Exploratory Visualization of Infectious Disease Propagation Ben Houston, Neuralsoft Zack Jacobson, Health Canada NX-Workshop on Social Network Analysis and Visualization for Public Safety 18 – 19 October 2005

Introduction Goal: Deliver a disease propagation modeling application based roughly on the VITA NetViz information visualization component Focused on application development. Theory is an important, but secondary, aid.

Approach Rapid application development –Evolutionary, circular development model. –Regular client/customer demonstration w/ feedback. –Don’t over think problems, focus on presenting possible solution. –Often the effective solutions are only apparent once your client sees aspects of it in front of them.

Technology Language: Microsoft C# Graphics: GDI+ or OpenGL or Direct3D Mathematics: custom library

Simulation Components Agents – one agent per “individual”, intrinsic characteristics: activity level, sociability, resiliency, dynamic attributes: health, energy level, position, disease state. Environment – infinite 2-dimensional plane. Agents are free to move. Agent-agent proximity is used to model disease exposure and transmission. Disease Model – a state transition graph. Interaction between the agent’s intrinsic and dynamic attributes determines the progression of the disease (the speed and path through disease’s state transition graph.)

Simulation Initialization –Population model used to initialize the intrinsic characteristics of each of the agents. Specifies “distribution” of characteristics. –Disease model – a general disease model which via parameters specification can be used to represent diverse specific diseases.

Running the Simulation All data produced during the simulation is captured in an efficient compressed form in real time.

Interactive Visualization 3 Complementary Views Population Snapshot View -- Overhead view of a 2D plane. The user can scroll through time to inspect specific times of interest. Agent-Agent Transmission Graph – The implicit social network created by tracking agent-agent infections. Population Attribute vs. Time Charts -- Population statistics. Useful for finding minimal, maximal points or slopes.

Model Parameter Specification

Interactive Visualization 3 separate synchronized views which each allow intuitive access to different data dimensions. Currently, only one view can be displayed on the screen at a time, but simultaneous views may be in the future.

Interactive Visualization - 1 Population Snapshot View– Overhead view of a 2D plane. The user can scroll through time to inspect specific times of interest.

Interactive Visualization - 2 Agent-Agent Transmission Graph – The implicit social network created by tracking agent-agent infections.

Interactive Visualization - 3 Population Attribute vs. Time Charts -- Population statistics. Useful for finding minimal, maximal points or slopes.

Formalizing Knowledge Search There is a hypothetical set of relevant documents which the user would like: D r The user attempts to get the set D r through initially guess and refining a series of: q 1, q 2, … q n. We can think of it as iterative evolutionary hill climber. –Serial sub goals of finding q n+1 such that P(D r |q n+1 ) > P(D r |q n ) Thus… How can we help the user maximize P(D r |q) as quickly as possible?

Don’t forget… popular IR problems. Difficulty in formulating effective queries. –Average number of terms per query is about 1.5. Words do not have a 1:1 mapping to semantic concepts. Determining the relevance ranking of an individual document. –Going past just words. How do you deal with +1 billion documents? –Did you know its more than doubling every year? –Databases/indices of GB each.

The Major “Neat” Features Focus on concrete representation of the query. Use data-mining techniques before visualization. Visual summaries. An active model for interaction. Bridging the gaps between “serial” queries. Widening / narrowing to get context.

Demonstration and Discussion –How do features of interest in one view appear on the other views? –Allow exploratory modification of the simulation to allow testing of containment strategies. –Currently we are just sampling one population from the population distribution – it may be useful to automatically sample many representative populations to find a mean. –Increase complexity of the environment (barriers, transmission modifiers), and agent behavior. –Can this tool (or a further evolution of it) be useful in a war room scenario to help responders? If so how? If not why?