ArticlePloS one2020
Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Climate-Sensitive Transmission of Human Brucellosis: A Systematic Review of Climatic and Environmental Determinants in the Middle East.Journal of tropical medicine · 2026Review
- Ecological study of human brucellosis in Iran from 2000 to 2023 and the limited role of climatic factors.Scientific reports · 2025Article
- Incidence trend analysis of human brucellosis in the context of human development: an ecological study using joinpoint regression in Iran.BMC public health · 2025Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China.Epidemiology and infection · 2024Article
- Article
- Risk effects of meteorological factors on human brucellosis in Jilin province, China, 2005-2019.Heliyon · 2024Article
- Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model.Transboundary and emerging diseases · 2024Article
- Epidemiology and time series analysis of human brucellosis in Tebessa province, Algeria, from 2000 to 2020.Journal of research in health sciences · 2022Article
- Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network.Journal of healthcare engineering · 2022Article
- Changing Patterns in Epidemiology of Brucellosis in the South of Iran (2015-2020): Based on Cochrane-Armitage Trend Test.Annals of global health · 2022Article
- Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.BMC infectious diseases · 2021Article
- Predicting the incidence of brucellosis in Western Iran using Markov switching model.BMC research notes · 2021Article
- Ovine visceral organs as reservoir candidate forIranian journal of veterinary research · 2021Article
- Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.BMJ open · 2020Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters.
methodsIn this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province-located in northwestern Iran- over a period of 9 years (2010-2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R2 criteria.
resultsThe incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010-2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.
conclusionsThe multilayer perceptron neural network can be used as an effective method in detecting the behavioral trend of brucellosis over time. Nevertheless, further studies focusing on the application and comparison of these methods are needed to detect the most appropriate forecast method for this disease.
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