Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models

Abstract

Background: This study aimed to explore the demographic and distributive features of acute hemorrhagic conjunctivitis (AHC). We constructed seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing (ETS) models to predict its trend in incidence in mainland China and provided evidence for the government to formulate policies regarding AHC prevention. Methods: Data regarding the distribution and demographic characteristics of AHC in China from 2011–2016 were retrieved from the Public Health Scientific Data website. Monthly AHC data from January 2011 to June 2019 were used to establish and evaluate the SARIMA and ETS models. Results: During 2011–2016, a total of 213,325 cases were reported; 46.01% were farmers, patients aged ≤9 years had the highest risk, and the male:female ratio was 1.31:1. Guangxi and Guangdong had the highest number of reported AHC cases. The SARIMA (0, 0, 1) (2, 0, 0) 12 model with the minimum root mean squared error and mean absolute percentage error were finally selected for in-sample simulation. Conclusions: AHC remains a serious public health problem in Southern and Eastern China that mainly affects farmers and children younger than 9 years. It is recommended that the health administration strengthen the publicity and education regarding AHC prevention among farmers and accelerate the development of related vaccines and treatment measures. Keywords: Acute hemorrhagic conjunctivitis, Seasonal autoregressive integrated moving average model, Exponential smoothing model, Time series analysis, Forecasting, China