Estimation of COVID-19 under-reporting in Brazilian States through SARI
| Autor: | Fabio Porto, Jorge de Abreu Soares, Balthazar Paixão, Eduardo Ogasawara, Rebecca Salles, Luciana E. G. Escobar, Lais Baroni, Carlos Augusto Moreira de Sousa, Rafaelli Coutinho, Marcel de Moraes Pedroso, Raphael de Freitas Saldanha |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Coronavirus disease 2019 (COVID-19) Computer Networks and Communications Computer science 02 engineering and technology Statistics - Applications Article Theoretical Computer Science Computer Science - Computers and Society Severe acute respiratory infection Moving average 020204 information systems Under-reporting Statistics Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Applications (stat.AP) Misinformation Estimation Mortality rate Time series modeling COVID-19 Hardware and Architecture Data analytics SARI 020201 artificial intelligence & image processing Software |
| Zdroj: | New Generation Computing |
| DOI: | 10.48550/arxiv.2006.12759 |
| Popis: | BackgroundDue to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to control, there are still few studies focused on under-reporting estimates. 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, the objective of this work is to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe on notification of Severe Acute Respiratory Infection (SARI). MethodologyThe 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 to 2019). The expected cases are derived from a seasonal exponential moving average. ResultsThe 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. ConclusionsOur work presents the estimation of the under-reporting rates of COVID-19 in Brazilian states. 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. |
| Databáze: | OpenAIRE |
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