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• W2121476890 abstract "Controlling epidemic outbreaks is, and has throughout history been, a major problem for society. Vaccination campaigns are important events that can be used to mitigate the spread of epidemics. Vaccination of an entire population is rarely feasible in real-world situations and therefore it is of interest to find methods that can predict important individuals which, when vaccinated, have a significant contribution to the immunization of a population. A majority of the previous research on network epidemiology has focused on static network topologies, where contacts are modeled as permanent events. Recently, scientists have started to use temporal networks, where contacts are not continuously active in time, for contagious disease studies. In this thesis, we propose a method to estimate the future importance of individuals with respect to disease spreading. The proposed method is based on practically obtainable local contact information of individuals and could thus be implemented in practice. In order to evaluate the efficiency of the proposed method, it is implemented as a vaccination strategy. We study the performance of the vaccination strategy with the help of computer simulations of diseases which spread upon person to person contact in a number of real-world temporal networks. With the help of comparative studies, we find that the proposed method outperforms a benchmark vaccination strategy for a number of empirical networks. This indicates that the method is able to estimate important individuals with respect to disease spreading." @default.
• W2121476890 created "2016-06-24" @default.
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• W2121476890 date "2012-01-01" @default.
• W2121476890 modified "2023-09-24" @default.
• W2121476890 title "Forecasting important disease spreaders from temporal contact data" @default.
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