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3 results on '"Gilberto Nerino de Souza Jr."'

1. Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará

Author

Gilberto Nerino de Souza Jr., Marcus de Barros Braga, Luana Lorena Silva Rodrigues, Rafael da Silva Fernandes, Rommel Thiago Jucá Ramos, Adriana Ribeiro Carneiro, Silvana Rossy de Brito, Cícero Jorge Fonseca Dolácio, Ivaldo da Silva Tavares Jr., Fernando Napoleão Noronha, Raphael Rodrigues Pinheiro, Hugo Alex Carneiro Diniz, Marcel do Nascimento Botelho, Antonio Carlos Rosário Vallinoto, and Jonas Elias Castro da Rocha

Subjects
COVID-19, Inteligência Artificial, Projeção, Tomada de Decisões, Medicine, Public aspects of medicine, RA1-1270
Abstract

Resumo Objetivo: Relatar o produto de pesquisa e extensão universitária denominado Boletim COVID-PA, que apresentou projeções sobre o comportamento da pandemia no estado do Pará, Brasil. Métodos: Utilizou-se da técnica de inteligência artificial conhecida como ‘redes neurais artificiais’, para geração de 13 boletins com projeções de curto prazo baseadas nos dados históricos do sistema da Secretaria de Estado de Saúde Pública. Resultados: Após oito meses de projeções, a técnica gerou resultados confiáveis, com precisão média de 97% (147 dias observados) para casos confirmados, 96% (161 dias observados) para óbitos e 86% (72 dias observados) para ocupação de leitos de unidade de terapia intensiva. Conclusão: Esses boletins tornaram-se um instrumento útil para a tomada de decisão de gestores públicos, auxiliando na realocação de recursos hospitalares e otimização das estratégias de controle da COVID-19 nas diversas regiões do estado do Pará.

Published
2021
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2. BOLETIM COVID PA #15

Author

De Barros Braga, Marcus, Gilberto Nerino De Souza Jr, Fernandes, Rafael, Da Rocha, Jonas Elias Castro, Botelho, Marcel Do Nascimento, Diniz, Hugo Alex, Brito, Silvana Rossy, Carneiro, Adriana, Vallinoto, Antonio Carlos R, Rodrigues, Luana, Miranda, Claudia, Thais Gleice Braga, Da Silva Tavares Júnior, Ivaldo, Cícero Dolácio, Rodolfo, Luis, and Pinheiro, Raphael Rodrigues

Published
2021
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3. Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon

Author

Antonio Carlos Rosário Vallinoto, Rommel Thiago Jucá Ramos, Raphael Rodrigues Pinheiro, Cícero Jorge Fonseca Dolácio, Adriana Ribeiro Carneiro, Jonas Elias Castro da Rocha, Rafael Fernandes, Fernando Napoleão Noronha, Silvana Rossy de Brito, Marcus de Barros Braga, Hugo Alex Carneiro Diniz, Gilberto Nerino de Souza Jr., Marcel do Nascimento Botelho, Ivaldo da Silva Tavares Jr., and Luana Lorena Silva Rodrigues

Subjects
Viral Diseases, Epidemiology, Social Sciences, Beds, Geographical locations, Mathematical and Statistical Techniques, Medical Conditions, 0302 clinical medicine, Animal Cells, Pandemic, Medicine and Health Sciences, 030212 general & internal medicine, Neurons, Neuronal Death, 0303 health sciences, Multidisciplinary, Cell Death, Geography, Artificial neural network, Amazon rainforest, Statistics, Hospitalization, Infectious Diseases, Cell Processes, Physical Sciences, Medicine, Cellular Types, Brazil, Research Article, Computer and Information Sciences, Coronavirus disease 2019 (COVID-19), Occupancy, Science, Research and Analysis Methods, Human Geography, Urban Geography, 03 medical and health sciences, Artificial Intelligence, Humans, Operations management, Statistical Methods, Cities, Pandemics, Artificial Neural Networks, 030304 developmental biology, Training period, Computational Neuroscience, Government, Models, Statistical, SARS-CoV-2, Biology and Life Sciences, Computational Biology, COVID-19, Covid 19, Cell Biology, South America, Term (time), Cellular Neuroscience, Earth Sciences, Neural Networks, Computer, People and places, Mathematics, Neuroscience, Forecasting
Abstract

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

Published
2021

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