Extrinsic incubation period models.

<p>(A–D) Vertical lines indicate the observed censored EIP observations (black for interval-censored and grey for right-censored) at each temperature (with added variability in temperature to improve visualization for observations at the same temperature). Thick solid lines and shaded areas indicate the mean and middle 95%, respectively, of the distribution for each fitted model (red: exponential; blue: Weibull; orange: gamma; and black: log-normal). (E) The lines indicate the predicted probability density for each model at 30°C.</p

Statistical distributions.

<p>Statistical distributions.</p

EIP model sensitivity to right-censored observations.

<p>The log-normal model is shown as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050972#pone-0050972-g001" target="_blank">Figure 1D</a>. The dotted and dashed lines are the comparable predicted mean and middle 95% range, respectively, for the model when the right-censored data is omitted.</p

The Incubation Periods of Dengue Viruses

<div><p>Dengue viruses are major contributors to illness and death globally. Here we analyze the extrinsic and intrinsic incubation periods (EIP and IIP), in the mosquito and human, respectively. We identified 146 EIP observations from 8 studies and 204 IIP observations from 35 studies. These data were fitted with censored Bayesian time-to-event models. The best-fitting temperature-dependent EIP model estimated that 95% of EIPs are between 5 and 33 days at 25°C, and 2 and 15 days at 30°C, with means of 15 and 6.5 days, respectively. The mean IIP estimate was 5.9 days, with 95% expected between days 3 and 10. Differences between serotypes were not identified for either incubation period. These incubation period models should be useful in clinical diagnosis, outbreak investigation, prevention and control efforts, and mathematical modeling of dengue virus transmission.</p> </div

Intrinsic incubation period models.

<p>The vertical bars are a histogram of the uncensored IIP data. Horizontal grey lines indicate interval-censored observations from the pre-1940 dataset (those which extend outside of the plot area are labeled with the interval maximum). The curves are the estimated IIPs for each model when fitted to the pre-1940 dataset.</p

Intrinsic incubation period models fitted with pre-1940s data.

<p>Intrinsic incubation period models fitted with pre-1940s data.</p

Extrinsic incubation period models.

†<p>without right-censored data, DIC is not comparable as the number of observations is different.</p

Intrinsic incubation period models and datasets.

<p>The thick solid lines indicate the estimated probability distributions using the complete dataset. The dashed and dotted lines indicate the estimate distributions using the pre-1940 and post-1970 subsets, respectively.</p

Immune status alters the probability of apparent illness due to dengue virus infection: Evidence from a pooled analysis across multiple cohort and cluster studies

<div><p>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.</p></div

Estimated risk of infection from cluster studies.

<p>The probability of infection in the time of follow up for those in the cluster around an index case (Analysis C).</p