ArticlePloS one2021
Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.BMC health services research · 2025Article
- Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.Health care management science · 2023Article
- Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference.BMC medical research methodology · 2023Article
- Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.PloS one · 2023Article
- Article
- Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model.European journal of medical research · 2022Article
- Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review.International journal of environmental research and public health · 2022Article
- Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.Water research · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.Journal of cellular and molecular medicine · 2022Article
- Numerical Investigations through ANNs for Solving COVID-19 Model.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
15 authors.
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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.
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