s41598-020-75558-9.pdf (2.38 MB)
Network inference from population-level observation of epidemics
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 KissUsing 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 ReportsISSN
2045-2322Publisher
Nature ResearchExternal DOI
Volume
10Page range
1-14Article number
a18779Pages
17.0Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2020-09-28First Open Access (FOA) Date
2020-11-03First Compliant Deposit (FCD) Date
2020-09-25Usage metrics
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