Back

Epidemiological and clinical characteristics of COVID-19 in Brazil using digital technology

Hajar, F. N.; Fernandes-Silva, M. M.; Pereira da Cunha, G. S.; Herrera, G.; Hamud, A.; Azevedo, V. F.

2020-10-02 epidemiology
10.1101/2020.09.30.20204917 medRxiv
Show abstract

BackgroundBrazil has the third-highest number of Coronavirus disease 2019 (COVID-19) cases worldwide. Understanding the epidemiology of COVID-19 from reported cases is challenging due to heterogeneous testing rates. We estimated the number of COVID-19 cases in Brazil on a national and regional level using digital technology. MethodsWe used a web-based application to perform a population-based survey from March 21st to August 29th, 2020 in Brazil. We obtained responses from 243 461 individuals across all federative units, who answered questions on COVID-19-related symptoms, chronic diseases and address of residence. COVID-19 was defined as at least one of the following: fever, cough, dyspnea and nasal flaring, associated with a history of close contact with a suspect or confirmed COVID-19 case in the previous 14 days. A stratified two-stage weighted survey analysis was performed to estimate the population level prevalence of COVID-19 cases. ResultsAfter calibration weighing, we estimated that 10 339 461 cases of COVID-19 occurred, yielding a 2.75 estimated infection per officially reported case. Estimated/reported ratios varied across Brazilian states and were higher in states with lower human development indexes. Areas with lower income levels displayed higher rates of COVID-19 cases (66 vs 38 cases/1000 people in the lowest and highest income strata respectively, p<0.001), but presented lower rates of COVID-19 testing. ConclusionIn this population-based survey using digital technology in Brazil, we estimated that the COVID-19 case rates were 2.75 times higher than officially reported. The estimated per reported case ratios were higher in areas with worse socioeconomic status.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.