EpiGrid 2007 Modern Epidemiology – A New Computational Science Facilitating Epidemiological Research through Computational Tools Armin R. Mikler Computational Epidemiology Research Laboratory Department of Computer Science and Engineering University of North Texas
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EpiGrid 2007
Modern Epidemiology – A New Computational Science
Facilitating Epidemiological Research through Computational Tools
Armin R. MiklerComputational Epidemiology Research Laboratory Department of Computer Science and Engineering
University of North Texas
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Searching for the cause of Death
Epidemiology has been in existence for hundreds of years. Its beginnings were motivated by the questions:
What causes people to die ?
The answer to this question would lead to new ways to prevent some of the causes - although the inevitable still cannot be avoided.
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Medicine ≠ Epidemiology
Dictionaries offer the following definitions:
Medicine -
the science and art dealing with the maintenance of health and the prevention, alleviation, or cure of disease.
Epidemiology -
a branch of medical science that deals with the incidence, distribution, and control of disease in a population.
Notice that "art" is missing in the definition for Epidemiology!!
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Some Historical events in Epidemiology Epidemiologic accounts date back to the time of Hippocrates (459 -
377 B.C.) and ancient Greeks. In the 1300s, Europe lost 25% of its population of 100 million to the Black Death or Plague.
A Smallpox outbreak in 1521 eradicated half of the Aztecs Empire of 3.5 million people.
London's Cholera Epidemic in 1854.
In 1918, the Spanish Flu (pandemic Influenza) caused an excess death of over 20 million world wide.
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Containing and Controlling the Disease A better understanding how diseases spread among the population has led to greater sophistication in methods that prevent or at least contain outbreaks.
• Quarantining entire villages.
•
Mapping of disease clusters (by John Snow in 1854 during London's Cholera Epidemic).
•
Social distancing (quarantining) was advocated during the 1918 Influenza pandemic.
Examples:
Question:How are we dealing with emerging and re-emerging diseases today?
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Epidemiology Computational Epidemiology
Methodical Approaches
John Snow's effort to identify the source of the 1854 Cholera epidemic in London was one of the earliest applications of GIS in Epidemiology.
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Morbidity in Context
John Snow’s map of cholera cases has led to the identification of the point source of the disease.
The Water Pumps on Broad Street!
The Geographic Information in addition to the Case Data has established a CONTEXT to display Relationships in Time and Space!
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A motivating example! The following data was collected in a single retrospective
observational study. What was the cause of death ??
2201 (1490) (126) 470 Total(1364) 1731
885 (673) (670) 862(3) 23 Other
706 (528) (106) 196 (422) 510 Low
285 (167) (13) 106 (154) 179 Middle
325 (122) (4) 145 (118) 180 High
BothFemaleMaleSocial Class
Exposed (Deaths)
Excess deaths by gender:
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2201 (1490) (52) 109 (1438) 2092 Total
885 (673) (0) 0 (673) 885 Other
706 (528) (476) 627(52) 79 Low
285 (167) (167) 261(0) 24 Middle
325 (122) (0) 6 (122) 319 High
BothChildAdultSocial Class
Exposed (Deaths)
Excess deaths by age group:
…more data ….no context
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…some Geographical Information…..
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…some temporal information….
April 14/15, 1912
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A modern Epidemiological study….
Results of a Tuberculosis survey in Tarrant County, TX
•Problem: Insufficient Context
GIS data with greater detail!
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The Epicenter
Pictures by Patrick Moonan, 2003
People standing outside homeless shelter
People sleeping inside homeless shelter
After identifying the homeless shelter as the epicenter, granularity of the study change again: more contextual detail
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Different arrangements at different $ in the same shelter.
Pictures by Patrick Moonan, 2003
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John Snows approach in the Shelter
The Map
TB Prevalence
From Spatial to Social Epidemiology!
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Towards Computational Epidemiology
Its all about the CONTEXT!
How can we prepare for Epidemiologic Emergencies including Epidemics, Pandemics, and Bioterrorism if there is no current morbidity or mortality data available?
We may use historical or anecdotal data!
Questions:
• Can we build a model that reproduces the historic event?
• How would the event manifest itself in a modern context?•Demographics may have changed;
•Infrastructure may have changed;
•Medical practices may have changed;
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Models in Computational EpidemiologyModels in Computational Epidemiology
From mathematical models to simulation:From mathematical models to simulation:-
Basic SIR Model based on Differential Eqs.–Dynamic System Modeling –Data Storage and Analysis–Simulation –Data visualization.
Computation Epidemiology is more than the sum of its parts: Computation Epidemiology is more than the sum of its parts: Epidemiology, Computer Science, Mathematics, Dynamic Epidemiology, Computer Science, Mathematics, Dynamic Systems, Public Health Systems, Public Health ……..
–Investigating disease outbreaks and risk assessment in spatially delineated environments –Investigating intervention strategies to control the spread of diseases–Investigating spread of disease in demographic, and geographic space!
GIS/EPI Data
Model
Visualization
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Tools of the trade
o
Mathematical ModelsSIR, SEIR, SIS, etc.Population EcologyGraph Theoretical Models
o
Computational solutions:Simulations
•
Agent based•
Cellular Automata•
Stochastic Field Simulation•
Social NetworksSpatial-Temporal DatabasesGISVisualization Web InterfacesHigh Performance Computing
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Some current issues….
The following is a collection of current problems for which computational models are being developed at CERL:
Contact Models to predict and quantify Pandemic Influenza
Infectious Disease Outbreaks in the K-12 School System
STD Spread Models: HPV & HIV
Social Network Models of Social/Intimate Relationships
Points of Distribution (PODs) Traffic Analysis
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PODs Traffic Analysis
Federal and State funding has been used by counties to develop a comprehensive disaster preparedness plan. This plan identifies several
sites in the county at which citizens can obtain medication or vaccination in the case of a Bio / Medical disaster.
Questions:
•Can PODs sustain traffic
•Can roads sustain traffic
•Placement of PODs
•How many people can get service in how little time?
PODs
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Waiting on line to get smallpox vaccine during New York Citysmallpox epidemic (1947)
We need to experiment with intervention strategies
When, How, Who, Where –
should we vaccinate?What are the predicted outcomes of specific strategies?
How should mass-intervention be organized ?
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The Global Outbreak ModelThe Global Outbreak Model
VaccinationPopulation
Demographics
Disease Parameters
Data Sets Visualization
Interaction factors
Distances
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Disease ParametersDisease Parameters
Illustrates time-line for infection (influenza)
Latent periodInfectious periodIncubation periodInfectivity Index case
Multiple index casesLocation of index case
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Model ParametersModel Parameters
o
Population per cello
Demographics i.e.
Age DistributionEthnic DistributionGender Distributionetc.
o
Geography/Hotspotso
Contact Rate(s)
avg.when symptomatic
o
Vaccinated Populationo
Vaccine Efficacy
o
Natural Immunityo
Immune Deficient Population
o
Public Health Events
Population distribution over the North Denton region.
Total Population of 110,000 distributed over a grid size of 50 * 100.
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The Complexity of ContactsThe Complexity of Contacts
InfectiousInfection transmits ?
Function of the infectivity parameter
Common meeting area
Contact includes
•Exposure
•Duration of exposure
•Infectivity/ Virulence of the virus (infection)
•Immunity
•Age of individual
•other demographic characteristics
Contact is any interaction that facilitates successful disease transmission.
Epidemics are driven by Contacts and Exposures!
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Social Networks determine contacts
o
Clusters indicate strongly connected subgroups.
o
Measures of Affinity
o
Who is likely to contact whom?
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Population Distributions for Different Age GroupsPopulation Distributions for Different Age Groups
0-9 years
60+ years35-59 years
10-34 years
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Global Stochastic Field Model demographic layersGlobal Stochastic Field Model demographic layers
IndividualProbable area of interaction
Age group 0-9
Age group 60+
Age group 35 -
59
Age group 10-34
Probability of interactions based on distances
Prob
abili
ty o
f in
tera
ctio
ns a
mon
g va
riou
s ag
e gr
oups
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Visualizing Spatial Spread of Influenza Visualizing Spatial Spread of Influenza simulated over Northern Denton Countysimulated over Northern Denton County
Local Interaction
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Index case
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Global InteractionGlobal Interaction
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A composite SIR Model
100 small regional outbreaks
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Σ
Observed Epidemic
Regional outbreaks
We can extend the model fromgeographic regions to demographicsub-populations
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Composition Model
Assumption : Sub-regions (or cells) with a larger proportion of a certain demographic may display increased or decrease prevalence of a certain disease as compared to a sub-
region with a larger proportion of a different demographic
Cell interaction is controlled by age proportions and population densities.
Composition model reflects the spread of the infection in each sub-
region.
Observed Cumulative Epidemic caused by Temporally and Spatially Distributed Local Outbreaks
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Mathematical Modeling of Epidemics
Let β be the transmission rate based on contact rate and infectivityLet γ be the rate of infectives becoming non-infectious
RemovedR
SusceptibleS
InfectivesI
dSdt
= − β SI
dIdt
= + β SI− γI
dRdt
= + γI
• Susceptibles Infectives Removals (SIR) model• Susceptibles Exposed Infectives Removals (SEIR)• Susceptibles Infectives Susceptibles (SIS)
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Susceptibles Infectives Removals (SIR) model
The naïve SIR assumes:oHomogeneous mixing of people
oEvery individual makes same contacts
oNo demographics consideredoGeographical distances not considered
Things can get unwieldy when adding demographics!!
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Composition Model -Experiment
Simulation parameters:
Disease Simulated : Influenza like disease
Incubation period : 3 days
Infectious period: 3 days
Recovery period: 5 days
Infectivity : 0.020
Contact rate/person : 11
o
The population distribution over the region is non-uniform.
o
Contacts made between cells depends on the population of the cell.
o
Assumption : Regions with high population make more contacts than regions with low population.
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Composition Model -Experiment
Population distribution over the north Denton region.
Total Population of 110000 distributed over a grid size of 50 * 100.
Total Population infected at the end of simulation: 48000
Infected Population distribution over the north Denton region.
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Experiment--
Immunityo
The probability of a contact with an infectious person resulting in a successful disease transmission depends on the immunity of the individual. o
Experiment was conducted considering that people residing in a particular region were immune to the particular virus as means of either vaccination or previous infection. oThe results show lower level of prevalence of disease in that region compared to other regions.
Region Immunized
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The need for Computational Horse PowerThe need for Computational Horse Power
Large geographic region Many cells/objects
We need multiple computers to execute We need multiple computers to execute small pieces of the simulation simultaneously.small pieces of the simulation simultaneously.
Complex interaction and Multiple Populations
Large Computational Complexity
Many cells/objects Complex interactions
Many cells/objects Simulation of Multiple Populations
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The Future:The Future: Clusters and the GRIDClusters and the GRID
o
Faster hardware and new high-bandwidth networks demand that we explore new cluster architectures.
o
Larger, more complex cluster environments make it imperative to invest in new efficient and scalable tools.
o
Grand Challenge
problems will continue to drive the development of computing infrastructure.
o
Distributed HPC
will become common place. (DOE SciDAC)
o
Management Tools
designed for single hosts or small clusters are likely NOT to scale.
o
New types of
Middleware
is needed to decouple the underlying distributed infrastructure from the applications.
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Grid Layers…virtualizationGrid Layers…virtualization
Internet / PrivateNetworks
GridEngine
GridEngine
GridEngine
GridEngine
GridEngine
GridEngine
General Grid Services
Application-Specific Grid Services (APIs)
Applications
Middleware
Grid Access
i.e., ScientificDiscovery throughAdvanced Computing
Data Grid
Comp.Grid
BioGrid
EPIGrid?
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Validate, validate, validate…
“Great Model”, “Compelling Results”, “…nice tool” …BUT….HOW DID YOU VALIDATE ITS CORRECTNES??
Problems:•No Data on Emerging Infectious Diseases to compare against•Insufficient Domain Knowledge•HIPPA & Data Privacy•Incomplete or Missing Data•Complexity, Complexity, Complexity
Much Ado About Nothing!?
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Domain Knowledge and Expectation
Don’t kid yourself –
we do not understand the details of howsociety works and how people interact!!
We can only theorize how an epidemic might manifest itself and prepare for the worst-case scenario!
If data of previous epidemics (of same or similar disease) is available, expectations can be based on observation. HOWEVER, circumstanceshave most likely changed.
Idea: Develop computational tools that allow experts to express theirexpectations. Validate against DOMAIN EXPERTISE even if it is justa theory or hunch!
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ConclusionConclusion
There are many different methodologies to chose from: Mathematical Models, Agent Based Models, CAs, GSFS, etc.
Chose the most appropriate modeling/simulation technique based on thedomain characteristics – Spatially Delineated, Regional, …
When developing a computational tool, keep in mind whose work is going to be facilitated!! Visualize & Parameterize & Animate
Facilitate WHAT-IF-ANALYSES and support quantification of policy and/or strategic decision making.
Validate against domain expertise if no reliable data source is available!
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