The Spatial Dynamics of Dengue Virus in Kamphaeng Phet, Thailand

Abstract

BackgroundDengue 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 FindingsWe 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.ConclusionsThere 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.

Synchrony of Dengue Incidence in Ho Chi Minh City and Bangkok

“…Previous work using case data has focused on the spread of dengue within urban or rural communities [7–9] or the dispersion of the virus across a country [6]. These studies have demonstrated micro-scale spatial dependence between cases at distances of under one kilometer in both rural Northern Thailand and Bangkok suggesting focal transmission in these settings but also waves of dengue incidence coming out of Bangkok [7, 8, 10].…”

Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918

“…The ϕ clustering statistic [ϕ(d 1 , d 2 )] is defined as the probability of two deaths occurring in a defined spatial and temporal window relative to the independent probabilities of observing two deaths within the spatial window or within the temporal window (29,30) ϕðd 1 , d 2 Þ = Prðj ∈ Ω i ðd 1 , d 2 , t 1 = 0, t 2 = 1ÞÞ Prðj ∈ Ω i ðd 1 , d 2 , · ÞÞPrðj ∈ Ω i ð · , t 1 = 0, t 2 = 1ÞÞ , where Ω i (d 1 , d 2 , t 1 = 0, t 2 = 1) is the set of all influenza deaths occurring both within the (d 1 , d 2 ) spatial window of case i and within 1 wk (t 1 = 0, t 2 = 1) of case i. Window size (d 2 − d 1 ) was kept constant at 100 m, and d 2 ranged from 100 m to 2.5 km; 95% CIs were calculated using the 2.5 and 97.5 percentiles of 1,000 bootstrap samples.…”

“…1). We used the spatiotemporal clustering statistic, ϕ(d 1 , d 2 ), to characterize spatial and temporal dependence of individual deaths (28,29). This statistic estimates the probability of any two deaths occurring within the same week and within a spatial window of one another relative to the expectation if spatial clustering and temporal clustering were independent (Methods).…”

Estimating infectious disease transmission distances using the overall distribution of cases

“…We rarely observe infection pairs (i.e., who infected whom) in a transmission network. Where only a minority of cases are observed, analyses tend to be restricted to characterizing the spatial and temporal scales at which cases tend to occur together but the relationship between spatial clustering and transmission distance is complex (Bhoomiboonchoo et al, 2014; Grabowski et al, 2014; Lin et al, 2011; Morrison et al, 1998; Salje et al, 2015; 2012). Only where we have been able to observe the majority of cases in a transmission network or we have detailed epidemiological data on who infected whom, has estimation of mean transmission distances previously been possible (Assiri et al, 2013; Ferguson et al, 2001a; Keeling et al, 2004).…”