| Author | Paixão, Balthazar | |
| Author | Baroni, Lais | |
| Author | Pedroso, Marcel | |
| Author | Salles, Rebecca | |
| Author | Escobar, Luciana | |
| Author | Sousa, Carlos de | |
| Author | Saldanha, Raphael de Freitas | |
| Author | Soares, Jorge | |
| Author | Coutinho, Rafaelli | |
| Author | Porto, Fabio | |
| Author | Ogasawara, Eduardo | |
| Access date | 2022-05-11T22:40:56Z | |
| Available date | 2022-05-11T22:40:56Z | |
| Document date | 2021 | |
| Citation | PAIXÃO, Balthazar et al. Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI. New Generation Computing, v. 39, n. (3-4), p. 623-645, Mar. 2021. | en_US |
| ISSN | 0288-3635 | |
| URI | https://www.arca.fiocruz.br/handle/icict/52627 | |
| Sponsorship | BP, LB, FP were supported by CNPq. RS was supported by CAPES (finance code 001). MP was supported by FAPERJ. EO was supported by both CNPq and FAPERJ. The content is solely the responsibility of the authors. It does not necessarily represent the official views of the funding agencies. The funding agencies had no role in the study design, data collection, and analyses, decision to publish, or preparation of the manuscript. | en_US |
| Language | eng | en_US |
| Publisher | Springer | en_US |
| Rights | open access | en_US |
| Title | Estimation of COVID‑19 Under‑Reporting in the Brazilian States Through SARI | en_US |
| Type | Article | en_US |
| DOI | 10.1007/s00354-021-00125-3 | |
| Abstract | Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, this work aims to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe. InfoGripe targets notifications of Severe Acute Respiratory Infection (SARI). The methodology is based on the combination of data analytics (event detection methods) and time series modeling (inertia and novelty concepts) over hospitalized SARI cases. The estimate of real cases of the disease, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016-2019). The expected cases are derived from a seasonal exponential moving average. The results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil. The states of Minas Gerais and Mato Grosso have the highest rates of under-reporting of cases. The rate of under-reporting of deaths is high in the Rio Grande do Sul and the Minas Gerais. This work can be highlighted for the combination of data analytics and time series modeling. Our calculation of under-reporting rates based on SARI is conservative and better characterized by deaths than for cases. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | National Laboratory of Scientific Computing. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal Center for Technological Education of Rio de Janeiro. Rio de Janeiro, RJ, Brazil. | en_US |
| Subject | COVID-19 | en_US |
| Subject | Data analytics | en_US |
| Subject | SARI | en_US |
| Subject | Time series modeling | en_US |
| Subject | Under-reporting | en_US |
| e-ISSN | 1882-7055 | |
