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Remote Sensing tools to study the EPIdemiology and Space/TIme dynamicS of diseases (EPISTIS)

Research project SR/00/102 (Research action SR)

Persons :

Description :

Context and objectives

The role of remote sensing and spatial analysis and modelling in epidemiology becomes increasingly important. Up to now, a variety of remote sensing tools has been developed for use in epidemiological studies, but little effort has been made to fine-tune these tools, exploit their complementarity, integrate their outputs in state-of-the-art spatial information systems and test their robustness in a data-driven decision support environment. The general objective of this proposal is to strengthen the Belgian pool of expertise on this topic through jointly providing tools improving the analysis of the space-time dynamics of diseases transmission.

To achieve this, two highly dynamic themes have been selected:
(1)The presence and spread of bluetongue (and its main vector C. imicola) in Italy and in the Mediterranean Basin since 1998, which causes high economic losses due to both the disease in animals and banned animal movement. Key issues: which factors affect the establishment and spread of the vector and its associated pathogen, and is there a risk for further spread north. (2)The dynamics of pathogen transmission (foot & mouth disease) at the human/livestock/wildlife interface at the fringe of large wildlife conservation areas, such as the Great Limpopo Transfrontier Park in southern Africa. Key issues: interface typology, epidemiological hotspots and dynamic change drivers. These issues are addressed through a set of common approaches, and the final product will be an integrated system designed to produce risk maps.


First of all, the general epidemiological setting will be described. This includes relevant data on disease occurrence, vectors, susceptible hosts, wildlife, domestic livestock and human population. These data will be supplemented with ancillary data such as soil maps, DEMs, topographic indices, etc. Remote sensing data will provide an essential source of information, no only for the epidemiological setting, but also as input variables for spatially explicit models. An innovative multi-sensor and multi-scale approach will be adopted: low, medium, high and very-high resolution data will be processed and used as input for different types of models that operate on different scales: distribution models on a country and regional scale, landscape models and spread models on a local scale, and very-high resolution data on an even more detailed scale enabling fine-tuning in areas where this is required. All the models will be integrated in a space/time information system (STIS). This will allow further analysis of the various model outputs in a wider epidemiological setting in a much more integrated way than before, the aims being to estimate the risk for future spread, to show the efficacy of the existing barriers and to improve disease management and control.

Results expected

- Establishment of a (Belgian) EPISTIS network of expertise in the field of spatial technologies applied to epidemiology
- Detailed comparison of statistically and non-statistically based classifier systems (M/LRRS – distribution models) including the assessment of their respective practical use (significant contribution to the state of the art).
- Innovative distribution models for vector, virus, and host (no examples in this context of multi-sensor and multiscale approach known to date for BT).
- Innovative spread models for vector and virus using inputs from L/MRRS and H/VHRRS (no examples in this context of multi-sensor and multiscale approach known to date for BT and FMD).
- Innovative landscape models based on high and very high resolution sensors; this also includes innovative landscape measures based on very high resolution sensors (no examples in this context of multi-sensor and multiscale approach known to date for BT and FMD).
- Integrated Space and Time Information System (STIS) including a set of generic tools for disease modelling based on earth observation data and multi-criteria analysis tools (no examples in this context of multi-sensor and multiscale approach known to date).

Documentation :

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