Modeling the effect of virus transmission on population using Systems Dynamics Modeling Dheeraj Manjunath Computer Systems Lab TJHSST
Project Goal Model to predict population based on virus transmission dynamics Easy access to variables-no programming necessary
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
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
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
Systems Dynamics Modeling Flows and Stocks Flows-flow from one stock or variable to another Example:
Development NetLogo Integrated Systems Dynamics and Agent Based function
Development-Systems Dynamics Main focus of model General pattern and trend of population rather than individual. Use of Lotka-Volterra model:
Development-Systems Dynamics
Functions included: – Births – Deaths – Infections – Immunity – Other Human concern factors – All of the above for both adults and children
Development-Agent Based Interaction Used for individual agents More precise but little general trend information Code for: Infected population control Immunity Mutations
Sample Runs and Results Sample run from early Lotka-Volterra
Sample Runs and Results-cont. Comparable run from University of Michigan presentation
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