Abstract
Dengue is an important vector-borne pathogen found across much of the world. Many factors complicate our understanding of the relationship between infection with one of the four dengue virus serotypes, and the observed incidence of disease. One of the factors is a large proportion of infections appear to result in no or few symptoms, while others result in severe infections. Estimates of the proportion of infections that result in no symptoms (inapparent) vary widely from 8% to 100%, depending on study and setting. To investigate the sources of variation of these estimates, we used a flexible framework to combine data from multiple cohort studies and cluster studies (follow-up around index cases). Building on previous observations that the immune status of individuals affects their probability of apparent disease, we estimated the probability of apparent disease among individuals with different exposure histories. In cohort studies mostly assessing infection in children, we estimated the proportion of infections that are apparent as 0.18 (95% Credible Interval, CI: 0.16, 0.20) for primary infections, 0.13 (95% CI: 0.05, 0.17) for individuals infected in the year following a first infection (cross-immune period), and 0.41 (95% CI: 0.36, 0.45) for those experiencing secondary infections after this first year. Estimates of the proportion of infections that are apparent from cluster studies were slightly higher than those from cohort studies for both primary and secondary infections, 0.22 (95% CI: 0.15, 0.29) and 0.57 (95% CI: 0.49, 0.68) respectively. We attempted to estimate the apparent proportion by serotype, but current published data were too limited to distinguish the presence or absence of serotype-specific differences. These estimates are critical for understanding dengue epidemiology. Most dengue data come from passive surveillance systems which not only miss most infections because they are asymptomatic and often underreported, but will also vary in sensitivity over time due to the interaction between previous incidence and the symptomatic proportion, as shown here. Nonetheless the underlying incidence of infection is critical to understanding susceptibility of the population and estimating the true burden of disease, key factors for effectively targeting interventions. The estimates shown here help clarify the link between past infection, observed disease, and current transmission intensity.