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Controlling infectious disease is one of the primary goals of disease ecology, according to the existing definition (Verity et al. 1). However, because of a comparatively rapid pace of new strain of viruses’ development, the necessity to come up with new approaches towards disease control emerges.
Though being a very challenging task the process of disease control may be improved extensively with the help of the method known as spatial targeting, as it allows for creating a map of infectious disease contraction, thus, facilitating the process of diseases geographical location.
When Spatial Targeting Is Used
Though created comparatively recently, the method of spatial targeting has already become a popular tool in carrying out major investigations concerning the effects of specific diseases; as a result, the specified approach has become popular and is nowadays used on a regular basis (Verity et al. 1).
As far as the areas of spatial targeting use are concerned, the approach of spatial targeting is used for identifying, controlling, avoiding and preventing infectious diseases (Verity et al. 2). In addition, spatial targeting is a popular tool in locating invasive species (Verity et al. 2).
How Spatial Targeting Is Used
The application of spatial targeting is rather basic, yet it is often viewed as complex, since it ties in two concepts, i.e., ecology and geography (Verity et al. 1). As a result, the possibility for identifying multiple sources of a specific disease that used to be unknown previously emerges. Traditionally, several mathematical models for carrying out a geographic analysis of a specific area are suggested for the spatial targeting procedure.
The GGT and the Bayesian methods are traditionally used as the key tool in carrying out the type of analysis in question. According to the existing set of standards, a distance-decay function is created around the given individual data points. The highest point is supposed to signify the level of confidence about the security of a specific data point. The lowest point on the graph, in its turn, allows for locating the lack of certainty regarding a specific issue related to the topic in question (Verity et al. 3).
Speaking of the second approach, i.e., the adoption of the Bayesian method, the probability of data based on the location of each resource is to be identified prior to the analysis of the information provided. Afterwards, the posterior distribution of the sources based on their location is carried out (Verity et al. 3).
Finally, the method known as the Dirichlet deserves a mentioning (Verity et al. 3). On the one hand, the specified approach does not offer anything new, as it is largely based on the Bayesian approach. On the other hand, it encompasses the existing phenomena in the so-called “cluster,” i.e., it allows for an opportunity to not only analyze a specific phenomenon, but also to create a rule based on the outcomes of the observation.
Therefore, it can be considered that the spatial targeting method as a method of disease ecology is quite promising as the approach to identifying the tendencies in disease development, the potential health concerns, the speed of virus evolution, etc. though the concept of spatial targeting is comparatively new, it has already become rather popular among researchers as an efficient tool in identifying and preventing epidemics, which means that spatial targeting is bound to become an important tool in the array of methods adopted in disease ecology researches.
Verity, Robert, Mark D. Stevenson, D. Kim Rossmo, Richard A. Nichols and Steven C. Le Comber. “Spatial Targeting of Infectious Disease Control: Identifying Multiple, Unknown Sources.” Methods in Ecology and Evolution 5.92 (2014), 1–26. Print.