TL;DR
This study estimates COVID-19 under-reporting in Brazilian states by analyzing SARI hospitalization data, revealing significant regional variations and highlighting the importance of accurate reporting for effective pandemic response.
Contribution
It introduces a novel method using SARI data and event detection techniques to estimate COVID-19 under-reporting at the state level in Brazil.
Findings
Under-reporting rates vary widely across states.
No consistent regional patterns in under-reporting.
Method provides more accurate estimates of true COVID-19 cases.
Abstract
Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to controlling, 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). The methodology is based on the concepts of inertia and the use of event detection techniques to study the time series of 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
