The Impact of Graph Density and Infection Probability on Disease Spread in Temporal Networks
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Epidemiological processes, such as the spread of a disease through a population, can be represented in a simple way by applying SI dynamics to temporal networks. The spreading behavior is observed by running simulations and comparing the results against theoretical approximations using differential equations. Two types of graphs are used in the simulations: Bernoulli graphs, which are built based on probability, and Barabasi-Albert graphs, which are built using the concept of preferential attachment . For the first type, we use mean field theory to approximate the behavior, while for the second type we use a degree-based mean field approximation to account for the difference in structure. The main focus of this study is on the impact of two specific variables on spreading behavior, namely, graph density and infection probability. Here we research the effects of changes in these variables independently as well as in conjunction. Results show that changes in graph density and infection probability affect the overall percentage of the population that becomes infected and the time it takes the infection to fully spread.