Ward Bryssinckx

Ward Bryssinckx

Livestock Modeller
Bio: 

Belgian, MSc – Ward (1986) graduated in biomedical sciences in 2009. During two master years, which were focused on tropical biomedical sciences, he first came into contact with geographic information system which he used to model herd movement characteristics during daily grazing activities to assess contact risk at the livestock-wildlife interface. Regression models using field data and remote sensing imagery revealed a statistical significant correlation between area coverage by the herd and environmental data supplemented with herdsmen tracking data. During another internship, he identified mosquito species through DNA extraction and polymerase chain reactions at the Institute for Tropical Medicine.As of September 2009, he works at Avia-GIS. He is now in the final stage of completing his PhD study on the “Cost efficient modelling of denominator data for spatial epidemiological studies in extensive livestock systems”. The main goal of this study is to set general rules of thumb for non-probability methods of livestock enumeration while applying tailored data-processing techniques. Both spatial interpolation as an ensemble of direct estimates and spatial modelling were studied as possible ways to increase survey accuracy on a given spatial scale. Operational parameters and data processing requirements were assessed using R and ArcGIS 10. Custom raster-processing applications were developed in C++.Apart from this study, Ward conducts data processing, mapping and spatial analysis such as statistical inference and spatial and temporal cluster analysis in a variety of settings. Focus is on distribution of livestock and disease prevalence. The latter mainly covers zoonoses (infectious diseases which can affect both animals and humans) such as brucellosis, echinococcosis, bovine tuberculosis, cysticercosis, leishmaniasis and trypanosomiasis. During this work a lot of effort is put into questionnaire design, collection and pre-processing of data and storing the information in appropriate database structures. This is applied to both epidemiological and socio-economic studies.