Spatial Modelling Tools to Integrate Public Health and Environmental Science, Illustrated with Infectious Cryptosporidiosis.

Aparna Lal

doi:10.3390/ijerph13020186

Abstract

Contemporary spatial modelling tools can help examine how environmental exposures such as climate and land use together with socio-economic factors sustain infectious disease transmission in humans. Spatial methods can account for interactions across global and local scales, geographic clustering and continuity of the exposure surface, key characteristics of many environmental influences. Using cryptosporidiosis as an example, this review illustrates how, in resource rich settings, spatial tools have been used to inform targeted intervention strategies and forecast future disease risk with scenarios of environmental change. When used in conjunction with molecular studies, they have helped determine location-specific infection sources and environmental transmission pathways. There is considerable scope for such methods to be used to identify data/infrastructure gaps and establish a baseline of disease burden in resource-limited settings. Spatial methods can help integrate public health and environmental science by identifying the linkages between the physical and socio-economic environment and health outcomes. Understanding the environmental and social context for disease spread is important for assessing the public health implications of projected environmental change.

Highlights

Contemporary spatial modelling tools can help examine how environmental exposures such as climate and land use together with socio-economic factors sustain infectious disease transmission in humans
Spatial approaches are useful for analyzing the associations between area-level environmental exposures, such as climate and proximity to livestock, and individual-level risk factors, such as age, susceptibility and disease risk
Spatial models can partition out the impact of environmental, socio-economic and demographic variables and individual level risk factors to quantify their relative contribution to disease incidence

Summary

Environmental Change and Infectious Disease Spread

Especially climate change and human exploitation of productive ecosystems have become increasingly important drivers of infectious disease spread [1,2]. Initial analyses using these descriptive tools can provide important insights into the spatial nature of disease spread Environmental exposures such as climate and land use are spatially dependent (i.e., individuals closer together are more likely to have a similar exposure than individuals further apart). A recent extension of cluster detection methods that allows the spatial autocorrelation structure and the continuity of the exposure to be modelled is the exceedance probability in a Bayesian framework This approach identifies areas where the probability of the relative risk of disease would exceed a specified threshold. While this technique is dependent on the precision of relative risk estimates, it offers a more flexible and conservative approach to spatial cluster detection and is useful to model environmental exposures [12,13]

Challenges

Opportunities

Cryptosporidiosis: A Case Study

Findings

Conclusions