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Network inference from population-level observation of epidemics

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Version 2 2023-06-12, 09:30
Version 1 2023-06-09, 21:41
journal contribution
posted on 2023-06-12, 09:30 authored by Francesco Di Lauro, J -C Croix, Masoumeh DashtiMasoumeh Dashti, Luc BerthouzeLuc Berthouze, Istvan Kiss
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, Erdos–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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Scientific Reports

ISSN

2045-2322

Publisher

Nature Research

Volume

10

Page range

1-14

Article number

a18779

Pages

17.0

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2020-09-28

First Open Access (FOA) Date

2020-11-03

First Compliant Deposit (FCD) Date

2020-09-25

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