ArticleBioMed research international2016
Modelling Seasonal Brucellosis Epidemics in Bayingolin Mongol Autonomous Prefecture of Xinjiang, China, 2010-2014.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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25 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Dynamic Modeling of Prevention and Control ofTransboundary and emerging diseases · 2025Pooled it
- Simulation and prediction of human brucellosis epidemic trends in Changji city.Infectious Disease Modelling · 2026Article
- Spatial characteristics and driving factors of human brucellosis and plague infections in theOne health (Amsterdam, Netherlands) · 2026Article
- Seasonal dynamic modeling for real-time prediction of human brucellosis epidemiological trends in Gansu, Guangdong and Sichuan Provinces, China.PLoS neglected tropical diseases · 2026Article
- A multi-source data-based modelling study on brucellosis transmission risk analysis and control strategies in Zhejiang Province, China.BMC infectious diseases · 2026Article
- Assessing the spatiotemporal dynamics and driving factors of human brucellosis in Northern Xinjiang, China (2015-2023).Tropical medicine and health · 2026Article
- The continuous expansion and spread of human brucellosis in the Xinjiang Uygur Autonomous Region: evidence from epidemiological and strains' genotyping-based analysis.BMC microbiology · 2025Article
- Spatial interpolation and spatiotemporal scanning analysis of human brucellosis in mainland China from 2012 to 2018.Scientific reports · 2025Article
- Changing trends in human brucellosis in pastoral and agricultural China, 2004-2019: a Joinpoint regression analysis.BMC infectious diseases · 2025Article
- Prediction and control for the transmission of brucellosis in inner Mongolia, China.Scientific reports · 2025Article
- Effective intervention of brucellosis prevention in developing countries: A dynamic modelling study.One health (Amsterdam, Netherlands) · 2024Article
- Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China.Epidemiology and infection · 2024Article
- The impact of temperature, humidity and closing school on the mumps epidemic: a case study in the mainland of China.BMC public health · 2024Article
- Socioeconomic, meteorological factors and spatiotemporal distribution of human brucellosis in China between 2004 and 2019-A study based on spatial panel model.PLoS neglected tropical diseases · 2023Article
- Driving effect of multiplex factors on human brucellosis in high incidence region, implication for brucellosis based on one health concept.One health (Amsterdam, Netherlands) · 2022Article
- Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study.Frontiers in surgery · 2022Article
- Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.BMJ open · 2020Article
- Seroprevalence ofVeterinary world · 2020Article
- The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans.BMC public health · 2020Article
- Effect of climatic factors on the seasonal fluctuation of human brucellosis in Yulin, northern China.BMC public health · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Brucellosis is one of the severe public health problems; the cumulative number of new human brucellosis cases reached 211515 from 2010 to 2014 in China. Bayingolin Mongol Autonomous Prefecture is situated in the southeast of Xinjiang, where brucellosis infection occurs every year. Based on the reported data of newly acute human brucellosis cases for each season in Bayingolin Mongol Autonomous Prefecture, we proposed a susceptible, exposed, infected, and vaccinated (SEIV) model with periodic transmission rates to investigate the seasonal brucellosis transmission dynamics among sheep/cattle and from sheep/cattle to humans. Compared with the criteria of MAPE and RMSPE, the model simulations agree to the data on newly acute human brucellosis. We predict that the number of newly acute human brucellosis is increasing and will peak 15325 [95% CI: 11920-18242] around the summer of 2023. We also estimate the basic reproduction number
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