ArticleBMJ open2020
Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Epidemiological characteristics and incidence prediction analysis of brucellosis in Bayingolin mongol autonomous prefecture, Xinjiang.BMC infectious diseases · 2026Article
- Diseases Caused by Parasites with Invertebrate Hosts in China: Burden and Trends of Leishmaniasis and Schistosomiasis.Pathogens (Basel, Switzerland) · 2026Article
- Impact of centralized volume-based procurement of high-value medical consumables on patients' hospitalization expenses in Gansu, China: an interrupted time series analysis.BMC health services research · 2026Article
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- Global Epidemiology of Vector-Borne Parasitic Diseases: Burden, Trends, Disparities, and Forecasts (1990-2036).Pathogens (Basel, Switzerland) · 2025Article
- Prediction and control for the transmission of brucellosis in inner Mongolia, China.Scientific reports · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Comparative effectiveness analysis of univariate time-series forecasting models for disease mortality rates in the global burden of disease database: a case study of global hypertensive heart disease among women of childbearing age.Frontiers in public health · 2025Article
- A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data.PloS one · 2025Article
- Sociodemographic inequalities in the global burden trends and machine learning-based projections of periodontitis from 1990 to 2030 across different development levels.Frontiers in oral health · 2025Article
- Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.PLoS neglected tropical diseases · 2025Article
- Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation Study.Entropy (Basel, Switzerland) · 2024Article
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- Uncertainty in Environmental Micropollutant Modeling.Environmental management · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.
designTime-series study.
settingMainland China.
methodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.
resultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)
conclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.
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