A region-specific LSTM model using meteorological factors identified by GeoDetector accurately predicted weekly hand-foot-mouth disease cases in Guangxi, China, with a mean R-square of 0.60.
Observational (n=170,920)
A region-specific LSTM model combined with GeoDetector successfully predicted hand-foot-mouth disease incidence based on meteorological factors in Guangxi, China.
Hand-foot-mouth disease (HFMD) is a common infectious disease in children and is particularly severe in Guangxi, China. Meteorological conditions are known to play a pivotal role in the HFMD. Previous studies have reported numerous models to predict the incidence of HFMD. In this study, we proposed a new method for the HFMD prediction using GeoDetector and a Long Short-Term Memory neural network (LSTM). The daily meteorological factors and HFMD records in Guangxi during 2014-2015 were adopted. First, potential risk factors for the occurrence of HFMD were identified based on the GeoDetector. Then, region-specific prediction models were developed in 14 administrative regions of Guangxi, China using an optimized three-layer LSTM model. Prediction results (the R-square ranges from 0.39 to 0.71) showed that the model proposed in this study had a good performance in HFMD predictions. This model could provide support for the prevention and control of HFMD. Moreover, this model could also be extended to the time series prediction of other infectious diseases.
Gu et al. (Fri,) conducted a observational in Hand-foot-mouth disease (HFMD) (n=170,920). GeoDetector and Long Short-Term Memory (LSTM) neural network model was evaluated on Prediction accuracy (Mean R-square). A region-specific LSTM model using meteorological factors identified by GeoDetector accurately predicted weekly hand-foot-mouth disease cases in Guangxi, China, with a mean R-square of 0.60.