Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis

Abstract

ObjectivesThe prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors. Study designThe design of this study is a combination of the prediction method and empirical analysis. MethodsBy organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis. ResultsOwing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self–memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/105). ConclusionsThe grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems.