John Nelson HuffmanMentor: Dr. Nina H. Fefferman

BiosurveillanceThe science of determining when unusual

patterns of disease arise in a populationWhy is biosurveillance important?Earlier responses mean lower mortality rates

Inherent problems with infection dataNatural fluctuation of what constitutes

endemic conditionsMethod of DiagnosisNon-uniform reporting

Problems with current methodologyEpidemic conditions are defined statically by

examining historical precedentMany critical outbreaks involve either new

diseases or diseases affecting new populations for which historical data is inadequate

ApproachThe surveillance network will be modeled as

a dynamically connected graphEach 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

F igure 4 – Serra tia m a rcescens, bacteria

know n to exh ibit cell- to- cell com m un ication to m on ito r their popu lation d en sity, synchron ize

their behavior, and in teract socially. (rep rod u ced from <w ww.m icro bio logybytes.com / blog/ 20 0 7/ 0 6 / ).

F igure 3 - L eaf cu tter ants return ing along the forage path to the co lony w ith their fi nd ings ( left) and an ant trail lead ing from the co lony to a nearby area w ith appropriate leaves for foraging ( righ t) ( reproduced from <latinam ericayourw ay.blogspot.com / >) .

F igure 2 - H oney bee fo rager gathering po llen ( left) and com m unicating the quality o f the d iscovered site to others back at the h ive ( righ t)

Bacteria Quorum SensingQuorum: the minimum number of members

required to achieve a consensusChemical 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 AlgorithmsIndividual 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