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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
Figure 2 - Honey bee forager gathering pollen (left)
and communicating the quality of the discovered site
to others back at the hive (right)
Figure 4 – Serratia marcescens, bacteria
known to exhibit cell-to-cell communication to
monitor their population density, synchronize
their behavior, and interact socially.
(reproduced from <www.microbiologybytes.com/blog/2007/06/).
Figure 3 - Leaf cutter ants returning along the forage path
to the colony with their findings (left) and an ant trail
leading from the colony to a nearby area with appropriate
leaves
for
foraging
(right)
(reproduced
from
<latinamericayourway.blogspot.com/>).
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