Abstract
Abstract Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.
Highlights
Chlamydia trachomatis (CT) is one of the most prevalent sexually transmitted diseases worldwide
To diagnose the fitness of the model, following criteria should be satisfied: (1) the residuals were distributed with a mean of zero and a constant variance in the standardised residuals; (2) there was no significant deviation from a zero mean white noise process in the autocorrelation functions (ACF) of the residuals; (3) the P value for the Ljung–Box statistic was greater than 0.05, which means that the null hypothesis of independence for this residual series cannot be rejected and (4) the normal Q–Q plot of the residuals of the model was normal distributed
We compared Akaike Information Criterion (AIC) values and Schwartz Bayesian Criterion (SBC) values of 40 models (Supplementary Table S1) and the seasonal autoregressive integrated moving average (SARIMA) (0.1,1)(0.1,1)12 model was selected as the optimal model with the lowest SBC (SBC = 444.26) and relatively low AIC (AIC = 436.08)
Summary
Chlamydia trachomatis (CT) is one of the most prevalent sexually transmitted diseases worldwide. According to the updated estimates from the World Health Organization, there were 127.2 million new CT cases among people aged 15–49 years in 2016 [1]. Infection of CT, if not treated properly and promptly, can result in serious sequelae, such as pelvic inflammatory disease, ectopic pregnancy, tubal infertility and chronic pelvic pain in women and nongonococcal urethritis, epididymitis and infertility in men [2,3,4,5]. It is estimated that the lifetime direct medical costs for chlamydia alone were ∼$516.7 million in the United States, which is a great burden to individuals and society [8]. Monitoring and prediction of epidemic status and trends of CT infections are critical for precision planning of CT control programme, appropriate allocation of available resources and accurate evaluation of implementation outcomes
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