Forecasting Scrub Typhus Cases in Eight High-Risk Counties in China: Evaluation of Time-Series Model Performance - He, Junyu; Wei, Xianyu; Yin, Wenwu; Wang, Yong; Qian, Quan; Sun, Hailong; Xu, Yuanyong; Soares Magalhaes, Ricardo J.; Guo, Yuming; Zhang, Wenyi (Frontiers in Environmental Science, 2022)
Scrub Typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modelling approaches at forecasting ST cases during 2012-2020 in eight high risk counties in China. We evaluated the performance of a seasonal autoregressive integrated moving average model (SARIMA), a SARIMA model with exogenous variables (SARIMAX), and the long-short term memory model (LSTM) to depict temporal variations in ST cases. In our investigation we considered eight environmental variables know to be associated with ST landscape epidemiology (including normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, multivariate El Niño/Southern Oscillation index, MEI). The first 8-years data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, indicating that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high risk areas under investigation. Further, the introduction of time-series models, especially LSTM, have enriched the ability of local public health authorities in ST high risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system, forecasting the ST precisely.