Download Epidemic Talk Sept. 25/08

The Utility of Agent Based Models:
Epidemics, Epizootics, and Healthcare
Friday, April 3
2:00 – 3:00 p.m.
Engineering Senate Chambers
Rm: E3-262
VW-Superbug
Coughing
Pigs
MRSA-Superbug
Bob McLeod
ECE Dept. Seminar Series
Wearing a Suit for the Auspicious Occasion
Internet Innovation Center
ALL WELCOME!
The Utility of Agent Based Models:
Applications to Epidemics, Epizootics,
Healthcare, Preparedness Planning, etc.
— Opportunities for Research
Robert D. McLeod [email protected]
Professor ECE University of Manitoba
Internet Innovation Centre (IIC)
Dept. Electrical and Computer Engineering
University of Manitoba
© IIC, April 3. 2009
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Overview

Part One: Agent Based Models (ABM) Introduction


Motivation: Interest in modeling complex systems
Part Two: Examples of ABM Utility




Epidemic modeling: Discrete Space Scheduled Walker
Epizootic modeling:
Patient Access and Emergency Department Waiting Time
Reduction
Nosocomial Infections (rhymes with polynomial)

Part Three: Extensions and ECE Opportunities

Summary/Discussion
Interspersed with pop science references and questions
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Overview

Goals (Future) : Develop high utility ABM simulators



Goals (Present): Garner Interest toward grant
applications


Wrt epidemics: Preparedness, recovery, mitigation, policy
Wrt healthcare: Patient access, nosocomial infections
Looking for $20K as matching funds (sources or leads)
In general: Preaching the ABM Gospel and its
Utility: Hallelujah
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Part 1: Book Reviews/Motivation

“World Without Us”: Alan Weisman

“Pandemonium”: Andrew Nikiforuk

“The Numerati”: Stephen Baker

“Super Crunchers”: Ian Ayres

“The Tipping Point”, “Outliers”: Malcolm Gladwell

“The Black Swan”, “Fooled by Randomness”: Nassim
Taleb

“The Man Who Knew Too Much: Alan Turing and the
Invention of the Computer”: David Leavitt
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Part 1: Agent Based Modeling

Long time interest: Complex Systems and Modeling

Research resulted from a Programming Challenge
Make the “equations” as simple as possible, but not
simpler, Albert Einstein

ABM is computational modeling essentially devoid
of governing equations


ABMs are pure mathematics.

Is that a G.H. Hardy reference? No, it’s a G. Boole reference.
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Making models more useful
“You can observe a lot by watching:”― Yogi Berra
“Prediction is very difficult, especially
about the future:” Niels Bohr
“In the country of the
blind, the one-eyed
man is King”: ―
Desiderius Erasmus
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How?: Data Mining
and Statistical
Inferencing using an
ABM engine
Refs: Wikipedia
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Part 2: Agent Based Modeling Utility
App1:

A nice attribute about ABMs in general is that
they are ideal idea communication vehicles
App2:

Epidemic modeling - DSSW Model
Epizootic modeling
Extension to ABMs that have not been
exploited
App3:
Modeling an Emergency Department

Further demonstration of ABM utility

Waiting times and nosocomial infections
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App1: Initial Specification for Epidemic
Modeling

Basis idea: Data mine people-people interactions. (Often
Disparate Sources)


Topology: Data mined from maps
Behaviour: Data mined from demographics

Models based on “real” network topologies and
“scheduled” walkers.

The goal of the research is to shed light on the problems
with very complicated phenomena through “data-driven”
modeling and simulation and statistical inference.
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The Model

Data mining is a common theme in modern information
technology:




Analytical methods may not exist or are complex.
Data does exists and can be extracted.
Statistical methods can easily deal with the vast amount of
data that is available (or becoming so).
Our work here is an attempt to help promote data-driven
epidemic simulation and modeling:


Where data is available we demonstrate its utility, where
unavailable we demonstrate how it would be utilized.
Unavailable refers to practical or political limitations.
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“Where”: Topological Data Sources
Google Earth with Overlays
Google Maps
Correct by construction small world topologies
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“Who and When”

Of similar importance to location (where), is the agents
(who) are being infected.

This data is generally technically available but may be
practically unavailable.

An agents’ schedule (when) is also of critical importance.
This data is more typically inferred rather than explicitly
available, but as we are primarily creatures of habit
reasonable assumptions can be made.
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“What”

The what here is typically a disease, either bacterial or
viral, communicated with an associated probability of
contraction when in contact with an infectious agent.

Example 1 of “stochastic” behaviour:


Example 2 of “stochastic” behaviour:


Modified schedule when ill: Low mobility when sick or getting
sick. (Nota Bene: the agent “decides” to stay home)
Weighted random schedule. (Don’t feel like going to work today)
Example of contact:

Physical touch, third party (door knob), cough.
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Implementation

Based on the model our underlying simulation model is
that of a Discrete-Space Scheduled Walker (DSSW).


In contrast to other models that are based on random or
Brownian walkers on artificial topologies.
We capture the most important aspects of real-people
networks, incorporating (correct by construction) notions
such as “small world” networks, scale free networks.
“it is what it is”
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(nota bene)
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“What if”
I live here
I take this bus
I work here
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City of Winnipeg, population: 635,869
The User Interface to DSSW
• Parameters for simulation are
set up in a number of files and the
user can step or loop through the
simulation at any given rate.
• During the simulation, a
number of plots and statistics
are collected and logged to a
web server where the user
can then further analyze the
simulation run.
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Analysis

Some data that is available on
the corresponding web server
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Seasonal Variations

Seasonal variations are well
known and provide fairly well
“labeled” data for comparison

Comparison allows for
a tuning of parameters
to more closely reflect
actual data collected
for a particular disease
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Mutations
“tipping point”
“Seasonal Variation”


A mutation to a deadlier strain or a sudden variation in the
mode of transmission (e.g. virus shift or drift, bioterrorism)
Other uses would be in helping to evaluate the extent of
inoculations (Herd immunity) or policies. This will allow for
epidemiologists to “partially close the loop” when evaluating
policy. (ABM utility, ref. CDC)
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App1: DSSW Summary

Introduced an ABM for epidemic modeling.


Basic characteristic of the model is to extract and
combine real topographic and demographic data.




Data mining and scheduled walkers.
Model using real data is feasible
Results in better characterization of epidemic dynamics
Further work will focus on refining the model, and
validating the afore-mentioned conjecture.
Complementary to “equation based approaches”
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App2: ABM Potential for Epizootics


Epizootics: “outbreak of disease affecting many animals”
Agent based modeling of epizootics.
 Domestic, feral, and/or natural
“ABBOTSFORD, B.C. - The H5
avian influenza virus has been
confirmed on a commercial turkey
farm in British Columbia's Fraser
Valley, and as many as 60,000
birds will be euthanized, the
Canadian Food Inspection Agency
said Saturday.”
January 24/09
Timely if nothing else
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ABM Potential for epizootics


Nicely “constrained” problem: Many Intensive Livestock
Production Operations are nearly “Farrow to Fork”
Best chances of ABM demonstrated utility  Cattle, swine and poultry (BTW: 1918 was a “swine flu”)
e.g. A pork producer should be interested in
the potential of an ABM as a tool in modeling
a swine production environment.
Extendable beyond a single farm to an entire
region including transport and processing.
Allow CFIA to Model: Bio-security measures
Figure 3
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Similar ABMs for Poultry




Broiler grow-out
intensive unit
production.
Similar epizootic
concerns
Manmade pathogen reservoir
Similar problems in other
monocultures
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Mobility and Infection Longevity
Percent dead
100%
Mobility/Longevity Impact
Substantive shift in the “Percolation
Threshold” (decrease)
Percolation threshold is like a tipping point
Mobility has a big effect:
“The mobility threshold for disease is a
critical percolation phenomenon for an epizootic”
5%
42%
Population Density
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Percolation with mobility.
Our study was a very preliminary attempt to use ABMs for ILPO
(Swam or particle type ABMs)
Although crude, clearly illustrates the
impact of mobility on disease spread
Provides design feedback on ILPOs
w/o mobility
with mobility
Disease Spread
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Epizootic Research Grant: Epilogue

Pork Board Proposal Rejected: Comments
 These models are great except... they are models. In
practice I fail to see how this model gets us any
closer to solving disease problems.
 Perhaps not modeling diseases will get us closer

 All but $2,000 of the $50,000 is for support of grad
students.
 Next time I’ll add an Ford F-150 (farm truck) 

Science in your Papers, Science Fiction in Proposals
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App3: ABMs for Patient Access


Models for reducing Emergency Department waiting
times and improving patient diversion policies.

Useful for closing the loop when evaluating policy decisions

Staffing, resources, patient diversion
Agent based simulation of an Emergency Department

Models patient flow through the modeling of individuals

(patients, doctors, service agents (registration, triage))

Examination rooms, waiting rooms (topology)
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Emergency Department Scenario
Basic ED setting
with data collection
resources
illustrated.
i.e. Empirical data
collected here
could be used in
the ED and patient
diversion simulator.
An ED ABM will
Integrate with
whatever
technology
becomes
available
(ECE Benefit)
E.g. Modification of
patient arrival and
treatment times.
Provide initial
conditions for
simulation
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Metropolitan Multiple ED Scenario
Integrated telecom
backbone for a regional
health authority.
Data backhauled to a
central server (CORE) for
processing, simulation, and
policy optimization.
Illustrates use of simulation
enhanced patient diversion
policy.
e.g. Ambulances and walk
in patients.
NHS Trend: No diversion,
better triage.
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Simulation “Proof of Concept”

Visual Simulation Suite Screenshot

Object oriented (OO), open-source, visual simulator to analyze and forecast
emergency department waiting times. (Nota Bene)

EDs can be instantiated with various resources, patient loads and associated
triage levels
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Simulation Scenarios

City wide scenarios

Two EDs with two doctors, two EDs with three doctors,
two EDs with four doctors.

Effect of different staffing levels is compared when there is no
patient diversion

Similar basic scenario is used to compare patient diversion
models.
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Simulation Scenario (Patient Diversion)

Patient diversion modeled using Random Early Detection
(RED) algorithm from Telecommunication Network
Engineering.

Random RED: patients diverted to random ED


Requires local ED information only
Guided RED: patients probabilistically sent to EDs with fewer
patients waiting

Requires city wide communication and coordination
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Simulations and results

Varying the number of Doctors, no patient diversion
Two Doctors
Queue Lengths:
For fewer doctors
queue lengths are
longer.
Three Doctors
Four Doctors
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Simulations and results

Varying redirection policy, averaged across all EDs
No diversion
Queue Length:
Scenario with the
information sharing
experiences the
shortest queues
without additional
resource allocation
Diversion to
random ED
Probabilistic diversion to less busy ED
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Demonstration:

Video on YouTube: http://www.youtube.com/watch?v=_6-Hk-_1MJ8&

Extensions:



Machine Learning for Policy and Resource Provisioning
Use the model as a starting environment for modeling the spread of an
infectious disease within a Hospital. (Extends Agent)
Anecdotal evidence for modeling to improve policy
One of these is
not a taxi
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Making models more useful
Agree
“All models are wrong
but some models are
useful.”
― George E.P. Box,
Statistician
“Truth is ever to be
found in the simplicity,
and not in the
multiplicity and
confusion of things.”
― Sir Isaac Newton
Perhaps truth can actually be found in the multiplicity
and confusion of things! ― Us
Ref: Wikipedia
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Part 3: Possible Extensions and data
Mining Opportunities

At present DSSW epidemic ABM appears mainly well
suited to “egalitarian” type diseases
 “Who agnostic” disease

Here we present a few extensions and opportunities

Extensions of utility to secondary/tertiary interest groups
 Manitoba Hydro, CFPA, Manitoba EMO, Public
Safety, etc.
 Preparedness planning, mitigation and recovery
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Data Mining Comment:



Data Mining is the process of processing large amounts
of data and picking out relevant information. (wiki defn:
common notion)
Here data mining is 2 phase.
 Mining “what to mine”
 Mining the “what”
Data Fusion: combine
data from multiple
sources
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Mine “what to mine”
Data Mining
Data
Fusion
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DSSW Extensions: Hierarchy

Incorporate Hierarchy
 Intracity and Intercity
 Basic modality remains: data-driven models of
discrete space- and time- walkers, (mined).

Cities are largely autonomous
 Allows for the problem to remain tractable and allow
for efficient modes of computation (parallelism can be
exploited).
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Extensions: Extracting Patterns of
Behaviour

Patterns of behavior can be taken from tracking
technologies that are in place albeit not mined for use in
epidemic modeling.
 E.g. Financial Transaction Profiling
 Usually mined to detect fraud
 E.g. Cell phone tracking, “where are you” services
 By default the service provider already knows
where you are, even more so with GPS
 Potential Obstacle: Privacy
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Related Research: Extracting Patterns
of Behaviour
(Benefit of being an ECE)


Consumer wireless electronics: MAC snooping and
tracking. (non obvious data source)
 Bluetooth headsets (ingress and egress of signalized
arterials)
 Similar protocols for WiFi
 Device-enabled Kiosks and vending machines
Security cameras and systems with person detection
 Monitoring for behaviour patterns those of illegal
activities and terrorist threats
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Related Research: Extracting Patterns
of Behaviour (Benefit of being an ECE)

http://gigapan.org/viewGigapanFullscreen.php?auth=033
ef14483ee899496648c2b4b06233c
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Related Research: Extracting Patterns
of Behaviour from Demographics
Clickable(minable) neighborhood demographic information:
http://www.toronto.ca/demographics/profiles_map_and_index.htm
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Related Research: Extracting Patterns
of Behaviour continued

Tracking subway ridership.
 Token data mining of ridership
 Their Objective: Bioterrorism impact

Mining online transportation information systems
 Helsinki public transport
 Input to ABM
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Related Research: Real-time Helsinki
Public Transport Information
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Related Research: Ubiquitous Vehicle
Tracking Cameras
Modeling Arterials
for traffic flow.
ITS data useful
for epidemic
modeling
Similar data is
available for air
traffic.
Ref: http://www.edmontontrafficcam.com/cams.php
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Related Research: Extracting Patterns
of Behaviour (Economic Impact)

Economic Impact: Costs to implement policy. (ref:
Brookings)
 Specifically, the economic impact of restricting air
travel as a policy in controlling a flu pandemic.
 Models global air travel and estimates impact and
cost associated with travel restrictions.
 E.g. 95% travel restriction required before
significantly impairing disease spread
 Not a surprise (also they removed edges not
vertices, cf. percolation)
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Related Research: Extracting Patterns
of Behaviour (Economic Impact)
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Related Research: Google’s Flu trends


Researchers "found that
certain search terms are
good indicators of flu
activity.
Google Flu Trends uses
aggregated Google
search data to estimate
flu activity in your state
up to two weeks faster
than traditional systems"
such as data collected
by CDC.
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Related Opportunity: Google’s Gmail





Google mail (gmail) provides an example of data
mining to extract coarse spatial behaviour patterns.
gmail, web/mail server has a reasonable estimate of
your activity status (busy, available, idle, offline, etc.).
In addition to status, your web browser's IP address
also provides coarse-grained information of where you
are logged in.
If I access gmail from a mobile device, this is also
known to various degrees.
Eric Schmidt, CEO of Google, said, "From a
technological perspective, it is the beginning."
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Other sources of information/concern

Occasional/periodic mass gatherings

E.g. Special event that may perturb a global simulation
E.g. The Hajj
 Largest mass pilgrimage in the world.
 2007 an estimated 2-3 million people participated.
 Conditions are difficult and thus offers opportunity for
a large scale disease such as influenza to take hold.
 These people then disperse to their home countries,
many via public transport, and could easily influence
the spread and outbreak of the disease.

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Mass Gatherings: Hajj
Tawaf, circumambulation of the
Ka’bah
Mosque at Ka’bah
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Related Research: Extracting Patterns
of Behaviour (RFID tracking/RTLS)

Although not explicit, “patterns of behavior” and
“interactions of agents” can be extracted at critical
institutions such as hospitals, through the use of RFID
tracking.

As RFID sensor networks move from inventory to
enhanced applications, data collected from RFID
tracking at clinics and hospitals can be envisioned as an
input to an ABM. (similar to WiFi Campus tracking)
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Preparedness, planning and mitigation

Preparedness planning: A massive undertaking but one
in which an ABM city model could be useful in providing
planners with policies and expectation how goods and
services could be provisioned in the event of a
catastrophe.

This aspect can be “catastrophe agnostic”

Simple investigations as to how long food/fuel/medical
supplies would last and could be distributed will be
modeled.
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Preparedness, planning and mitigation

Provisioning of resources extempore will lead to an
aggravated and worsening disaster.

Models can become an effective tool for any city.
 Specific model to their region

Allowing for provisioning not only of supplies but for
inoculation services as well as temporary hospital
and/or mortuary facilities.
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Preparedness, planning and mitigation
Power generation: Remote
maintained by “healthy”
individuals: Stakeholders
Hydro/Electric
Easily Isolated: Transportation
Stakeholders: EMO
Food production/provisions: Local
Stakeholders: Producers(Distributors)
Water Supply: Remote, EMO
Result: Pandemic Lag if Prepared
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Multiple Hospital Model Patient
Diversion : Future Work

Incorporate empirical data mined from ITS sources such as
Google/Globis real-time traffic to estimate delays the
ambulance would experience enroute
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Summary



Presented our Agent Based Modeling approach to high
“utility” simulation.
 Emphasis on data mining of spatial topologies and
agent behavior patterns
Presented several indirect data sources
 Non-obvious connection to epidemic modeling
 Good for ECEs though
Presented potential extensions: Utility of ABMs
 Epidemics, Epizootics, ED Wait times, Nosocomials
 Opportunities in preparedness planning, mitigation
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Ideally one would like to model
everything: (someday will)





Threats: epidemic natural or bio-terrorist. (In progress)
 Model impact of policy, provisioning (PHAC)
Model Food Supply:
 Intensive unit production facilities through from birth
to slaughter.
Model Food and Fuel Supply and Distribution:
 Guidelines for stock provisioning. (CPMA)
Model infrastructure: Transportation, water, power.
 Model impact of policy (Amenable to ABMs)
Assess interest in moving forward, from tertiary groups.
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Exploring research opportunities

Being “devoid” of equations, agent based models allow
for a tradeoffs between specificity and utility.

We would like to be part of a larger modeling effort and
want to explore that possibility. Extend models beyond
epidemics to related areas of direct interest to Manitoba.

Assessing interest to provide some degree of matching
funds to apply for a MITACS seed grant. May 2009.

Total matching funds we are targeting is 20K, providing
70K of funding if successful.

Leverage other efforts: Possible with some traction here
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Dissemination efforts:

Epi-at-home.com: Future home of Epidemic ABM open
source project (DSSW)

Bio-inference.ca: Future home of ABM and data mining
opportunities (non obvious sources)



Epizootic, patient access, preparedness planning
Facebook group: “Pandemic Awareness Day”

Exploring social networks as an information tool

A non invasive information portal (140+ members)
A growing number of papers/proposals/talks.
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A mathematicians apology:

ABMs are facing considerable resistance from the
Mathematical community.

“ABMs will never be useful” – Recent quote from a
famous and influential mathematician.

Two Mistakes:

His null hypothesis can only be rejected .

“I think there is a world market for maybe five
computers” – T.J. Watson (IBM 1943) Even brilliant
people make mistakes.
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Miscellany: Developing ABM Codes

Product development often under a water fall model.

Errors introduced early and not contained or corrected
amplify costs, time and money.

Lessons learned in knowledge translation.

The specification should be as close to executable as
possible. (Any modern telecom protocol)

Verification is more easily undertaken with an ABM
during the development cycle.

These are byproducts of an ABM
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Miscellany: ABMs vs. IBMs

Individual based models (IBMs)


Emphasis on the agent as the individual (person)
Agent based models (ABMs)

Agents can be people, animals, or inanimate objects
(e.g. piece of medical equipment in a hospital)

This generalization of the notion of ‘agent’ constitutes
an implementation advantage, from an objectoriented paradigm

Analogy: Executable Specification
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IIC Contact: U of M ABM initiatives
Bob McLeod
Professor ECE
University of Manitoba
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E3-416 EITC
University of Manitoba
Winnipeg, Manitoba
R3T 5V6
Acknowledgements:
Too many to list.
Named throughout talk.
Healthcare ABM Research:
Marek Laskowski
Email: [email protected]
http://www.iic.umanitoba.ca
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Pandemic Awareness Day:
A Facebook Group that Invites You to Join Us!
Seasonal Variation of
Influenza or Facebook
Ad?
140
100
Members as of
Mar.16/09
100%
Percent infected
Mobility/Longevity Impact
Substantive shift in the “Percolation
Threshold” (decrease)
Percolation threshold is like a tipping point
5%
42%
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Population Density
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