ArticleMedicine2016
Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011.
Article in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- Article
- Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.BMC infectious diseases · 2024Article
- "Back to the future" projections for COVID-19 surges.PloS one · 2024Article
- Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.BMC infectious diseases · 2023Article
- Interrupted time series analysis using the ARIMA model of the impact of COVID-19 on the incidence rate of notifiable communicable diseases in China.BMC infectious diseases · 2023Article
- Article
- Utilizing Baidu Index to Track Online Interest in Influenza During the COVID-19 Pandemic in China.Cureus · 2022Article
- Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.Frontiers in public health · 2022Article
- Modeling COVID-19 incidence with Google Trends.Frontiers in research metrics and analytics · 2022Article
- Application Effect of Transparent Supervision Based on Informatization in Prevention and Control of Carbapenem-ResistantThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2022Article
- Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.International journal of environmental research and public health · 2021Article
- Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study.Journal of medical Internet research · 2021Article
- Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020.International journal of environmental research and public health · 2021Article
- A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine.Journal of healthcare engineering · 2021Article
- Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases.PloS one · 2021Article
- Temporal analysis of visceral leishmaniasis between 2000 and 2019 in Ardabil Province, Iran: A time-series study using ARIMA model.Journal of family medicine and primary care · 2020Article
- Anomalous asthma and chronic obstructive pulmonary disease Google Trends patterns during the COVID-19 pandemic.Clinical and translational allergy · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence.
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