title: Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.
abstract: Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions.
  
  
  
  We estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (<25%) and varied within and between countries.
  
  
  
  As expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.
authors.affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
authors.forename: Natsuko
authors.initials: N
authors.lastname: Imai
authors.affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
authors.forename: Ilaria
authors.initials: I
authors.lastname: Dorigatti
authors.affiliation: Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Cauchemez
authors.affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
authors.forename: Neil M
authors.initials: NM
authors.lastname: Ferguson
mesh_terms: D003715:Dengue / Q000635:transmission*; D018563:Disease Notification; D014943:Global Health; D006801:Humans; D008954:Models, Biological