A web-based surveillance model of eosinophilic meningitis: future prediction and distribution patterns

Abstract

Background: web-based surveillance is a useful tool for predicting future cases of various emerging infectious diseases. There are limited data available on web-based surveillance and patterns of distribution of eosinophilic meningitis (EOM), which is an emerging infectious disease in various countries around the world. 

Methods: this study applied web-based surveillance to the prediction of EOM incidence and the analysis of its distribution pattern by using a national database, which may be used for future prevention and control. The number cases of EOM in each month over a period of 12 years (between 2006 to 2017) from Loei province were retrieved from the National Disease Surveillance (Report 506) website, operated by Thailand's Public Health Center. 

Results: we developed autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) models. The best model was used for predicting numbers of future cases. The forecast values from the SARIMA (1, 1, 2)(0,1,1)6 model were close to actual values and were the most valid, as they had the lowest RMSE and AIC. The predictive model for future cases of EOM was related to previous numbers of EOM cases over the past eight months. The disease exhibited a seasonal pattern during the study period. 

Conclusions: web-based surveillance can be used for future prediction of EOM, that the predictive model applied here was valid, and that EOM exhibits a seasonal pattern.