ArticlePloS one2018
A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS).
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 29 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
- Spatiotemporal dynamics of the capture rate of Rattus tanezumi and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China.Environmental monitoring and assessment · 2026Article
- Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China.Epidemiology and infection · 2024Article
- Long-term trend prediction of pandemic combining the compartmental and deep learning models.Scientific reports · 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
- Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.PloS one · 2022Article
- Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.BMC infectious diseases · 2021Article
- On the accuracy of ARIMA based prediction of COVID-19 spread.Results in physics · 2021Article
- Misdiagnosis of scrub typhus as hemorrhagic fever with renal syndrome and potential co-infection of both diseases in patients in Shandong Province, China, 2013-2014.PLoS neglected tropical diseases · 2021Article
- An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China.Communications biology · 2021Article
- Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling.PloS one · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.
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