Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries

Abstract

Background: 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.

Methodology/principle findings: 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.

Conclusions/significance: 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.

Fig 1. Flowchart describing the literature search process for age-stratified incidence data.

Fig 2. Total force of infection and corresponding R₀ estimates from the model fitted to the incidence data grouped by country.

Each dot represents the posterior median estimate and the error bars show the 95% CrI for each dataset. The box represents the country-specific central estimate calculated by taking the mean values of the MCMC output for each country (the line and limits of the box represents the posterior median and the 95% CrI respectively). R₀ assumption one: complete protection acquired upon quaternary infection, assumption two: complete protection reached after secondary infection.

Fig 3. Summary of estimated reporting rates showing the baseline reporting rate or probability of detecting a secondary infection (ρ), the probability of detecting a primary infection (γ1) relative to a secondary infection, and the probability of detecting a tertiary/quaternary infection (γ3) relative to a primary infection.

Each point represents the posterior median estimate and the error bars show the 95% CrI for each dataset. The box represents the country-specific central estimate calculated by taking the mean values of the MCMC output for each country (the line and limits of the box represents the posterior median and the 95% CrI respectively). A single overall value of γ1 and γ3 were estimated per country.

Fig 4. Comparison of weighted deming regression of force of infection estimates by country from cumulative incidence data and seroprevalence data.

Each point is weighted depending on the error in both serology and incidence estimates, represented by the size of circles (larger circles indicating greater weight, i.e. smaller error).

Fig 5. Posterior median estimates of the total force of infection from the model fitted to incidence data (model 1) and model A (as described in [13]) to age-stratified seroprevalence data (serology) from Thailand where incidence and serology data were available from the same year and location.