ArticleEpidemiology and infection2020
Time series analysis and forecasting of chlamydia trachomatis incidence using surveillance data from 2008 to 2019 in Shenzhen, China.
Article in Epidemiology and infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- Emerging resistance in genital mycoplasmas: 6-year trends of Ureaplasma spp. and Mycoplasma hominis infections in Eastern China.BMC infectious diseases · 2025Article
- Enhancing HIV/STI decision-making: challenges and opportunities in leveraging predictive models for individuals, healthcare providers, and policymakers.Journal of translational medicine · 2024Review
- Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018.Heliyon · 2024Article
- Prevalence trend and burden of neglected parasitic diseases in China from 1990 to 2019: findings from global burden of disease study.Frontiers in public health · 2023Article
- Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019.Tropical medicine and infectious disease · 2022Article
- Article
- Analyzing and forecasting COVID-19 pandemic in the Kingdom of Saudi Arabia using ARIMA and SIR models.Computational intelligence · 2022Article
- Impact of COVID-19 outbreaks and interventions on influenza in China and the United States.Nature communications · 2021Article
- Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.Infection and drug resistance · 2021Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
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.
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