Sequential dengue virus infections detected in active and passive surveillance programs in Thailand, 1994-2010

BACKGROUND: The effect of prior dengue virus (DENV) exposure on subsequent heterologous infection can be beneficial or detrimental depending on many factors including timing of infection. We sought to evaluate this effect by examining a large database of DENV infections captured by both active and passive surveillance encompassing a wide clinical spectrum of disease. METHODS: We evaluated datasets from 17 years of hospital-based passive surveillance and nine years of cohort studies, including clinical and subclinical DENV infections, to assess the outcomes of sequential heterologous infections. Chi square or Fisher\u27s exact test was used to compare proportions of infection outcomes such as disease severity; ANOVA was used for continuous variables. Multivariate logistic regression was used to assess risk factors for infection outcomes. RESULTS: Of 38,740 DENV infections, two or more infections were detected in 502 individuals; 14 had three infections. The mean ages at the time of the first and second detected infections were 7.6 +/- 3.0 and 11.2 +/- 3.0 years. The shortest time between sequential infections was 66 days. A longer time interval between sequential infections was associated with dengue hemorrhagic fever (DHF) in the second detected infection (OR 1.3, 95% CI 1.2-1.4). All possible sequential serotype pairs were observed among 201 subjects with DHF at the second detected infection, except DENV-4 followed by DENV-3. Among DENV infections detected in cohort subjects by active study surveillance and subsequent non-study hospital-based passive surveillance, hospitalization at the first detected infection increased the likelihood of hospitalization at the second detected infection. CONCLUSIONS: Increasing time between sequential DENV infections was associated with greater severity of the second detected infection, supporting the role of heterotypic immunity in both protection and enhancement. Hospitalization was positively associated between the first and second detected infections, suggesting a possible predisposition in some individuals to more severe dengue disease

The spatial dynamics of dengue virus in Kamphaeng Phet, Thailand.

BACKGROUND: Dengue is endemic to the rural province of Kamphaeng Phet, Northern Thailand. A decade of prospective cohort studies has provided important insights into the dengue viruses and their generated disease. However, as elsewhere, spatial dynamics of the pathogen remain poorly understood. In particular, the spatial scale of transmission and the scale of clustering are poorly characterized. This information is critical for effective deployment of spatially targeted interventions and for understanding the mechanisms that drive the dispersal of the virus. METHODOLOGY/PRINCIPAL FINDINGS: We geocoded the home locations of 4,768 confirmed dengue cases admitted to the main hospital in Kamphaeng Phet province between 1994 and 2008. We used the phi clustering statistic to characterize short-term spatial dependence between cases. Further, to see if clustering of cases led to similar temporal patterns of disease across villages, we calculated the correlation in the long-term epidemic curves between communities. We found that cases were 2.9 times (95% confidence interval 2.7-3.2) more likely to live in the same village and be infected within the same month than expected given the underlying spatial and temporal distribution of cases. This fell to 1.4 times (1.2-1.7) for individuals living in villages 1 km apart. Significant clustering was observed up to 5 km. We found a steadily decreasing trend in the correlation in epidemics curves by distance: communities separated by up to 5 km had a mean correlation of 0.28 falling to 0.16 for communities separated between 20 km and 25 km. A potential explanation for these patterns is a role for human movement in spreading the pathogen between communities. Gravity style models, which attempt to capture population movement, outperformed competing models in describing the observed correlations. CONCLUSIONS: There exists significant short-term clustering of cases within individual villages. Effective spatially and temporally targeted interventions deployed within villages may target ongoing transmission and reduce infection risk

The spatial dynamics of dengue virus in Kamphaeng Phet, Thailand.

BACKGROUND: Dengue is endemic to the rural province of Kamphaeng Phet, Northern Thailand. A decade of prospective cohort studies has provided important insights into the dengue viruses and their generated disease. However, as elsewhere, spatial dynamics of the pathogen remain poorly understood. In particular, the spatial scale of transmission and the scale of clustering are poorly characterized. This information is critical for effective deployment of spatially targeted interventions and for understanding the mechanisms that drive the dispersal of the virus. METHODOLOGY/PRINCIPAL FINDINGS: We geocoded the home locations of 4,768 confirmed dengue cases admitted to the main hospital in Kamphaeng Phet province between 1994 and 2008. We used the phi clustering statistic to characterize short-term spatial dependence between cases. Further, to see if clustering of cases led to similar temporal patterns of disease across villages, we calculated the correlation in the long-term epidemic curves between communities. We found that cases were 2.9 times (95% confidence interval 2.7-3.2) more likely to live in the same village and be infected within the same month than expected given the underlying spatial and temporal distribution of cases. This fell to 1.4 times (1.2-1.7) for individuals living in villages 1 km apart. Significant clustering was observed up to 5 km. We found a steadily decreasing trend in the correlation in epidemics curves by distance: communities separated by up to 5 km had a mean correlation of 0.28 falling to 0.16 for communities separated between 20 km and 25 km. A potential explanation for these patterns is a role for human movement in spreading the pathogen between communities. Gravity style models, which attempt to capture population movement, outperformed competing models in describing the observed correlations. CONCLUSIONS: There exists significant short-term clustering of cases within individual villages. Effective spatially and temporally targeted interventions deployed within villages may target ongoing transmission and reduce infection risk

Population characteristics.

<p>Population characteristics of patients admitted to Kamphaeng Phet Provincial Hospital between 1994 and 2008.</p><p>Population characteristics.</p

Spatial and temporal distribution of cases that presented at KPPH (1994–2008).

<p>(A) Map of case locations. The red circles mark the village clusters with at least 40 cases. (B) Total number of cases per month.</p

Short-term spatial dependence between cases.

<p>Spatial dependence between cases occurring within the same month as measured through <i>φ(d₁, d₂)</i> where <i>d₁</i> and <i>d₂</i> is the distance range between cases. The spatial range (<i>d₂−d₁</i>) was kept constant at 1 km when <i>d₂</i> was greater than 1 km. When <i>d₂</i> was less than 1 km, <i>d₁</i> was equal to zero. Estimates are plotted at the midpoint of the spatial ranges.</p

Model coefficients.

<p>Exponentiated coefficients estimates and 95% confidence intervals for the models set out in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003138#pntd-0003138-t002" target="_blank">Table 2</a>.</p>(a)<p>Mean from 500 resamples.</p><p>Model coefficients.</p

Model summary.

<p>Overview of the different models used to estimate the correlation in the epidemic curves between pairs of village clusters in Kamphaeng Phet.</p><p>Model summary.</p