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Abstract
ObjectiveThe objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases.MethodsAccording to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model.ResultsThe simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025.ConclusionThe grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control.
Highlights
Typhoid fever and paratyphoid fever are systemic infections caused by Salmonella enterica, including S enterica serotype Typhi and serotypes Paratyphi A, B, and C [1,2]
Typhoid and paratyphoid fever have been effectively controlled in Europe and North America, in spite of that, the incidence remains high in some developing countries in Asia, Africa, and South America [4]
We propose that the grey disaster model is able to define the time distribution of typhoid and paratyphoid fever in China and the result may provide useful references for controlled application
Summary
Typhoid fever and paratyphoid fever are systemic infections caused by Salmonella enterica, including S enterica serotype Typhi and serotypes Paratyphi A, B, and C [1,2]. Typhoid and paratyphoid fever have been effectively controlled in Europe and North America, in spite of that, the incidence remains high in some developing countries in Asia, Africa, and South America [4]. It is still an important public health problem [5], to which much attention has been paid. Time series analysis [19,20], D-R model, GM(1,1) model [21,22], Markov chain prediction model [23] and multivariate linear regression [24] have been used to predict future trends in some infectious diseases These published forecasting methods mostly aim at the incidence, prevalence, or mortality rate (or the number of people) of a disease, rather than the time when an epidemic peak may occur
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