Identifying Asymptomatic Nodes in Network Epidemics using Betweenness Centrality
Resumo
Epidemics of certain viruses in a population can have major impact effects, as is the case in the recent global pandemic caused by the COVID-19 virus. Identifying infected individuals during the course of an epidemic is extremely important for measuring spread and designing more effective control measures. However, in some epidemics infected individuals do not exhibit clear symptoms despite being infected and contributing to the contagion of others (called asymptomatic). This work addresses the problem of identifying asymptomatic individuals in network epidemics based on the observation of infected (symptomatic) individuals. The main contribution of this work is the evaluation of different centrality measures to identify asymptomatic individuals when a fraction of the infected nodes in a network epidemic is observed at a given moment in time. In particular, a variation of the betweenness centrality measure is proposed in this work. An evaluation using different network models and different asymptomatic rates shows that the proposed centrality measure outperforms other centrality measures in many scenarios. Furthermore, the performance of centrality measures increases as the fraction of asymptomatic decreases, showing an interesting trade-off.
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