Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018-Reference-Cited by-同舟云学术

Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

Published:2022 Issue: Volume:55 Page:
ISSN:1678-9849
Container-title:Revista da Sociedade Brasileira de Medicina Tropical
language:
Short-container-title:Rev. Soc. Bras. Med. Trop.

Author:

Barboza Matheus Félix Xavier¹^ORCID,Monteiro Kayo Henrique de Carvalho¹^ORCID,Rodrigues Iago Richard²^ORCID,Santos Guto Leoni²^ORCID,Monteiro Wuelton Marcelo³^ORCID,Figueira Elder Augusto Guimaraes⁴^ORCID,Sampaio Vanderson de Souza⁵^ORCID,Lynn Theo⁶^ORCID,Endo Patricia Takako¹^ORCID

Affiliation:

1. Universidade de Pernambuco, Brasil

2. Universidade Federal de Pernambuco, Brasil

3. Universidade do Estado do Amazonas, Brasil; Fundação de Medicina Tropical Doutor Heitor Vieira Dourado, Brasil

4. Fundação de Vigilância em Saúde Rosemary Costa Pinto, Brasil; Instituto Oswaldo Cruz, Brasil

5. Fundação de Medicina Tropical Doutor Heitor Vieira Dourado, Brasil; Fundação de Vigilância em Saúde Rosemary Costa Pinto, Brasil; Instituto Todos pela Saúde, Brasil

6. Dublin City University, Ireland

Publisher

FapUNIFESP (SciELO)

Subject

Infectious Diseases,Microbiology (medical),Parasitology

Link

http://www.scielo.br/scielo.php?script=sci_pdf&pid=S0037-86822022000100324&tlng=en

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4. World Malaria Report 2020,2020

5. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016;Hay SI;The Lancet,2017

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