Epidemiological trends and comparative forecasting models of human brucellosis in inner mongolia autonomous region, mainland China, 2004-2024.

Authors

Na Zhang, Qiuju Yang, Chuizhao Xue, Zhiguo Liu, Na Ta, Zhenjun Li

Published in

PLoS neglected tropical diseases. Volume 20. Issue 7. Pages e0014439. Epub Jul 16, 2026.

Abstract

Brucellosis remains a severe zoonotic threat in the Inner Mongolia Autonomous Region of China.
This study integrates a comprehensive epidemiological trend analysis with a novel methodological comparison of forecasting techniques to inform control strategies.
Using reported human brucellosis surveillance data from Inner Mongolia for 2004-2024, joinpoint regression analysis revealed a persistently increasing yet fluctuating long-term trend (AAPC = 5.13%, P < 0.001), characterized by significant epidemic surges in 2004-2010 (APC = 22.43%, P < 0.001) and 2016-2021 (APC = 29.83%, P < 0.001), interrupted by a decline phase in 2010-2016 (APC = -17.17%, P < 0.001) and 2021-2024 (APC = -12.15). The disease demonstrated strong seasonality with June-August peaks, and predominance among farmers and herdsmen aged 30-60 years. Building on this epidemiological foundation, we rigorously compared the predictive performance of the standard Seasonal Autoregressive Integrated Moving Average (SARIMA) model against its Bootstrap-enhanced version for 24-month-ahead forecasting (2023-2024 validation). This finding offers a novel perspective on enhancing the predictive performance of brucellosis models. While the Bootstrap approach achieved superior point forecast accuracy by reducing Mean Absolute Error by 39.95% and Median Absolute Percentage Error by 33.55% compared to SARIMA, it produced severely overconfident prediction intervals, with only 33.33% empirical coverage compared to SARIMA's 91.67%. This study validates the SARIMA model as a robust baseline for brucellosis forecasting and introduces a Bootstrap ensemble method as a powerful tool for significantly enhancing point prediction accuracy.
The findings provide novel epidemiological insights that offer a scientific basis for disease control measures and decision-making. Future work should aim to develop hybrid models that bridge this gap, and delivering high accuracy in both point and interval forecasts.

PMID:
42461909
Bibliographic data and abstract were imported from PubMed on 17 Jul 2026.

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