prevalence of infectious disease dynamics than the traditional statistical model. The 2005-2017 data of the top 20 Chinese cities with the highest dengue incidence were used to construct the LSTM model, advantageous in predicting dengue in most cities. Moreover, the model helped to predict the dengue outbreaks in 2018 and used to compare the estimated dengue cases with the RMSE results of other previously published models. A thorough search of the literature shows that this is the first established dengue forecast model using the LSTM method, which is effective in predicting the trend of dengue dynamics.