Scholarlink

Arboviruses are part of the Neglected Tropical Diseases (NTDs) and are a global public health problem. Understanding and anticipating the outbreak of these arboviruses through predictive approaches is imperative in order to mitigate the associated direct and indirect impacts, especially in tropical regions. The aim of this study was therefore to create models to classify the predicted number of arbovirus cases into concentration intervals, as well as using a reservoir computing algorithm that reduces the training time of the models created. It obtained accuracy above 96.2%, precision and recall values above 0.962 and the training time was below 0.5 s for all the models.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatio-Temporal Prediction of Arbovirus Cases Using a Reservoir Computing Algorithm

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • A. C. G. da Silva,
  • C. L. de Lima,
  • C. P. N. Silva,
  • D. W. Lapa,
  • F. E. da Silva,
  • J. M. V. Fonseca,
  • J. M. B. de Albuquerque,
  • M. M. da S. Andrade,
  • C. C. da Silva,
  • G. M. M. Moreno,
  • J. C. Gomes,
  • M. A. de Santana,
  • F. T. Borges,
  • K. A. Sancho,
  • M. F. S. de Mendonça,
  • H. R. L. de Melo,
  • C. A. V. Cavalcante,
  • G. E. Z. Acosta,
  • J. A. B. Junior,
  • M. S. Neves,
  • M. Mané,
  • R. V. Rosa,
  • W. P. dos Santos

摘要

Arboviruses are part of the Neglected Tropical Diseases (NTDs) and are a global public health problem. Understanding and anticipating the outbreak of these arboviruses through predictive approaches is imperative in order to mitigate the associated direct and indirect impacts, especially in tropical regions. The aim of this study was therefore to create models to classify the predicted number of arbovirus cases into concentration intervals, as well as using a reservoir computing algorithm that reduces the training time of the models created. It obtained accuracy above 96.2%, precision and recall values above 0.962 and the training time was below 0.5 s for all the models.