The landscape epidemiology of echinococcoses

Abstract

Echinococcoses are parasitic diseases of major public health importance globally. Human infection results in chronic disease with poor prognosis and serious medical, social and economic consequences for vulnerable populations. According to recent estimates, the geographical distribution of Echinococcus spp. infections is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and over time it may be affected by global environmental change. Therefore, landscape epidemiology offers a unique opportunity to quantify and predict the ecological risk of infection at multiple spatial and temporal scales. Here, we review the most relevant environmental sources of spatial variation in human echinococcosis risk, and describe the potential applications of landscape epidemiological studies to characterise the current patterns of parasite transmission across natural and human-altered landscapes. We advocate future work promoting the use of this approach as a support tool for decision-making that facilitates the design, implementation and monitoring of spatially targeted interventions to reduce the burden of human echinococcoses in disease-endemic areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0109-x) contains supplementary material, which is available to authorized users.

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

“…Another emerging field where remote sensing is increasingly utilized to better understand human-environment dynamics is landscape epidemiology. Our search returned only one such paper, which reviews the use of remote sensing to predict risk of human infection at multiple spatial and temporal scales [92]. Remote sensing and other geospatial technologies allow researchers to explore in detail the landscape patterns that influence the transmission dynamics of an infectious disease at different spatiotemporal scales.…”

Statistical evaluation of spatial uncertainty in schistosomiasis mapping

“…A positional mismatch is present in the spatial modelling of leptospiriosis, echinococcosis, soil-transmitted helminths, among others (Cadavid Restrepo et al, 2016;Dhewantara et al, 2019). For instance, for leptospiriosis modelling, different wild or domestic animals carry the bacterium that can get into the soil/water and survive there even several months (Dhewantara et al, 2019).…”

“…In the absence of empirical data on infection at unsampled communities, one way to identify communities at risk is to study the role of the environment (physical and biological) to characterize potential habitats of parasites and intermediate hosts, as well as to understand the ecology and epidemiology of infections. Statistical modelling of the spatial distribution of helminth infections provides empirical relationships between infections and risk factors, which can then be used to predict the level of infection prevalence at unsampled locations (Cadavid Restrepo et al, 2016;Soares Magalhães et al, 2011b;Weiss et al, 2015). In the statistical model, prevalence or another morbidity indicator, is treated as the response variable.…”

“…prevalence or intensity of infection) and risk factors. Risk factors for schistosome exposure include various environmental and socio-economic covariates that help to interpolate the level of infection at unsampled locations (Cadavid Restrepo et al, 2016;Hamm, Soares Magalhães and Clements, 2015;Weiss et al, 2015). Covariates and morbidity indicators are commonly extracted from survey locations such as health centres, hospitals and schools.…”

“…Those could be extracted from Earth Observation (EO) data such as monitor sites or satellite imagery. In addition, EO data help to interpolate the level of infection towards unsampled locations (Cadavid Restrepo et al, 2016;Soares Magalhães et al, 2011b).…”