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Modeling the effect of virus transmission on population using Systems Dynamics Modeling Dheeraj Manjunath 2008-2009 Computer Systems Lab TJHSST.

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Presentation on theme: "Modeling the effect of virus transmission on population using Systems Dynamics Modeling Dheeraj Manjunath 2008-2009 Computer Systems Lab TJHSST."— Presentation transcript:

1 Modeling the effect of virus transmission on population using Systems Dynamics Modeling Dheeraj Manjunath 2008-2009 Computer Systems Lab TJHSST

2 Project Goal Model to predict population based on virus transmission dynamics Easy access to variables-no programming necessary

3 Background Virus Transmission-spread of virus in population Systems Dynamics and Agent Based-types of modeling using individual agents and group flows. Virus-a sub-microscopic infectious agent that is unable to grow or reproduce outside a host cell. Viruses infect all cellular life. – Affect each person differently depending on: – Age – Health – Natural Susceptibility – Living Conditions

4 Background-Modeling Two major types: Agent Based Modeling  More popular  Individual agents simulate program  Pure Code Systems Dynamics Modeling  Flows of agents rather than individual agents  Flowchart and code

5 Agent Based Modeling Code to vary population that is not possible in Systems Dynamics Change variables where not possible in Systems Dynamics Easier access to change variables

6 Systems Dynamics Modeling Flows and Stocks Flows-flow from one stock or variable to another Example:

7 Development NetLogo 3.1.4 Integrated Systems Dynamics and Agent Based function

8 Development-Systems Dynamics Main focus of model General pattern and trend of population rather than individual. Use of Lotka-Volterra model:

9 Development-Systems Dynamics

10 Functions included: – Births – Deaths – Infections – Immunity – Other Human concern factors – All of the above for both adults and children

11 Development-Agent Based Interaction Used for individual agents More precise but little general trend information Code for: Infected population control Immunity Mutations

12 Sample Runs and Results Sample run from early Lotka-Volterra

13 Sample Runs and Results-cont. Comparable run from University of Michigan presentation

14 Conclusion and Results Self stabilizing system General sinusoidal trend of population, but each population varies with time and virus values. Future Additions: – Mutations – Immunity – Age Classes


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