ArticleScientific reports2016
Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Resurgence of pertussis: Epidemiological trends, contributing factors, challenges, and recommendations for vaccination and surveillance.Human vaccines & immunotherapeutics · 2025Review
- Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis.Journal of global health · 2025Article
- Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model.BMC public health · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Pertussis in the Tshwane District, Gauteng province, South Africa: A cross-sectional study.Journal of public health in Africa · 2025Article
- Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management.Scientific reports · 2024Article
- Assessing the impact of COVID-19 non-pharmaceutical interventions and relaxation policies on Class B respiratory infectious diseases transmission in China.Scientific reports · 2024Article
- A SEER-based analysis of trends in HPV-associated oropharyngeal squamous cell carcinoma.Infectious agents and cancer · 2024Article
- Comparison of ARIMA and Bayesian Structural Time Series Models for Predicting the Trend of Syphilis Epidemic in Jiangsu Province.Infection and drug resistance · 2024Article
- Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.BMC infectious diseases · 2023Article
- Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic.JAMA network open · 2022Article
- ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021.BMC public health · 2022Article
- Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020.JMIR public health and surveillance · 2022Observational
- Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020.BMC public health · 2022Article
- Pertussis surveillance results from a French general practitioner network, France, 2017 to 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022Article
- Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.PLOS global public health · 2022Article
- Article
- Epidemiological characteristics and forecasting incidence for patients with breast cancer in Shantou, Southern China: 2006-2017.Cancer medicine · 2021Article
- Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.Modeling earth systems and environment · 2021Article
- Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.BMJ open · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.