Network inference from population-level observation of epidemics

Di Lauro, F, Croix, J -C, Dashti, M, Berthouze, L and Kiss, I Z (2020) Network inference from population-level observation of epidemics. Scientific Reports, 10. a18779 1-14. ISSN 2045-2322

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Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings. To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics. We use extensive simulations over Regular, Erdős–Rényi and Barabási–Albert networks to build network class-specific priors for these rates. We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic. We show that the proposed methodology yields good results on both synthetic and real-world networks.

Item Type: Article
Keywords: Epidemics, Networks, Inference, PDEs, Fokker-Planck
Schools and Departments: School of Engineering and Informatics > Informatics
School of Mathematical and Physical Sciences > Mathematics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 28 Sep 2020 10:22
Last Modified: 03 Nov 2020 15:15

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