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Multiscale and multisensor modeling of the spatial distribution of tick-borne diseases (MULTITICK)

Onderzoeksproject SR/10/123 (Onderzoeksactie SR)

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The spatial distribution of vector-borne diseases is tied to environmental conditions in two ways. First, vectors will only thrive under certain habitat conditions. These can be defined in terms of appropriate microhabitats, but also in terms of climatic ranges. While factors relating to microhabitats are best documented at the local scale, climatic ranges are best described at the regional scale. Second, given a certain vector and pathogen distribution, for the disease to be transmitted to humans, there must be an overlap with the spatial distribution of human activities. Land use will often reflect accurately the spatial distribution of human activities, which both influence and are influenced by the landscape.Tick-borne diseases are currently the most important vector-borne diseases in Europe. A dramatic upsurge in the incidence of tick-borne encephalitis was observed during the 1990s. Lyme borreliosis currently continues to increase in many European countries. Many explanations have been suggested for the observed increases, but a thorough, quantitative explanation remains to be found. Climate trends have been widely incriminated, but recent evidence indicates that it cannot account for the diverse dynamics found across regions.


Both low- and high-resolution data have been successful at mapping either vectors or disease cases. However, the knowledge that is currently available on environmental factors dictating vector or disease distribution indicates that factors acting at more than one scale should be considered. Using the example of tick-borne diseases in Europe, we propose to fill this gap in the use of remotely sensed data in spatial epidemiology. In order to achieve this, we plan on using statistical techniques that allow combining nested, hierarchical data.


The expected scientific outcomes of the project are:

- the demonstration of the use of multilevel statistical modelling for the analyses of remotely sensed data, and of remotely sensed data with data from other sources, such as disease incidence.
- a better understanding of the influence of environmental factors on vector-borne diseases incidence, and, particularly, the scale of action of the environmental factors that are the most influent.
- an improved use of remotely sensed data in spatial epidemiology, for which combining high- and low-resolution has not been achieved yet.
- an improved understanding of the environmental aspects of the etiology of two highly concerning European vector-borne diseases, carried by the main European disease vector, the tick.

Other significant outcomes of the project will include:

- the strengthening of the Belgian expertise in remote sensing and spatial epidemiology

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