Vector-borne Infections: risk based and Cost Efficient surveillance systems (VICE)

The overall aim is to develop a fully functional framework for a dynamic risk-based surveillance for VBD according to EMIDAs specific topic A5. The potential risk of outbreaks of VBD and consequently the spread within the population at risk will be continuously estimated. This will permit 1) active surveillance to be focused on specific periods and areas of elevated risk and 2) active surveillance activities to be fitted to the available resources at EU and national level.

The proposed surveillance system will be based on computer models scanning and interpreting risk parameters already collected for other purposes (weather data, environmental data, import data, syndromes) and costs may therefore be very low. Only during time periods and in areas of elevated risk will costly active surveillance activities gradually be suggested based on cost-effectiveness estimates and the desired sensitivity. This framework for continuous risk based surveillance will be based on three independent pillars: (1) risk of introduction, (2) potential for spread if introduced and (3) syndrome surveillance. The sensitivity of the system will be evaluated with scenario trees.

The surveillance system will be built on present knowledge of disease biology and vector ecology. The surveillance system will be modular allowing for easy and continuous updating of the underlying models whenever new or more precise information becomes available. Using selected VBD we will demonstrate how risk and hence the need for active surveillance can be communicated as dynamic maps of risk displayed on the internet at a weekly resolution.

We will also demonstrate the surveillance system on historic data for a 30-year period to determine the average risk and hence cost of surveillance. This will assist decision makers at national and EU level to estimate long-term expenses for surveillance in various regions. Furthermore we will make spatially specific 50-year prognoses based on existing climate change predictions.