Towards Computational Epidemiology Designing an Infectious Disease Outbreak Simulator

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Towards Computational EpidemiologyDesigning an
Infectious Disease Outbreak Simulator

• Armin R. Mikler
• Department of Computer Science and Engineering
• Department of Biological Sciences
• University of North Texas

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Towards Computational Epidemiology
Address broader aspects of Epidemiology Disease
Tracking, Analysis, and Surveillance High
Performance Computing (HPC) Simulation Data
visualization.

• Design and implement computational tools
• investigating Tuberculosis outbreaks and risk
  assessment in spatially delineated environments
• modeling and simulating details of specific
  instances of Tuberculosis occurrences in North
  Texas
• applicable to a wide variety of disease
  outbreaks in spatially well-defined settings

Contribute towards establishing computational
epidemiology as a new research domain!!
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• Disease Outbreak Model

Local
Global

• Global
• Demography
• Socio-economics
• Travel
• Transportation
• Geography
• Culture

• Local
• Delineated space
• Factory, homeless shelter, school
• Airflow
• Heating and cooling
• Distances in feet
• Architectural properties

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Global Stochastic Cellular Automata and the SWARM
Top LayerCellular Automata Global
Middle Layer Cellular Automata Regional
Bottom Layer SWARM Local
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• The Focus of Study--Locality based

This study proposes to model the dynamics of
tuberculosis transmission within two facilities
in North Texas - a homeless shelter facility
providing both long and short-term occupancy with
800 beds, and a factory.
Data was previously collected through interviews
during targeted surveillance screening of workers
in the factory and homeless people who use the
shelter.
Data has been Deidentified !!!
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Homeless Shelter Data and Findings

• For the homeless shelter, the data set comprises
  screening records for each case including
• Date tested (relative to t0)
• Status of tuberculosis
• Location in the facility
• Length of time spent in the facility
• Other variables

Results of initial analysis suggest that TB risk
is not uniformly distributed but depends on the
location of the sleeping bed and duration and
frequency of stay at the night shelter.
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Factory Data and Findings

• In addition to basic screening records as
  collected for the homeless shelter, other
  available data for the factory include measures
  of duration and proximity to infected person such
  as
• Hours per week in the factory
• Hours per week in the same workspace
• Hours per week within 3 feet of infected person
• Usual work area.

Results of initial analysis indicate that
proximity of workspace to infected person was a
major determinant of infection.
In fact 100 of those who worked directly in the
same space with one infected person were infected
with the same strain of TB.
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Factory Layout
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The Paint Area
The Restroom
The Eating Area
Air vent system
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Modeling Approaches

• Agent based modeling
• Level of exposure
• Emergent behavior defined by individuals
  actions.
• The average number of bacilli that are emitted
  (through coughing, sneezing, etc.)
• Spatial interaction.
• Stochastic Cellular Automata
• Ambient temperature and airflow
• Particle Suspension and Dispersion
• Intrinsically stochastic.

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 From GIS data to Agent-Based Simulation to
Visualization
GIS/ Epidemiologic Data
Social Interactions
Particle suspension Airflow
Visualization
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Movement and Desire
D
S/D A B C D A - B C B
B A - C C C A B
- D D C C C -

C
A
B
Agent at (xi, yi)
Example of functions that model different types
of desire as a function of time.
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Particle Suspension and Dispersion
As a function of time, bacilli settle toward the
ground and may spread to neighboring cells
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• State of each cell Ci,j depends on Ci,j1,
  Ci,j-1, Ci1,j, Ci-1,j, Ci1,j-1, Ci1,j1,
  Ci-1,j-1, Ci-1,j1

• The color of a cell changes based on the majority
  color of its neighbors

T0
T1
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Visualization--Simulated Simulation
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Pathogen Content
Obstructability
? Healthy Person Normal
? Weaker Person Low/Med TB
? Sick Person High TB
? Removed Floor
Obstacle
Wall
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The Future Clusters and the GRID

• Faster hardware and new high-bandwidth networks
  demand that we explore new cluster architectures.
• Larger, more complex cluster environments make it
  imperative to invest in new efficient and
  scalable tools.

• Grand Challenge problems will continue to drive
  the development of computing infrastructure.
• Distributed HPC will become common place. (DOE
  SciDAC)
• Management Tools designed for single hosts or
  small clusters are likely NOT to scale.
• New types of Middleware is needed to decouple the
  underlying distributed infrastructure from the
  applications.

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Grid Layersvirtualization
Data Grid
Comp. Grid
Bio Grid
i.e., Scientific Discovery through Advance
Computing
Applications
Application-Specific Grid Services (APIs)
Middleware
General Grid Services
Grid Engine
Grid Engine
Grid Engine
Grid Engine
Grid Engine
Grid Engine
Grid Access
Internet / Private Networks
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Matter of Facts.

• There is increasing demand for harnessing
  computational resources
• Increasing demand for Grid-based computing at the
  private sector
• Computing Power will become a commodity like
  Water, Gas, etc.
• As with ISPs, Grid Access Providers (GAPs) will
  have to guarantee Quality of Service.
• Through Grid Services, we can provide a global
  computing infrastructure and facilitate services
  for a large number of application domains at the
  private and public sector!
• Examples Healthcare, Education, Industrial RD,
  Entertainment, Sciences, etc.

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Cluster Semantics
Cluster Nodes
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People Behind - The Group
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A Final Push to Control TB
Because the number of cases of TB in the U.S. are
lower than theyve ever been, we have the
opportunity to finally control TB in the U.S.
Yet little research exists on the dynamics of
localized TB transmission in homeless shelters.
Recent research suggests that focusing on the
dynamics of how TB is transmitted in specific
locations is a much-needed final push to TB
control.
Little attention has been given to places like
factories, warehouses, healthcare facilities, or
schools where people work in close proximity for
long periods of time.
Homeless shelters and overcrowded areas
constitute reservoirs of TB infection.
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Cray Y-MP IBM Power4

• Common supercomputer in early 1990's
• 1 million from Cray
• Max speed 2.3 gigaflops (record speed)

• Pentium III 1Ghz processors. Same processors sold
  off the shelf
• 64 gigaflops
• 198th on Top500 list (http//www.top500.org)

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Big Mac _at_ Virginia Tech

• Macintosh G5 workstations
• Infiniband networking interconnect
• 3rd fastest supercomputer in the world

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Cellular Automata (4 Neighbors von Newman)

• State of each cell Ci,j depends on the neighbors
  Ci,j1, Ci,j-1, Ci1,j, Ci-1,j

• For example, the color of a cell depends on the
  majority color of its neighbors

T0
T1