Simulation of agent based disease spread models

Abstract

Disease spread models can be used to see how different parameters might affect the spread of a particular disease and to know how a disease can be controlled before it becomes common in public. By simulating agent based disease spread models, it is aimed, in case of an outbreak, to predict the number of people that will get affected and to be able to take important precautions such as antiviral agents as a preventive mechanism before people become infected. In this thesis, agent based simulation of disease spread models is studied and Turkish population structure is considered. Possible data structures to be included in the simulation, possible infection parameter values and contact structures are analyzed. A synthetic population structure is formed and the simulation is done using this population structure. A function that calculates approximate R0 value for a given population to calibrate infection probabilities is built. The relation between the R0 value and the expected outbreak behavior is analyzed in detail. The reliability of R0 value described in the literature is discussed. The simulation model is an improvable model in case that new parts are added to the population structure or that any small detail is required to be changed. The model and the population structure are highly flexible since they are developed using R mathematical programming language starting from the beginning.