Arbovirus case prediction in Recife using reservoir computing
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1298Keywords:
Reservoir computing, Forecasting Model, Arbovirus ForecastingAbstract
Objective: Due to the complexity of diagnosing arboviruses, machine learning prediction aims to anticipate outbreaks, speed up treatment and reduce spread. Method: This study proposes applying reservoir computing techniques, incorporating climatic factors, to predict outbreaks and increases in the incidence of these diseases. Results: The models created had values greater than 0.80 for accuracy, precision and recall in predicting cases in Recife, Pernambuco. Conclusion: These models are crucial for decision-making, enabling more effective preventive and control interventions against arboviruses in public health.
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