119 research outputs found

    Estimated risk of infection from cohort studies.

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    <p>The yearly probability of infection in the cohort studies for each study year (from Analysis A all studies together).</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

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    <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 probability of apparent disease given infection by study.

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    <p>Probability densities of estimates for the apparent proportion in primary (i) and secondary (ii) infection for each study (Analysis A for each study separately).</p

    Estimated risk of infection from cluster studies.

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

    Overall estimated probability of apparent disease given infection.

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    <p>(i) Probability densities of estimates of the apparent proportion in primary and secondary infections from cohort studies (Analysis A). (ii) Probability densities of estimates including a period of cross-immunity (Analysis D). (iii) Probability densities of estimates from cluster studies (Analysis C). For (i) and (iii) estimates for primary infection shown in green, secondary infection in orange and for (ii) estimates for primary infection shown in green, secondary infections in the year after infection in brown and secondary infections in the subsequent years in orange.</p

    Studies from which data was extracted for the analysis.

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    <p>HI: Haemagglutination inhibition, PRNT: Plaque reduction neutralisation titre, ELISA: Enzyme linked immunosorbent assay.</p

    Estimated probability of apparent disease given infection by serotype.

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    <p>Probability densities of estimates for the apparent proportion in primary (i) and secondary (ii) infection across serotypes (Analysis B).</p

    Measuring Spatial Dependence for Infectious Disease Epidemiology - Fig 1

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    <p>The transmission kernel determines the spatial area where directly transmitted cases may be found (area of elevated risk, red circle in <b>(B)</b>). In addition, due to branching in the transmission chain there may be a larger area of elevated prevalence from more distally related cases (area of elevated prevalence, blue circle in <b>(A)</b> and <b>(B)</b>). <b>(C)</b> Example transmission chains. Each color represents a transmission chain of a different strain. The strains are divided into two serotypes, with the blue and red circles representing one serotype and the green and yellow squares a second serotype. <b>(D)</b> Increasing ability to discriminate between different transmission chains will increase the power of the Ï„-statistic to identify the spatial extent of elevated prevalence. Where case-pairs are known, we can identify the area of elevated risk (black line).</p

    Overview of simulated epidemics of 100 separate transmission chains.

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    <p>Each transmission chain consisted of cases caused by a single strain, with all strains divided into four serotypes. <b>(A)</b> Distribution of underlying population for simulated data. The black square represents the area of analysis to avoid edge effects from the simulation process. The two black dots represent two ‘surveillance hospitals’ (relevant for the spatially-biased observation scenario set out in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155249#pone.0155249.g003" target="_blank">Fig 3</a>). <b>(B)</b> Using disease type information (blue) allow the τ-statistic to correctly show no spatial clustering of disease in a spatially clustered population, whereas the pair correlation function would falsely indicate clustering in disease cases. <b>(C)</b> τ-statistic results from simulated epidemics where the cases infected individuals between 0 and 100m away. Each case infected one individual (effective reproductive number of one). <b>(D)</b> As (C) but each case infects two individuals (effective reproductive number of two).</p

    Performance of the Ï„-statistic under different observation scenarios: complete observation (blue solid line), spatially random observation (dashed blue line) and spatially biased observation (purple line).

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    <p>For the spatially random observation all cases had a 1% probability of being observed. For the partially observed observation, the probability of observation was 0.1xexp(-<i>d</i>), where <i>d</i> was the distance (in km) to the closest ‘surveillance hospital’ in the area (marked by two black dots in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155249#pone.0155249.g002" target="_blank">Fig 2A</a>).</p
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