AIMC Topic: Brucellosis

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A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data.

Zoonoses and public health Jul 15, 2019
The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniqu...
Humans Animals Brucellosis Support Vector Machine Models, Statistical Population Surveillance Iran Data Mining Zoonoses
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Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.

BMC infectious diseases May 14, 2019
BACKGROUND: Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the period...
Humans Neural Networks, Computer China Incidence Brucellosis Models, Statistical Seasons Recurrence Jordan
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Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature selection and hyperparameter tuning.

Acta tropica Jul 1, 2025
Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to cr...
Humans Machine Learning Algorithms Brucellosis Iran Disease Susceptibility
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