Prevalence of molecular and serological tests of the new coronavirus (SARS-CoV-2) in Carlos Chagas laboratory - Sabin group in Cuiabá.

BACKGROUND: Coronavirus disease 2019, which is caused by the new severe acute respiratory syndrome coronavirus 2, became a pandemic in 2020 with a mortality rate of 2% and high transmissibility, thus making studies with an epidemiological profile essential. OBJECTIVES: The aim of this study was to characterize the population that performed the severe acute respiratory syndrome coronavirus 2 molecular and serological tests in Carlos Chagas Laboratory - Sabin Group in Cuiabá. METHODS: A retrospective cross-sectional study was carried out with all the samples collected from nasal swab tested by RT-PCR and serological for severe acute respiratory syndrome coronavirus 2 IgM/IgG from the population served between April and December 2020. FINDINGS: In the analysis period, 23,631 PCR-coronavirus disease 2019 examinations were registered. Of this total number of cases, 7,649 (32.37%) tested positive, while 15,982 (66.31%) did not detect viral RNA and 374 of the results as undetermined. The peak of positive RT-PCR performed in July (n=5,878), with 35.65% (n=2,096). A total of 8,884 tests were performed on serological test SOROVID-19, with a peak of 1,169 (57.16%) of the positive tests for severe acute respiratory syndrome coronavirus 2 in July. MAIN CONCLUSIONS: Molecular positivity and serological tests, both peaked in July 2020, were mostly present in women aged 20-59 years, characterizing Cuiabá as the epicenter of the Midwest region in this period due to the high rate of transmissibility of severe acute respiratory syndrome coronavirus 2.

Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases.

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.