Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models

Abstract

Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as and respectively. SARIMA (0,1,7) × (1,0,1) 12 was selected among a collection of constructed models for high R 2 and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

“…Therefore, this novel data-driven hybrid technique can be recommended as an effective and valuable tool in analyzing and estimating the temporal trends of the TB incidence in Tibet. Moreover, although the SARIMA model, NARNN model, ETS model, and SARIMA-NARNN mixture model produced a relatively low predictive performance relative to our proposed hybrid technique, they are also shown to have a good potential to model the TB epidemics because they gave a MAPE value less than 20% both in the mimic and predictive aspects, this is in good agreement with prior researches that used the above common models to analyze the temporal patterns of other infectious diseases (eg COVID-19, 39 typhoid and paratyphoid fevers, 40 schistosomiasis, 41 and hand-foot-mouth disease 42 ). Also, our proposed hybrid model can be used to estimate the current intervention effects for TB, if this model estimates a significantly higher incidence than the actual, meaning that the current interventions play an important role.…”

“… 43 Gao et al observed that the SARIMA(0,1,7)(1,0,1) 12 method displayed a good performance for forecasting the cumulative incidences of typhoid (MAPE=13.257%) and paratyphoid fevers (MAPE=19.501%). 40 Although the SARIMA model displays a good forecasting performance, it cannot describe the nonlinear information included in the TB morbidity in that this model assumes that there is a linear link between successive values of the time series. 4 However, different from the SARIMA model, the dynamic NARNN method has been deemed as a promising alternative to address any nonlinear issue without any constraints because of its short-run memory function besides the common properties of static BPNN and GRNN.…”

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

“…For the prediction of ocean temperature changes, we use the seasonal ARIMA time series model, which can effectively predict the overall seasonal temperature changes in the target sea area in the next 30 years [22][23][24][25][26][27], and then we can determine the future annual average temperature of the target sea area and compare it with the suitable ocean temperature for herring and mackerel; we can get the target migration position of the future fish school.…”

Monthly pork price forecasting method based on Census X12-GM(1,1) combination model

“…GM (1,1) model can be used to predict a wide range of time series, such as traffic data prediction [19][20][21][22], financial data prediction [23,24], agricultural data prediction, weather data, geological disaster data, disease prevention and control data, etc [25][26][27][28]. At the same time, in order to further improve the prediction accuracy, GM (1,1) model is also combined with other models [29][30][31][32].…”