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Nowcasting methods to improve the performance of respiratory sentinel surveillance: lessons from the COVID-19 pandemic.
Flores-Alvarado, Sandra; Olivares, María Fernanda; Vergara, Natalia; García, Christian; Canals, Mauricio; Cuadrado, Cristóbal.
Afiliação
  • Flores-Alvarado S; Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Av. Independencia 939, Santiago, Chile.
  • Olivares MF; Programa de Doctorado en Salud Pública, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
  • Vergara N; Departamento de Epidemiología, Subsecretaría de Salud Pública, Ministerio de Salud de Chile, Santiago, Chile.
  • García C; Departamento de Epidemiología, Subsecretaría de Salud Pública, Ministerio de Salud de Chile, Santiago, Chile.
  • Canals M; Departamento de Epidemiología, Subsecretaría de Salud Pública, Ministerio de Salud de Chile, Santiago, Chile.
  • Cuadrado C; Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Av. Independencia 939, Santiago, Chile.
Sci Rep ; 14(1): 12582, 2024 05 31.
Article em En | MEDLINE | ID: mdl-38822070
ABSTRACT
Respiratory diseases, including influenza and coronaviruses, pose recurrent global threats. This study delves into the respiratory surveillance systems, focusing on the effectiveness of SARI sentinel surveillance for total and severe cases incidence estimation. Leveraging data from the COVID-19 pandemic in Chile, we examined 2020-2023 data (a 159-week period) comparing census surveillance results of confirmed cases and hospitalizations, with sentinel surveillance. Our analyses revealed a consistent underestimation of total cases and an overestimation of severe cases of sentinel surveillance. To address these limitations, we introduce a nowcasting model, improving the precision and accuracy of incidence estimates. Furthermore, the integration of genomic surveillance data significantly enhances model predictions. While our findings are primarily focused on COVID-19, they have implications for respiratory virus surveillance and early detection of respiratory epidemics. The nowcasting model offers real-time insights into an outbreak for public health decision-making, using the same surveillance data that is routinely collected. This approach enhances preparedness for emerging respiratory diseases by the development of practical solutions with applications in public health.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vigilância de Evento Sentinela / COVID-19 Limite: Humans País/Região como assunto: America do sul / Chile Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Chile País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vigilância de Evento Sentinela / COVID-19 Limite: Humans País/Região como assunto: America do sul / Chile Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Chile País de publicação: Reino Unido