Internet Innovation Center The Utility of Agent Based Models: Applications to Epidemics, Epizootics,...

Internet Innovation Center

The Utility of Agent Based Models:Applications to Epidemics, Epizootics, Preparedness Planning, etc.

Robert D. McLeod [email protected]

Professor ECE University of Manitoba

Internet Innovation Centre (IIC)Dept. Electrical and Computer Engineering

University of Manitoba

© IIC, Jan. 2009

— Opportunities for Research

Internet Innovation Center 2

Overview Part One: 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

Part Three: Extensions and Opportunities

Summary/Discussion

Interspersed with pop science references and questions


Overview Goals (Future) : A high utility ABM simulator

Epidemic, preparedness, recovery, mitigation, policy

Goals (Today): Garner Interest toward a MITAC$ grant Apply as a seed project May/09, w/blessing(collaboration) Looking for $20K as matching funds (sources or leads)


Part 1: Book Reviews/Motivation “World Without Us”: Alan Weisman

“Pandemonium”: Andrew Nikiforuk

“The Numerati”: Stephen Baker

“Super Crunchers”: Ian Ayres

“The Tipping Point”: Malcolm Gladwell

“The Black Swan”: Nassim Taleb

“Fooled by Randomness”: Nassim Taleb

“The Man Who Knew Too Much: Alan Turing and the

Invention of the Computer”: David Leavitt


Part 1: Agent Based Modeling

General Interests in Complex Systems and

Modeling

Much of this 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.


Making models more useful

Refs: Wikipedia

“In the country of the blind the one-eyed man is King”: ― Desiderius Erasmus

How?: Data Mining and Statistical Inferencing

“You can observe a lot by watching:”― Yogi Berra

“Prediction is very difficult, especially about the future:” Niels Bohr


Part 2: Agent Based Modeling Utility

App1: Epidemic modeling - DSSW Model

A nice attribute about ABMs in general is that

they are ideal idea communication vehicles

App2: Epizootic modeling

An extension to areas where ABMs have not

been fully exploited

App3: Modeling an Emergency Department

Another area where ABM utility can be

demonstrated


App1: Initial Specification for Epidemic Modeling

Basis idea: Data mine where possible the basic tenets of

people-people interactions. (Often Disparate Sources) Topology: Data mined from maps Behaviour: Data mined from demographics

Our approach develops models based on “real” network

topologies and “scheduled” walkers.

The goal of the research is to shed additional light on the

problems associated with very complicated phenomena

through “data-driven” modeling and simulation and

statistical inference.


The Model Data mining is a common theme in modern information

technology: Analytical methods may not exist or are overly complex. Data exists and can be readily extracted. Statistical methods can now more 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 data refers to practical or political limitations on

access, rather than technical or theoretical availability.


“Where”: Topological Data Sources

Google Earth with Overlays Google Maps

Correct by construction small world topologies


“Who and When” Of similar importance to location (where), is the agents

(who) are being infected. This is data that is generally technically available but

may be practically unavailable. Our model attempts to illustrate how the data would be

used if available.

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.


“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: Modified schedule when ill: Low mobility when sick or getting

sick. (agent “decides” to stay home)

Example 2 of “stochastic” behaviour: Weighted random schedule. (Don’t feel like going to work today)

Example of contact: Physical touch, third party (door knob), cough.


Implementation

Based on the model as described above, it should be

clear that our underlying simulation model is that of a

Discrete-Space Scheduled Walker (DSSW), in contrast

to other models that are more traditionally based on

random or Brownian walkers on artificial topologies.

We attempt to capture the most important aspects of

real-people networks, incorporating (by construction)

notions such as “small world” networks, scale free

networks, “it is what it is”. (nota bene)


“What if”

City of Winnipeg, population: 635,869

I live here

I work here

I take this bus


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.


Analysis

Some data that is available on the corresponding web server


Seasonal Variations Seasonal variations are well

known and provide fairly well “labeled” data for comparison

The figure illustrates the type of data available

Comparison allows fora tuning of parametersto more closely reflectactual data collected for a particular disease


Mutations

A mutation to a deadlier strain or a sudden variation in the mode of transmission (e.g. virus shift or drift, bioterrorism)

Other uses of the simulator would be in helping to evaluate the extent of inoculations or policies in the event of a simulated outbreak. This will allow for epidemiologists to “partially close the loop” when evaluating policy. (ABM utility, ref. CDC)

“tipping point”

“Seasonal Variation”


App1: DSSW Summary

Introduced a reasonable method of epidemic modeling, taking advantage of opportunities for data mining and scheduled walkers.

The basic characteristic of the model is to extract and combine real topographic and demographic data. This work shows that model creation using real data is indeed feasible, and will likely result in better characterization of the actual dynamics of an epidemic outbreak.

Further work will focus on refining the model, and validating the afore-mentioned conjecture.

Complementary to “equation based approaches”


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


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

Figure 3

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


Similar ABMs for Poultry Broiler grow-out

intensive unit production.

Similar epizootic concerns

Man made pathogen reservoir Similar problems in other

monocultures


Mobility and Infection Longevity

42%5%

Per

cent

dea

d

Population

100%

Mobility/Longevity ImpactSubstantive shift in the “Percolation Threshold”

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”


Percolation with mobility.Our study was a very preliminary attempt to use ABMs for ILPO

Although crude, clearly illustrates the impact of mobility on disease spread

Provides design feedback on ILPOs

w/o mobility with mobility

Disease Spread


App3: ABMs for Patient Access

Methods for reducing hospital Emergency Department

waiting times and patient diversion. Useful for closing the loop when evaluating policy decisions

Useful across a regional hospital authority for load balancing

(patient diversion policies)

Agent based simulation of Emergency Department Models patient flow through the modeling of individuals

(patients, doctors, service agents (registration, triage)


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.

E.g. Modification of patient arrival and treatment times.

Provide initial conditions for simulation


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.


Simulation “Proof of Concept”

Visual Simulation Suite Screenshot Object oriented (OO), open-source, visual simulator to analyze and forecast

emergency department waiting times.

EDs can be instantiated with various resources, patient loads and associated triage

levels


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

communication (i.e. no patient diversion)

Same basic scenario is used to compare patient diversion

models.


Simulation Scenario (Patient Diversion)

Patient diversion modeled using Random Early Detection

(RED) algorithm from Telecommunication Network

Engineering.

After a threshold in queue length is reached, the probability of

a patient being diverted increases from 0.

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


Simulations and results

Varying the number of Doctors, no patient diversion

Queue Lengths:For fewer doctors queue lengths are longer.

Two Doctors

Three Doctors

Four Doctors


Simulations and results

Varying redirection policy, averaged across all EDs

Queue Length:Scenario with the most information sharing experiences the shortest queues without additional resource allocation

No diversion

Diversion to random ED

Probabilistic diversion to less busy ED


Demonstration:

Video on YouTube

Extensions: Machine Learning for Policy and Provisioning

Use the model as a starting environment for modeling the spread of an

infectious disease within a Hospital.


Making models more useful

“All models are wrong but some models are useful.”― George E.P. Box,Statistician

“Truth is ever to befound in the simplicity,and not in themultiplicity andconfusion of things.”― Sir Isaac Newton

Ref: Wikipedia

Agree

Perhaps truth can actually be found in the multiplicity and confusion of things! ― Us


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 well suited to mining of disparate sources for epidemic modeling

Extensions of utility to secondary/tertiary interest groups Manitoba Hydro, Peak of the Market, Manitoba EMO,

Public Safety, etc. Preparedness planning, mitigation and recovery


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”

Mine “what to mine”

Data Mining

Data Fusion

Data Fusion: combine data from multiple sources


DSSW Extensions: Hierarchy

Incorporate Hierarchy Intracity and Intercity

Basic modality remains: data-driven models of discrete space- and time- walkers, mined from available sources.

Cities are largely autonomous Allows for the problem to remain tractable and allow

for efficient modes of computation (parallelism can be exploited).


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

Obstacle: Privacy


Related Research: Extracting Patterns of Behaviour

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


Related Research: Extracting Patterns of Behaviour from Demographics

Clickable(minable) neighborhood demographic information:http://www.toronto.ca/demographics/profiles_map_and_index.htm


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 Their objective is to provide information for riders,

ours would be using this data to model the movement of people with a city for disease modeling and its possible spread


Related Research: Real-time Helsinki Public Transport Information


Related Research: Ubiquitous Vehicle Tracking Cameras

Ref: http://www.edmontontrafficcam.com/cams.php

Modeling Arterials for traffic flow.

ITS data useful for epidemic modeling

Similar data is available for air traffic.


Related Research: Extracting Patterns of Behaviour (Economic Impact) Economic Impact: Costs associated with implementing

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)


Related Research: Extracting Patterns of Behaviour (Economic Impact)


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.


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."


Other sources of information/concern Occasional/periodic mass gatherings E.g. Olympics or other special event that may perturb an

overall or 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 it offers an

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.


Mass Gatherings: Hajj

Mosque at Ka’bah

Tawaf, circumambulation of the Ka’bah


Related Research: Extracting Patterns of Behaviour (RFID tracking) Although not as explicit or readily attainable, the potential

to extract “patterns of behavior” and “interactions of agents” at critical institutions such as hospitals can be made more feasible through the use of RFID tracking.

As RFID sensor networks move from inventory solutions to enhanced applications, data collected from RFID tracking at clinics and hospitals can be envisioned as an input to DSSW. (e.g. WiFi Campus tracking)


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 some degree of 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


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.


Preparedness, planning and mitigation

Power generation: Remote maintained by “healthy” individuals: Stakeholders Hydro

Water Supply: Remote: MEMO

Food production/provisions: LocalStakeholders: Peak of the Market

Easily Isolated: Transportation wiseStakeholders: MEMO

Result: Pandemic Lag if Prepared


Multiple Hospital Model Patient Diversion : Future Work

Incorporate empirical data mined from sources such as

Google/Globis real-time traffic to estimate delays the

ambulance would experience enroute


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

Often no obvious connection to epidemic modeling Presented potential extensions: Utility of ABMs

Epidemics, Epizootics, ED Wait times Opportunities in preparedness planning, mitigation


Ideally one would like to model everything: (someday will) Threats: epidemic natural or bio-terrorist. (In progress)

Model impact of policy Model Food Supply:

Intensive unit production facilities through from birth to slaughter. (Proposal submitted, www.pork.org)

Model Food and Fuel Supply and Distribution: Guidelines for stock provisioning.

Model infrastructure: Transportation, water, power. Model impact of policy (Amenable to ABMs)

Assess interest in moving forward, from tertiary groups.


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.

Trying to get an interested parties 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


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 (50+ members)

A growing number of papers/proposals/talks.


Bob McLeodProfessor ECEUniversity of Manitoba

Internet Innovation CenterE3-416 EITCUniversity of ManitobaWinnipeg, ManitobaR3T 5V6

Email: [email protected]://www.iic.umanitoba.ca

IIC Contact: U of M ABM initiatives

Acknowledgements:Too many to list

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