The Utility of Agent Based Models: Applications to Epidemics, Epizootics, Preparedness Planning, etc. — Opportunities for Research Robert D. Mc. Leod mcleod@ee. umanitoba. ca Professor ECE University of Manitoba Internet Innovation Centre (IIC) Dept. Electrical and Computer Engineering University of Manitoba © IIC, Jan. 2009 Internet Innovation Center
Overview n Part One: ABM Introduction q n Motivation: Interest in modeling complex systems Part Two: Examples of ABM Utility q q q Epidemic modeling: Discrete Space Scheduled Walker Epizootic modeling: Patient Access and Emergency Department Waiting Time Reduction n Part Three: Extensions and Opportunities n Summary/Discussion Interspersed with pop science references and questions Internet Innovation Center 1
Overview n Goals (Future) : A high utility ABM simulator q n Epidemic, preparedness, recovery, mitigation, policy Goals (Today): Garner Interest toward a MITAC$ grant q q Apply as a seed project May/09, w/blessing(collaboration) Looking for $20 K as matching funds (sources or leads) Internet Innovation Center 2
Part 1: Book Reviews/Motivation n “World Without Us”: Alan Weisman n “Pandemonium”: Andrew Nikiforuk n “The Numerati”: Stephen Baker n “Super Crunchers”: Ian Ayres n “The Tipping Point”: Malcolm Gladwell n “The Black Swan”: Nassim Taleb n “Fooled by Randomness”: Nassim Taleb n “The Man Who Knew Too Much: Alan Turing and the Invention of the Computer”: David Leavitt Internet Innovation Center 3
Part 1: Agent Based Modeling n General Interests in Complex Systems and Modeling n Much of this research resulted from a Programming Challenge n Make the “equations” as simple as possible, but not simpler, Albert Einstein n ABM is computational modeling essentially devoid of governing equations n ABMs are pure mathematics. n Is that a G. H. Hardy reference? No, it’s a G. Boole reference. Internet Innovation Center 4
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 Internet Innovation Center How? : Data Mining and Statistical Inferencing Refs: Wikipedia 5
Part 2: Agent Based Modeling Utility n App 1: Epidemic modeling - DSSW Model n n App 2: Epizootic modeling n n A nice attribute about ABMs in general is that they are ideal idea communication vehicles An extension to areas where ABMs have not been fully exploited App 3: Modeling an Emergency Department n Another area where ABM utility can be demonstrated Internet Innovation Center 6
App 1: Initial Specification for Epidemic Modeling n Basis idea: Data mine where possible the basic tenets of people-people interactions. (Often Disparate Sources) q q Topology: Data mined from maps Behaviour: Data mined from demographics n Our approach develops models based on “real” network topologies and “scheduled” walkers. n 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. Internet Innovation Center 7
The Model n Data mining is a common theme in modern information technology: q q q n 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: q q 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. Internet Innovation Center 8
“Where”: Topological Data Sources Google Earth with Overlays Google Maps Correct by construction small world topologies Internet Innovation Center 9
“Who and When” n Of similar importance to location (where), is the agents (who) are being infected. n This is data that is generally technically available but may be practically unavailable. n Our model attempts to illustrate how the data would be used if available. n 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. Internet Innovation Center 10
“What” n 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. n Example 1 of “stochastic” behaviour: q n Example 2 of “stochastic” behaviour: q n Modified schedule when ill: Low mobility when sick or getting sick. (agent “decides” to stay home) Weighted random schedule. (Don’t feel like going to work today) Example of contact: q Physical touch, third party (door knob), cough. Internet Innovation Center 11
Implementation n 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. n 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) Internet Innovation Center 12
“What if” I live here I take this bus I work here Internet Innovation Center 13 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. Internet Innovation Center 14
Analysis n Some data that is available on the corresponding web server Internet Innovation Center 15
Seasonal Variations n Seasonal variations are well known and provide fairly well “labeled” data for comparison n The figure illustrates the type of data available n Comparison allows for a tuning of parameters to more closely reflect actual data collected for a particular disease Internet Innovation Center 16
Mutations “tipping point” “Seasonal Variation” n n 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) Internet Innovation Center 17
App 1: DSSW Summary n n 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” Internet Innovation Center 18
App 2: ABM Potential for Epizootics n n Epizootics: “outbreak of disease affecting many animals” Agent based modeling of epizootics. n Domestic, feral, and/or natural “ABBOTSFORD, B. C. - The H 5 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 Internet Innovation Center 19
ABM Potential for epizootics n n Nicely “constrained” problem: Many Intensive Livestock Production Operations are nearly “Farrow to Fork” Best chances of ABM demonstrated utility n Cattle, swine and poultry 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 Internet Innovation Center 20
Similar ABMs for Poultry n n Broiler grow-out intensive unit production. Similar epizootic concerns Man made pathogen reservoir Similar problems in other monocultures Internet Innovation Center 21
Mobility and Infection Longevity Percent dead 100% Mobility/Longevity Impact Substantive 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” 5% 42% Population Internet Innovation Center 22
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 Internet Innovation Center 23
App 3: ABMs for Patient Access n Methods for reducing hospital Emergency Department waiting times and patient diversion. q q n 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 q Models patient flow through the modeling of individuals q (patients, doctors, service agents (registration, triage) Internet Innovation Center 24
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 Internet Innovation Center 25
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. Internet Innovation Center 26
Simulation “Proof of Concept” n Visual Simulation Suite Screenshot q q 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 Internet Innovation Center 27
Simulation Scenarios n City wide scenarios n Two EDs with two doctors, two EDs with three doctors, two EDs with four doctors. n Effect of different staffing levels is compared when there is no communication (i. e. no patient diversion) n Same basic scenario is used to compare patient diversion models. Internet Innovation Center 28
Simulation Scenario (Patient Diversion) n Patient diversion modeled using Random Early Detection (RED) algorithm from Telecommunication Network Engineering. n After a threshold in queue length is reached, the probability of a patient being diverted increases from 0. n Random RED, patients diverted to random ED n n Requires local ED information only Guided RED, patients probabilistically sent to EDs with fewer patients waiting n Requires city wide communication and coordination Internet Innovation Center 29
Simulations and results n Varying the number of Doctors, no patient diversion Two Doctors Queue Lengths: For fewer doctors queue lengths are longer. Three Doctors Four Doctors Internet Innovation Center 30
Simulations and results n 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 Internet Innovation Center 31
Demonstration: n Video on You. Tube n Extensions: q q Machine Learning for Policy and Provisioning Use the model as a starting environment for modeling the spread of an infectious disease within a Hospital. Internet Innovation Center 32
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 Internet Innovation Center 33
Part 3: Possible Extensions and data Mining Opportunities n n n At present DSSW epidemic ABM appears mainly well suited to “egalitarian” type diseases n “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 n Manitoba Hydro, Peak of the Market, Manitoba EMO, Public Safety, etc. n Preparedness planning, mitigation and recovery Internet Innovation Center 34
Data Mining Comment: n n n 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. Mine “what to mine” n Mining the “what” Data Fusion: combine data from multiple sources Internet Innovation Center Data Mining Data Fusion 35
DSSW Extensions: Hierarchy n n Incorporate Hierarchy n Intracity and Intercity n Basic modality remains: data-driven models of discrete space- and time- walkers, mined from available sources. Cities are largely autonomous n Allows for the problem to remain tractable and allow for efficient modes of computation (parallelism can be exploited). Internet Innovation Center 36
Extensions: Extracting Patterns of Behaviour n n Patterns of behavior can be taken from tracking technologies that are in place albeit not mined for use in epidemic modeling. n E. g. Financial Transaction Profiling n Usually mined to detect fraud n E. g. Cell phone tracking, “where are you” services n By default the service provider already knows where you are, even more so with GPS Obstacle: Privacy Internet Innovation Center 37
Related Research: Extracting Patterns of Behaviour n n Consumer wireless electronics: MAC snooping and tracking. (non obvious data source) n Bluetooth headsets (ingress and egress of signalized arterials) n Similar protocols for Wi. Fi n Device-enabled Kiosks and vending machines Security cameras and systems with person detection n Monitoring for behaviour patterns those of illegal activities and terrorist threats Internet Innovation Center 38
Related Research: Extracting Patterns of Behaviour from Demographics Clickable(minable) neighborhood demographic information: http: //www. toronto. ca/demographics/profiles_map_and_index. htm Internet Innovation Center 39
Related Research: Extracting Patterns of Behaviour continued n n Tracking subway ridership. n Token data mining of ridership n Their Objective: Bioterrorism impact Mining online transportation information systems n Helsinki public transport n 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 Internet Innovation Center 40
Related Research: Real-time Helsinki Public Transport Information Internet Innovation Center 41
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 Internet Innovation Center 42
Related Research: Extracting Patterns of Behaviour (Economic Impact) n Economic Impact: Costs associated with implementing policy. (ref: Brookings) n Specifically, the economic impact of restricting air travel as a policy in controlling a flu pandemic. n Models global air travel and estimates impact and cost associated with travel restrictions. n E. g. 95% travel restriction required before significantly impairing disease spread n Not a surprise (also they removed edges not vertices, cf. percolation) Internet Innovation Center 43
Related Research: Extracting Patterns of Behaviour (Economic Impact) Internet Innovation Center 44
Related Research: Google’s Flu trends n n 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. Internet Innovation Center 45
Related Opportunity: Google’s Gmail n n n 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. " Internet Innovation Center 46
Other sources of information/concern n Occasional/periodic mass gatherings E. g. Olympics or other special event that may perturb an overall or global simulation E. g. The Hajj n Largest mass pilgrimage in the world. n 2007 an estimated 2 -3 million people participated. n Conditions are difficult and thus it offers an opportunity for a large scale disease such as influenza to take hold. n These people then disperse to their home countries, many via public transport, and could easily influence the spread and outbreak of the disease. Internet Innovation Center 47
Mass Gatherings: Hajj Tawaf, circumambulation of the Ka’bah Mosque at Ka’bah Internet Innovation Center 48
Related Research: Extracting Patterns of Behaviour (RFID tracking) n 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. n 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. Wi. Fi Campus tracking) Internet Innovation Center 49
Preparedness, planning and mitigation n 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. n This aspect can be “catastrophe agnostic” n Simple investigations as to how long food/fuel/medical supplies would last and could be distributed will be modeled Internet Innovation Center 50
Preparedness, planning and mitigation n Provisioning of resources extempore will lead to an aggravated and worsening disaster. n Models can become an effective tool for any city. n Specific model to their region n Allowing for provisioning not only of supplies but for inoculation services as well as temporary hospital and/or mortuary facilities. Internet Innovation Center 51
Preparedness, planning and mitigation Power generation: Remote maintained by “healthy” individuals: Stakeholders Hydro Easily Isolated: Transportation wise Stakeholders: MEMO Food production/provisions: Local Stakeholders: Peak of the Market Water Supply: Remote: MEMO Result: Pandemic Lag if Prepared Internet Innovation Center 52
Multiple Hospital Model Patient Diversion : Future Work n Incorporate empirical data mined from sources such as Google/Globis real-time traffic to estimate delays the ambulance would experience enroute Internet Innovation Center 53
Summary n n n Presented our Agent Based Modeling approach to high “utility” simulation. q Emphasis on data mining of spatial topologies and agent behavior patterns Presented several indirect data sources q Often no obvious connection to epidemic modeling Presented potential extensions: Utility of ABMs n Epidemics, Epizootics, ED Wait times n Opportunities in preparedness planning, mitigation Internet Innovation Center 54
Ideally one would like to model everything: (someday will) n n n Threats: epidemic natural or bio-terrorist. (In progress) n Model impact of policy Model Food Supply: n Intensive unit production facilities through from birth to slaughter. (Proposal submitted, www. pork. org) Model Food and Fuel Supply and Distribution: n Guidelines for stock provisioning. Model infrastructure: Transportation, water, power. n Model impact of policy (Amenable to ABMs) Assess interest in moving forward, from tertiary groups. Internet Innovation Center 55
Exploring research opportunities n Being “devoid” of equations, agent based models allow for a tradeoffs between specificity and utility. n 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. n Trying to get an interested parties to provide some degree of matching funds to apply for a MITACS seed grant. May 2009. n Total matching funds we are targeting is 20 K, providing 70 K of funding if successful. n Leverage other efforts: Possible with some traction here Internet Innovation Center 56
Dissemination efforts: n Epi-at-home. com: Future home of Epidemic ABM open source project (DSSW) n Bio-inference. ca: Future home of ABM and data mining opportunities (non obvious sources) n n Epizootic, patient access, preparedness planning Facebook group: “Pandemic Awareness Day” n n n Exploring social networks as an information tool A non invasive information portal (50+ members) A growing number of papers/proposals/talks. Internet Innovation Center 57
IIC Contact: U of M ABM initiatives Bob Mc. Leod Professor ECE University of Manitoba Internet Innovation Center E 3 -416 EITC University of Manitoba Winnipeg, Manitoba R 3 T 5 V 6 Acknowledgements: Too many to list Email: mcleod@EE. UManitoba. CA http: //www. iic. umanitoba. ca Internet Innovation Center 58
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