Orientation & Selected Guest Lecture Models Nathaniel Osgood Agent-Based Modeling Bootcamp for Health Researchers August 22, 2011
Goals of Bootcamp • To expose participants to basics of building agent- based models (ABMs) in AnyLogic • To convey a sense as to the capabilities, strengths and weaknesses of agent-based modeling • To train AnyLogic users so that they are comfortable running, understanding many elements of, and performing small modifications on ABMs • To provide aspiring modelers with hands-on familiarity with the capabilities & functions of AnyLogic and sufficient exposure to Java programming to navigate within models • To help create informed consumers of Agent-based models
Non-Goals of the Bootcamp • To serve as a comprehensive survey of Agent-Based modeling • To give a thorough conceptual or theoretic framework for understanding and analyzing Agent-Based Models • To review the tradeoffs between AnyLogic & other ABM platforms • To create a set of fully proficient Agent-Based modelers • To render participants into Java programmers • To sell participants on the superiority of AnyLogic, or the desirability of buying AnyLogic licenses
Ways Seeking to Resemble a Bootcamp …. • Intense • Long hours • Willingness to push limits • No frills • Development of sense of camaraderie • Tolerance of a loud & (technologically) aggressive instructor • Thorough exercise of (brain) muscles • Looking back at end – Sense of accomplishment – New appreciation of what are capable of
Ways Seeking to Avoid Resembling a Bootcamp …. • No derogatory language or demeaning humour • Breaking down barrier between instructor/TAs & participants • Willingness of participants to speak up with – Questions – Suggestions – Objections • Rich dialogue within room • Inclusive atmosphere
Notes on the Schedule • Each day, schedule incorporates – Many (primarily) hands-on plenary lectures – Guest lectures highlighting ABM applications in health – Parallel sessions w/more advanced material/exercises – Breaks – Discussion periods • Color coded by level of material & nature of session • The schedule is not immutable: Subject to change based on – Sense of whethre more breaks are required – Student interest & feedback – Perception of comfort with & absorption of material
Teaching Assistants • Yuan Tian (Head TA, Computer Science) – Formal Health & CS training; Tuberculosis prevention & control application, contact tracing protocol simulation, TB&Smoking • Amy Gao (Computer Science) – Diabetes Epidemiology, Diabetic & ESRD, Health Care Cost estimation with models, leveraging database tie-ins • Yudi Xue (Computer Science) – AnyLogic “under the hood”, agent subtyping, software for modeling process facilitation via versioning&collaboration • Anqi Dong (Walter Murray Collegiate Institute) – ABM for evaluating effectiveness of treatment prioritization strategies, evaluating Bayesian inferencing • Weicheng Qian (Computer Science) – Agent mobility modeling, Bayesian estimation & filtering
Other Participants w/ABM Exposure • David Vickers – Immuno-epidemiological modeling with ABM- System Dynamics Hybrids, STIs, N. meningitidis • Mohammad Hashemian – Incorporation of person-place & person-person sensor micro-contact data into ABM simulations • Cheryl Waldner – Help advise on construction of CWD model, Zoonoses, Veterinary Medicine, H1N1 modeling
Teaching Assistant Roles • Assisting participants with ad-hoc questions & modeling difficulties (e.g. with exercises) • Discussing Details of presented & example models • Brainstorming on ABM applications • Advising on AnyLogic capabilities, functions & limitations • Consultations on modeling topics • Guest lectures
Practical Essentials • Java & AnyLogic Sessions held in Spinks S386 • Longer consultations with TAs/instructor can be held in Spinks S371 • Washrooms & water fountain are down the hall to left • Quiet lounge area (e.g. for phone conversations) is to the right • USB Stick contains – Binder contents – Sample models – Preliminary Presentations • Red Dots Identify TAs, Yellow Dots all Volunteers
Food • “ Bootcamp Breakfasts” and snacks will be provided in hallway outside S320 • Lunches (& lunch talks) in Faculty Club – Some downstairs, some upstairs • Dinners on your own (restaurants suggestions in binder & on USB drive)
IT Essentials • Your user name & password should be in the back of your nametag – Please log in with this information throughout the workshop • Once logged in to a machine, go to windows explorer & “X:” drive to find files from instructor – Preliminary Lecture Slides/Exercises/Example Models (take home & not take home)/Binder content • Your files will be stored with your account, not with the particular machine – But try to avoid relying on files stored to desktop (store to “My documents” instead). • WiFi Wireless networks are available – Use “guest” as network (SSID), “visitor” as both user name & password
Finding S371 & S386 S386 (for AnyLogic & Java (Ignore Green Dots!) Tutorials) Path from S320 to S371 S386 S320 I am here! S371 S371 (for Consultations with TAs & Instructor)
Exercises: Prerequisites A First Encounter With Anylogic: Modifying A Simple Sample Model Building a Minimalist Network-Based Model Framework Incorporating Attribute Heterogeneity A Simple Network Based Infection Spread Among Agents Model Building A Minimalist Two-Population Model Framework A Simple Debugging Exercise Legend Strength of Prerequisite Relationships Recommended Required Target Skill Level Introductory Basic Intermediate
Glimpse of Guest Lectures (Primarily over Lunch) • TB • CWD • ESRD • Treatment Prioritization • ABMs for Reliability of Inferencing • HPV & Smoking • Alzheimer’s Disease • SILVER
Tuberculosis Spread, Prevention & Control (Earlier Version) 16
Chronic Wasting Disease
Health & Cost Implications of Diabetic ESRD
HPV & Smoking
SILVER Versioning & Collaboration System
κ y u v Free virus Within Host β 1 x v Model ay+dx+p φ zy p φ zy Uninfected Infected Cells Cells β 2 xy dx ay cyw Clonally- z 0 /m hz Memory Activated expanding CTLs CD8+ cells CD8+ cells c θ w bw z 8 /m
Network Embedded Individuals Virion Production Rate Mean of Viral Load Virion Production Rate if 1 Person Per Contact Virions Rate of Neighbors Non Quantized Infection Mean Viral Load <Population Size> Virus Load Virion Clearance Virion Production From Infected Cells Likelihood Density of Mean Virion Uninfected Cell Infection by Single Virion Lifetime Replentishment Rate Per Infected CellVirion Uninfected Infected Production Rate Cells Cells infected cell death New Cell Uninfected Cell Infections by CTLs rate which infected cells Replentishment Infected Cell Uninfected Cell are killed by CTLs Death death <Population Size> Mean Infected Mean Uninfected Mean Uninfected Cells <Population Size> Cell Lifetime Mean Infected Cell Cells Lifetime Mean CTL CTL CTLs responsiveness immune response to lifespan CTL turnover infected cells
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