COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020

Bastos, Leonardo Soares; Niquini, Roberta Pereira; Lana, Raquel Martins; Villela, Daniel A. M.; Cruz, Oswaldo Gonçalves; Coelho, Flávio Codeço; Codeço, Cláudia Torres; Gomes, Marcelo Ferreira da Costa

doi:10.1590/0102-311x00070120

Cited by 88 publications

(89 citation statements)

References 3 publications

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“…The events detected in 2020 are a consequence of COVID-19 in Brazil. These events coincide with the first record of the disease in the country, considering the time for the disease spread and the manifestation of symptoms [32,33]. The events appear for most of the states from March 07 and March 14.…”

Section: Methods and Parameter Selectionsupporting

confidence: 67%

Estimation of COVID-19 under-reporting in the Brazilian States through SARI

Paixão

Pedroso

Baroni

et al. 2020

Preprint

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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.

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Section: Methods and Parameter Selectionsupporting

confidence: 67%

Estimation of COVID-19 under-reporting in the Brazilian States through SARI

Paixão

Pedroso

Baroni

et al. 2020

Preprint

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“…(11,22,23,24) Our findings also agree with epidemiological data from syndromic surveillance of severe acute respiratory illness (SARI) that detected an excess of non-influenza SARI cases above the seasonal average in France, the USA and Brazil since late-February/early-March and further confirm SARS-CoV-2 positive samples among hospitalised SARI cases in Brazil since mid-February (16th-22nd February) (Available from: http://info.gripe.fiocruz.br). (9,41,42) The TN1 projections and the molecular clock analyses provide quite convergent estimates of the probable onset date of community transmission of SARS-CoV-2 in those locations where the epidemic was mostly driven by one seeding event. For countries with evidence of few and multiple independent seeding events, however, our TN10 and TN100 projections push back the timeline of community spread 10-20 days earlier than molecular clock estimates, respectively.…”

mentioning

confidence: 94%

Tracking the onset date of the community spread of SARS-CoV-2 in western countries

Delatorre

Mir

Gräf

et al. 2020

Mem. Inst. Oswaldo Cruz

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly spread around the world during 2020, but the precise time in which the virus began to spread locally is difficult to trace for most countries. Here, we estimate the probable onset date of the community spread of SARS-CoV-2 for heavily affected countries from Western Europe and the Americas on the basis of the cumulative number of deaths reported during the early stage of the epidemic. Our results support that SARS-CoV-2 probably started to spread locally in all western countries analysed between mid-January and mid-February 2020, thus long before community transmission was officially recognised and control measures were implemented.

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“…In contrast, in Bahia, the ratio between the number of deaths and the number of confirmed COVID-19 cases was 2.2% and the mortality rate for COVID-19 was 20.1/100,000 inhabitants. The incidence of SRAG in Ceará reached 161.1/100,000, followed by 112.5/100,000 in Pernambuco and 97.8/100,000 in Piauí, indicating that there are a large number of cases of COVID-19 that are not being registered as such in these states, even considering the underreporting and delayed notification of SRAG cases 26 . The epidemic had already reached 100% of the municipalities in most states, with the exception of Bahia and Piauí.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 no Nordeste brasileiro: sucessos e limitações nas respostas dos governos dos estados

Kerr

Kendall

Silva

et al. 2020

Ciênc. saúde coletiva

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Resumo No Brasil, a pandemia da COVID-19 tem sido severa nos estados das regiões mais pobres, como o Nordeste. A falta de políticas nacionais para controle da pandemia levou as autoridades estaduais e municipais a implementarem medidas de saúde pública. O objetivo deste estudo é mostrar o efeito dessas medidas na epidemia. A maior incidência da COVID-19 entre os nove estados do Nordeste foi registrada em Sergipe, Paraíba e Ceará. O Piauí, a Paraíba e Ceará foram os que mais testaram. Muitos estados apresentavam alta proporção de pessoas em trabalho informal. Estados com aeroportos internacionais tiveram importante papel na entrada e disseminação inicial do vírus, em especial o Ceará. Todos os estados aplicaram medidas de distanciamento social, proibição de eventos públicos e fechamento de unidades de ensino. As respostas foram o aumento significativo de distanciamento social, em especial Ceará e Pernambuco, a queda do número de reprodução (Rt) e a separação da curva dos casos observados da curva dos casos esperados sem as intervenções não medicamentosas em todos os estados. A pobreza, a desigualdade e as altas taxas de trabalho informal fornecem pistas do porquê da intensidade da COVID-19 na região. Por outro lado, as medidas de mitigação tomadas precocemente pelos governantes amenizaram os efeitos da pandemia.

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COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020

Cited by 88 publications

References 3 publications

Estimation of COVID-19 under-reporting in the Brazilian States through SARI

Estimation of COVID-19 under-reporting in the Brazilian States through SARI

Tracking the onset date of the community spread of SARS-CoV-2 in western countries

COVID-19 no Nordeste brasileiro: sucessos e limitações nas respostas dos governos dos estados

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