Seasonal autoregressive moving average model-based prediction of bacteriophage dysentery incidence trends in Xinjiang / 公共卫生与预防医学

ABSTRACT

Objective To analyze the epidemiological characteristics of bacillary dysentery in Xinjiang from 2005-2018, to explore the feasibility and applicability of seasonal autoregressive moving average model to predict the incidence pattern of bacillary dysentery in Xinjiang, and to provide a scientific basis for decision-making in the prevention and control of bacillary dysentery. Methods Descriptive analysis was used to analyze the epidemiological characteristics of bacillary dysentery, and Python software was used to construct a SARIMA model and predict the incidence trend. Results The average annual reported incidence rate of bacillary dysentery in Xinjiang from 2005-2018 was 35.71/100 000, with peak incidence concentrated in June-October. The difference in the incidence rate of bacillary dysentery among the age groups was statistically significant (χ2=145605.90, P60 years age groups. The resulting model was SARIMA (0,1,2)(0,1,1)12 with all parameters statistically significant (P12 model has good accuracy in predicting the incidence of bacillary dysentery in Xinjiang and can be used for medium-term prediction of the disease.