Characterization of Distributed Micro-releases of Pathogens

Michael Wolf, University of Illinois at Urbana-Champaign

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In this project, we are working on identification of the time and location of several small “micro-releases” of pathogen based on the identification of infected individuals at medical institutions. Large releases of pathogen can be more easily detected by sensors placed at a relatively small number of key locations. However, distributed micro-releases can evade sensor detection since the amount of contagion is smaller at each location and the targets can be much more varied. The first indication of the pathogen will be patients at clinics or hospitals showing sufficiently obvious symptoms for positive diagnosis. Timely identification and characterization of the threat of these micro-releases is essential for the containment of the disease propagation.

Since detection is difficult and the dynamics of the disease propagation is complex, obtaining an accurate model is very challenging and computationally intensive, requiring the solution of many interesting computational science problems. The general approach towards this problem of inferring the micro-release characterization from the patient data is to formulate a Bayesian inverse problem which can provide probabilistic descriptions to identify the most likely timings and locations of the onset of pathogen. With these probabilistic descriptions, appropriate responses can be shaped to most effectively mitigate the spread of the pathogens. In order to solve the Bayesian inverse problem, a forward disease propagation problem must be solved numerous times to provide the information necessary to solve this inverse problem accurately. We have implemented an agent-based model to solve the forward propagation problem. Since this forward problem is to be solved numerous times for each Bayesian inverse problem, I am working to improve this implementation to solve this problem as rapidly as possible.

Abstract Author(s): Michael Wolf<br />Jaideep Ray<br />Karen Devine<br />Brian Adams