Időpont: 2020.06.30., 14:20-14:40
Kivonat: Network epidemic models have been widely and successfully used for describing and understanding real-world infection propagation. The mathematical framework is given by a continuous time Markov-chain and its nonlinear dynamical system approximations. The general question is to understand how the structure of the underlying network affects the spread of the infection. In this project, a model network of a city or a country is built up and different network epidemic approximations are applied to understand the present infection propagation. The first step is a multilayer network construction. The layers of the network are as follows: households, workplaces-schools, geometric location, community places (e.g. stores and medical centers). For given values of the parameters a so-called Gillespie stochastic simulation can be run on the network to obtain the number of infected and recovered nodes as a function of time. The main quantity of interest is the maximal number of infected nodes during the process, and the number of recovered (immune) nodes at the end of the process. The goal of the research is to investigate how these numbers are affected by policy measures like school closures, or restrictions in shopping times.