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. 2015 Apr 16;9(4):e0003719.
doi: 10.1371/journal.pntd.0003719. eCollection 2015 Apr.

Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries

Affiliations

Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries

Natsuko Imai et al. PLoS Negl Trop Dis. .

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.

Methodology/principal findings: 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/significance: 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart describing the literature search process for dengue seroprevalence surveys.
Fig 2
Fig 2. A) Force of infection and B) corresponding R0i estimates of cross-sectional non-serotypes specific datasets fitted to Model A.
Posterior median and 95% credible intervals shown.
Fig 3
Fig 3. Estimated time-varying A) serotype-specific force of infection in individuals under the threshold age and B) R0i derived by fitting Model C to Nicaraguan data (2001–2007).
Posterior median and 95% credible intervals shown.
Fig 4
Fig 4. Serotype-specific estimates of A) force of infection, λ i, and B) R 0i estimates derived from PRNT datasets fitted to Model D2.
Posterior median and 95% credible intervals shown.
Fig 5
Fig 5. Total force of infection (λ) estimates (for all 4 serotypes) derived from PRNT datasets fitted to Models A (treating PRNT data as IgG data) and D1–D4.
Models D2–D4 allow for cross-protection between serotypes. Posterior median and 95% credible intervals shown.

References

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