Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

Abstract

The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of internode EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.

Field Detection of Schistosoma japonicum Cercariae in Environmental Water Samples by Quantitative PCR

“…Currently an estimated 800,000 humans are infected with S. japonicum, and 30 million people remain at risk in the People's Republic of China (11,22), contributing to substantial ill health (8), developmental effects (6,13), and socioeconomic impacts (10,21). With the success of China's mass chemotherapeutic control campaigns, difficulties detecting increasingly rare and low-concentration transmission sites are posing challenges to achieving elimination (16). Traditional environmental detection methods, including microscopy-based techniques and animal bioassays, are of limited utility for surveillance.…”

Genetic Evidence of Contemporary Dispersal of the Intermediate Snail Host of Schistosoma japonicum: Movement of an NTD Host Is Facilitated by Land Use and Landscape Connectivity

“…[15]. Moreover, classifications of landscape types were themselves limited by the accuracy of classification procedures.…”

“…The average village in the region had approximately 80 people over 6 years of age (range 30-170 people), based on a census conducted in 2007 [36]. The region is characterized by mountainous terrain and intense agricultural cultivation, particularly rice and vegetable crops [15]. Agricultural practices rely on irrigation networks that may facilitate the dispersal of the intermediate host.…”

“…The cost of every possible path between each pair of sites across the study region was calculated, yielding a distribution of costs as described elsewhere [15]. The minimum value of this distribution is generally taken as a summary measure of effective distance between sites, yet some have argued that such minima are insufficient to characterize landscape connectivity across complex landscapes [41–43].…”

“…Percentage of land cover devoted to agriculture was determined using Landsat and IKONOS imagery according to classification schemes described previously [15]. A one kilometer buffer was rendered surrounding each village center in ArcGIS [37], and each cell within the buffer was characterized as either agricultural or non-agricultural (flowing water, barren, forest, built, other).…”