EPI STIS - Remote Sensing tools to study the EPIdemiology and Space/TIme DynamicS of diseases

You are here

Context and objectives

Effective control of contagious diseases requires a good understanding of the space-time dynamics of transmission. Such a better understanding contributes to the development of effective strategies that support the prevention of disease outbreaks or, if outbreaks occur, contribute to the implementation of the most effective strategy to stop the spread. A wide range of earth observation products can play an important role in modelling and understanding the spatial and temporal aspects of the epidemiology of such contagious diseases at various resolutions and scales and contribute to decision making in their control. Two diseases (i.e. Bluetongue and Foot-and-Mouth Disease) were selected to serve as an example. They represent the important groups of vector-borne (Bluetongue) and highly contagious (Foot-and-Mouth Disease) viral diseases that can have devastating effects on the livestock sector. Both cases have important spatial and temporal connotations, are strongly data-driven and use similar remote sensing information sources. In addition, given the epidemiological differences mentioned above, they enabled EPISTIS to develop a wider range of spatial epidemiology and modelling approaches. The whole process was integrated into a Space and Time Information System (STIS) used as a decision support tool. The approaches that were developed and implemented are as much as possible generic and can be readily applied to other case studies (notably relating to human health).

Project outcome

• Establishment of a multidisciplinary group that strengthens the Belgian expertise on the international scene.

• Advances in image classification methods integrating ancillary data and expert knowledge, applied to heterogeneous landscapes

o OBIA for mapping a Mediterranean landscape using L/MRRS and HRRS data

o OBIA for mapping a Lowveld savanna landscape using HRRS and VHRRS data

• Complementary modelling approaches that integrate the space/time dynamics of disease transmission:

o Distribution model for C. imicola in Italy based on a time series of L/MRRS predictors

o Contribution of landscape predictors from HRRS data to the distribution model

o Spread models for C. imicola in Italy based on epidemiological surveillance data and genetic analysis.

o Predictive wind model for risk assessment of BTV spread in North-western Europe

o Distribution and density models for cattle and buffaloes (Kruger National Park)

o Models for stratifying the risk of FMD transmission at the livestock-cattle interface of the Kruger National Park (Bayesian spatio-temporal risk model, multiagent simulation, expert-based GIS risk model)

• Space/Time Information System (STIS) made up of three major components: database updating, computation (production of distribution maps and risk maps) and visualisation (web mapping).