ArticleBMC medical research methodology2020
SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 32 citations in OpenAlex.
- Spatial-temporal distribution characteristics of haemorrhagic fever with renal syndrome (HFRS) in 31 provinces and municipalities of P.R. China and multi-factorial prediction of incidence.PLoS neglected tropical diseases · 2026Article
- Three-stage interpretability analysis of influenza virus and meteorological correlation in Jiuquan City, 2016-2025: SARIMAX + TreeSHAP.Frontiers in cellular and infection microbiology · 2026Article
- Forecasting Acute Hemorrhagic Conjunctivitis Incidence in Henan, China: A Comparative Study of Seasonal Autoregressive Fractionally Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models.Risk management and healthcare policy · 2026Article
- Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis.Journal of global health · 2025Article
- Enhancing public health surveillance: SARIMAX model incorporating Baidu search index for HCV prediction in China.BMC medical research methodology · 2025Article
- Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand.Animals : an open access journal from MDPI · 2025Article
- Effect and prediction of long-term weather and pollutant exposure on hemorrhagic fever with renal syndrome: based on statistical models.Frontiers in public health · 2025Article
- Application of an ARFIMA Model to Estimate Hepatitis C Epidemics in Henan, China.The American journal of tropical medicine and hygiene · 2024Article
- Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis.Journal of translational medicine · 2024Article
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China.World journal of gastroenterology · 2023Article
- Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.PLoS neglected tropical diseases · 2023Article
- COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.International journal of environmental research and public health · 2023Article
- Predicting COVID-19 using lioness optimization algorithm and graph convolution network.Soft computing · 2023Article
- Forecasting COVID-19 Epidemic Trends by Combining a Neural Network withEntropy (Basel, Switzerland) · 2022Article
- Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.PloS one · 2022Article
- Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China.Infection and drug resistance · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.
methodsData on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.
resultsModel assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)
conclusionsThe SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.
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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.