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Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.
Cox, Victoria; O'Driscoll, Megan; Imai, Natsuko; Prayitno, Ari; Hadinegoro, Sri Rezeki; Taurel, Anne-Frieda; Coudeville, Laurent; Dorigatti, Ilaria.
Afiliação
  • Cox V; MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, United Kingdom.
  • O'Driscoll M; Department of Genetics, University of Cambridge, Cambridge, United Kingdom.
  • Imai N; MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, United Kingdom.
  • Prayitno A; Department of Child Health, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
  • Hadinegoro SR; Department of Child Health, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
  • Taurel AF; Sanofi Pasteur, Singapore.
  • Coudeville L; Sanofi Pasteur, Lyon, France.
  • Dorigatti I; MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, United Kingdom.
PLoS Negl Trop Dis ; 16(7): e0010592, 2022 07.
Article em En | MEDLINE | ID: mdl-35816508
BACKGROUND: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. METHODS: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). RESULTS: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. CONCLUSIONS: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dengue Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dengue Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article