Pseudorabies virus infections in wild boar: data visualisation as an aid to understanding disease dynamics.
Spatio-temporal survey data often form the main decision support in disease management. Consequently, disease managers rely on adequate description of the data supplied from epidemiological analysis. Practically speaking, such a description comprises of a visualisation, a deduced hypothesis and a statistical evaluation of the hypothesis. Prior to the application of traditional methods of data visualisation and hence data description, an aggregation of the data is required. This paper illustrates the resulting uncertainty in hypotheses that can be generated by traditional analytical techniques. Additionally, a method of avoiding this typical drawback in traditional data exploration is demonstrated: This is the use of data movies. Data from a 10-year study on the spread of pseudorabies virus (PrV) infections in wild boar populations were revisited. The visualisation of the data using a data movie instead of using traditional approaches for the first time enabled immediate observation of spreading infection in time and space. This dynamic perception of the spread drastically changes the understanding of the epidemiology of the virus. The paper demonstrates how data movies can account for the complete information contained within the data set. It is concluded that data movies can provide a more appropriate starting point. We recommend the data movie approach as an important addition in the epidemiological toolbox, because data movies can easily be integrated into the usual demonstration and publication process.
Thulke
Hans-Hermann
HH
Department of Ecological Modelling, UFZ, Centre for Environmental Research Leipzig-Halle, P.O. Box 500136, 04301 Leipzig, Germany. hans.thulke@ufz.de
Selhorst
Thomas
T
Müller
Thomas
T
D000818:Animals; D000835:Animals, Wild; D003626:Data Display; D005858:Germany / Q000453:epidemiology; D011557:Pseudorabies / Q000453:epidemiology*; D013552:Swine; D013553:Swine Diseases / Q000453:epidemiology*