title: Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries.
abstract: BACKGROUND
  Estimates of dengue transmission intensity remain ambiguous. Since the majority of infections are asymptomatic, surveillance systems substantially underestimate true rates of infection. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing both the burden of disease from dengue and the likely impact of interventions.
  
  
  RESULTS
  The force of infection (λ) and corresponding basic reproduction numbers (R0) for dengue were estimated from non-serotype (IgG) and serotype-specific (PRNT) age-stratified seroprevalence surveys identified from the literature. The majority of R0 estimates ranged from 1-4. Assuming that two heterologous infections result in complete immunity produced up to two-fold higher estimates of R0 than when tertiary and quaternary infections were included. λ estimated from IgG data were comparable to the sum of serotype-specific forces of infection derived from PRNT data, particularly when inter-serotype interactions were allowed for.
  
  
  CONCLUSIONS
  Our analysis highlights the highly heterogeneous nature of dengue transmission. How underlying assumptions about serotype interactions and immunity affect the relationship between the force of infection and R0 will have implications for control planning. While PRNT data provides the maximum information, our study shows that even the much cheaper ELISA-based assays would provide comparable baseline estimates of overall transmission intensity which will be an important consideration in resource-constrained settings.
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: D050936:Basic Reproduction Number; D003715:Dengue / Q000453:epidemiology* / Q000635:transmission*; D003716:Dengue Virus / Q000276:immunology; D004797:Enzyme-Linked Immunosorbent Assay; D014943:Global Health; D006801:Humans; D015995:Prevalence; D016036:Seroepidemiologic Studies