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Estimating the impact of the COVID-19 pandemic on dengue in Brazil.
Roster, K O; Martinelli, T; Connaughton, C; Santillana, M; Rodrigues, F A.
Afiliação
  • Roster KO; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil.
  • Martinelli T; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil.
  • Connaughton C; Mathematics Institute, University of Warwick, Coventry, United Kingdom.
  • Santillana M; London Mathematical Laboratory, London, United Kingdom.
  • Rodrigues FA; Machine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, MA, USA.
Res Sq ; 2023 Feb 09.
Article em En | MEDLINE | ID: mdl-36798282
Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: Res Sq Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: Res Sq Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos