| Autor: |
da Silva, Cecilia Cordeiro, de Lima, Clarisse Lins, da Silva, Ana Clara Gomes, Moreno, Giselle Machado Magalhães, Musah, Anwar, Aldosery, Aisha, Dutra, Livia, Ambrizzi, Tercio, Borges, Iuri V. G., Tunali, Merve, Basibuyuk, Selma, Yenigün, Orhan, Massoni, Tiago Lima, Jones, Kate, Campos, Luiza, Kostkova, Patty, da Silva Filho, Abel Guilhermino, dos Santos, Wellington Pinheiro |
| Zdroj: |
Research on Biomedical Engineering; June 2022, Vol. 38 Issue: 2 p499-537, 39p |
| Abstrakt: |
Purpose: Dengue is considered one of the biggest public health problems in recent decades. Climate and demographic changes, the disorderly growth of cities and international trade have brought new arboviruses such as chikungunya and Zika. Control of arboviruses depends on control of the vector: the Aedes aegypti mosquito. Objective: In this work, we propose a methodology for building disease predictors capable of predicting infected cases and locations based on machine learning. We also propose an artificial experts committee based on meta-heuristic methods to detect the most relevant risk factors. Method Conclusion: The spatiotemporal prediction results showed the evolution of arboviruses, pointing out as major focuses on both regions richer in urban green areas and low-income neighborhood with irregular water supply. Determining the most relevant factors for prediction, as well as the spatial distribution of cases, can be useful for the planning and execution of public policies aimed at improving the health infrastructure and planning and controlling the vector. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
Full text from SpringerLink