Visualising Uncertainty in Disease Forecasting
Discuss, examine, and explore novel interactive visualisation schemes to help aid disease forecasting.
There are many models and techniques for disease forecasting over a huge range of spatial and temporal scales, using a wealth of potential data streams. This results in many different predictions of disease spread and effect on a population, with no clear indication of the accuracy of any one outcome. In combination with a wealth of possible intervention strategies, this leaves the decision maker information rich, but potentially unable to sift this information and provide a succinct evidence base for key decisions.
Visualising uncertainty in a way that is easily understood and which facilitates immediate action is a complex and application specific problem. There have been a number of research efforts in the recent past looking at this problem, particularly addressing how uncertainty should be presented for a decision maker. Visualising uncertainty from an ensemble of epidemiological models poses a number of particular challenges:
- The output needs to reflect the assumptions which make up the predictions.
- The output must enable high-level decisions as well as detailed analysis of specific model outputs.
- The output is defined over a spatio-temporal region, meaning there are multiple dimensions to be presented.
- The output must be visualised over a number of different modelling scales, from high level approximations to detailed simulation models and reflect the associated uncertainties.
Dstl Uncertainty Visualisation Study Group
KTN are organising a study group on behalf of Dstl focusing on exploring the state of the art around interactive visualisations for uncertain disease forecasts. Participants will be asked to discuss, examine, and explore (with the aid of representative datasets) novel interactive visualisation schemes around four themes:
- Communicating the assumptions underlying model predictions to help the decision maker understand the utility of the outputs.
- Decision aids that include data summaries and temporal predictions
- Spatio-temporal visualisation of hazard areas and populations at risk
- Visualising uncertainty in the numbers in ‚Äòat risk‚Äô groups due to multiple model predictions
Held in London, between the 23rd and 25th May, accommodation and food for the three days will be covered by the organisers for those invited and accepted to take part.
Why take part in the Study Group?
- Get exposure to new application areas and understand the impact of ongoing research.
- Gain access to novel example data sets and current state of the art approaches.
- Expand your research portfolio.
- Make vital contacts with industry and government research sponsors.
- Meet and connect with other academics from a wide variety of different fields.
- Help to solve problems that will have real world impact.
How to Register
This event is by invitation only. If you are interested in attending please contact Matt Butchers¬†(email@example.com, 07715082259) for further information.