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Spatial modelling of disease using data- and knowledge-driven approaches. | LitMetric

Spatial modelling of disease using data- and knowledge-driven approaches.

Spat Spatiotemporal Epidemiol

Veterinary Epidemiology and Public Health Group, Department of Veterinary Clinical Sciences, Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Hertfordshire AL9 7TA, UK.

Published: September 2011

AI Article Synopsis

  • - The main goals of spatial modeling in animal and public health include identifying current risk patterns, understanding the biological reasons for disease emergence, and predicting future occurrences in different areas or timeframes.
  • - Traditional predictive models like GLM and GAM require both presence and absence data for diseases, which can be difficult to gather, while newer methods like MAXENT and GARP only need presence data.
  • - This review discusses these novel modeling techniques, their strengths and weaknesses, and showcases research that has successfully applied them in mapping disease distributions.

Article Abstract

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.sste.2011.07.007DOI Listing

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