Abstract
Recentstudiesshowthatthedegreedistributionof realistic contact networks impacts the prediction accuracy for diseasedynamicsduringanepidemic. Basedonthesurveillance dataoftheEbolaoutbreakin2014, notonlythebasicstructural knowledge degree distribution but also another structural knowledge clustering, affect the prediction accuracy for disease dynamics, and their impacts are different. In this paper, combining degree distribution with clustering, we design an new algorithm to predict disease dynamics with the improved accuracy. Based on our extensive experiments, we find that the structural knowledge (degree distribution and clustering) of contact networks is helpful to improve the prediction accuracy for disease dynamics, as compared with the algorithm that just considers the degree distribution of contact networks.
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