Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI

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.

How many more? Under‐reporting of the COVID‐19 deaths in Brazil in 2020

“…The key finding of 22.62% underestimation and corrected COVID-19 MR in Brazil in 2020 was based on the direct method, thus independent of the pre-epidemic data and eventual bias in the adjustment methods. Finally, a reasonable agreement between this result and that from the other two studies with the same scope [23,25] provides some reassurance as to the validity of the conclusion.…”

“…Another study found the mortality by natural causes in Brazil 22% higher than expected as of early June 2020 compared to the 2015-2019 period [27], which is equivalent to the excess MR of 125 per 100 000. The most recent publication on this topic [25] estimated 57 070 undisclosed COVID-19 deaths in 2020, corresponding to about 23% downward bias and COVID-19 MR of 117 per 100 000 compared with the Ministry of Health data [5]. As COVID-19 treatment advanced, its case fatality reduced and brought about lower mortality by the end of the year, as suggested in the present study.…”

“…The 21.62% underestimation of COVID-19 mortality is very close to the 22% [23] and not far from 28% [25] excess mortality from natural causes and SARI, respectively. Two other studies found an average underreporting of 40.68% (range 25.9-62.7%) for six large metropolitan areas in Brazil [20], and 30-57% in the state of Minas Gerais by mid-June 2020 compared with 2017-2019 mean [22].…”

“…Except for the latter, all other used historical time series forecast [19], or 3-year average prior to the epidemic [21], or the last year before the epidemic [22] as a reference to estimate the EM due to COVID-19. Only two publications provided results for all federal states, as well as for the whole country [23,25] but did not compare the results by multiple methods and data sources.…”

Seroprevalence of SARS-CoV-2 in southwest Goiás, Brazil, 2020: a population-based cross-sectional serological study

“…In Brazil, one of the countries with the lowest number of tests performed in the world, the Ministry of Health confirmed the first case on 25 February 2020 and the first report of self-sustaining cases on 5 March 2020. Therefore, COVID-19 is significantly underreported, thus resulting in an increase in the number of symptomatic cases and hampering the application of effective public policies, mainly in terms of social restriction [4][5][6][7] . Moreover, information on socioeconomic, demographic and epidemiological determinants are not routinely investigated [8] .…”