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Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil.

Laís Picinini FreitasClaudia Torres CodeçoLeonardo Soares BastosDaniel Antunes Maciel VillelaOswaldo Gonçalves CruzAntonio Guilherme PachecoFlávio Codeço CoelhoRaquel Martins LanaLuiz Max Fagundes de CarvalhoRoberta Pereira NiquiniWalquiria Aparecida Ferreira de AlmeidaDaiana Araujo da SilvaFelipe Cotrim de CarvalhoMarcelo Ferreira da Costa Gomes
Published in: Cadernos de saude publica (2024)
The influenza-like illness (ILI) sentinel surveillance operates in Brazil to identify respiratory viruses of public health relevance circulating in the country and was first implemented in 2000. Recently, the COVID-19 pandemic reinforced the importance of early detection of the circulation of new viruses in Brazil. Therefore, an analysis of the design of the ILI sentinel surveillance is timely. To this end, we simulated a sentinel surveillance network, identifying the municipalities that would be part of the network according to the criteria defined in the design of the ILI sentinel surveillance and, based on data from tested cases of severe acute respiratory illness (SARI) from 2014 to 2019, we drew samples for each sentinel municipality per epidemiological week. The draw was performed 1,000 times, obtaining the median and 95% quantile interval (95%QI) of virus positivity by Federative Unit and epidemiological week. According to the ILI sentinel surveillance design criteria, sentinel units would be in 64 municipalities, distributed mainly in capitals and their metropolitan areas, recommending 690 weekly samples. The design showed good sensitivity (91.65% considering the 95%QI) for qualitatively detecting respiratory viruses, even those with low circulation. However, there was important uncertainty in the quantitative estimate of positivity, reaching at least 20% in 11.34% of estimates. The results presented here aim to assist in evaluating and updating the ILI sentinel surveillance design. Strategies to reduce uncertainty in positivity estimates need to be evaluated, as does the need for greater spatial coverage.
Keyphrases
  • public health
  • randomized controlled trial
  • clinical trial
  • high resolution
  • deep learning