John Nelson Huffman Mentor: Dr. Nina H. Fefferman

Biosurveillance The science of determining when unusual patterns of disease arise in a population Why is biosurveillance important? Earlier responses mean lower mortality rates

Inherent problems with infection data Natural fluctuation of what constitutes endemic conditions Method of Diagnosis Non-uniform reporting

Problems with current methodology Epidemic conditions are defined statically by examining historical precedent Many critical outbreaks involve either new diseases or diseases affecting new populations for which historical data is inadequate

Approach The surveillance network will be modeled as a dynamically connected graph Each node will represent a disease incidence monitoring station (i.e. hospitals, doctor’s offices, etc.) and will possess multidimensional data about the respective populations they represent Nodes will be able to share data with other nodes located in a ‘sphere of proximity’ as defined by a specific algorithm

Biologically Inspired Algorithms

Bacteria Quorum Sensing Quorum: the minimum number of members required to achieve a consensus Chemical signals from individual bacterium are used by their 'most immediate neighbors' Bacteria can determine the density of their neighbors and act accordingly based upon which chemical signals are propagated by a quorum

Adaptation of Algorithms Individual organisms are analogous to our monitoring stations (i.e. nodes) “Traveling to a specific location” will represent a node’s decision to share its information/decisions with its ‘sphere of proximity’ Frequency of communication and relative weight among all nodes in the network will be interpreted as “excitement,” an attribute of the nodes which represents their population's potential for an outbreak compared to other nodes in the network