Inferring network properties based on the epidemic prevalence

Ma, Ling; Liu, Qiang; Mieghem, Piet Van

doi:10.1007/s41109-019-0218-0

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…The remaining task is to estimate the infection probabilities β ij . The goal of network inference (Peixoto 2019;Ma et al 2019;Di Lauro et al 2019;Timme and Casadiego 2014;Wang et al 2016) is to estimate the matrix B of infection probabilities from the SIR viral state observations…”

Section: Network Inferencementioning

confidence: 99%

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Prasse

Achterberg

et al. 2020

Appl Netw Sci

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At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose the Network-Inference-Based Prediction Algorithm (NIPA) to forecast the future prevalence of the COVID-19 epidemic in every city. Our results indicate that NIPA is beneficial for an accurate forecast of the epidemic outbreak.

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Section: Network Inferencementioning

confidence: 99%

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Prasse

Achterberg

et al. 2020

Appl Netw Sci

Self Cite

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“…Our aim in this paper is to establish the feasibility of inferring the class of the underlying network from population-level observations. Whilst a very recent paper 21 provides an algorithm to infer properties of a given network-type from prevalence data, we are not aware (for a survey, see 5 ) of any research that specifically addresses the problem of network class inference based purely on population-level observations. We do so within the framework of continuous-time SIS epidemics on networks when only population-level data from a single realisation of the epidemic are available.…”

Section: Introductionmentioning

confidence: 99%

Network inference from population-level observation of epidemics

Lauro

Croix

Dashti

et al. 2020

Sci Rep

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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.

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“…More importantly perhaps, the solution of the PDE serves as a likelihood which can be very efficiently computed/evaluated and can form the basis of many networks and epidemic inference models, see Section 3.3. This is in contrast with approaches where the networks are explicitly modelled [25] and computational complexity can make inference out of reach.…”

Section: Discussionmentioning

confidence: 95%

PDE-limits of stochastic SIS epidemics on networks

Lauro¹,

Croix²,

Berthouze³

et al. 2020

Preprint

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Stochastic epidemic models on networks are inherently high-dimensional and the resulting exact models are intractable numerically even for modest network sizes. Mean-field models provide an alternative but can only capture average quantities, thus offering little or no information about variability in the outcome of the exact process. In this paper we conjecture and numerically prove that it is possible to construct PDE-limits of the exact stochastic SIS epidemics on regular and Erdős-Rényi networks. To do this we first approximate the exact stochastic process at population level by a Birth-and-Death process (BD) (with a state space of O(N ) rather than O(2 N )) whose coefficients are determined numerically from Gillespie simulations of the exact epidemic on explicit networks. We numerically demonstrate that the coefficients of the resulting BD process are density-dependent, a crucial condition for the existence of a PDE limit. Extensive numerical tests for Regular and Erdős-Rényi networks show excellent agreement between the outcome of simulations and the numerical solution of the Fokker-Planck equations. Apart from a significant reduction in dimensionality, the PDE also provides the means to derive the epidemic outbreak threshold linking network and disease dynamics parameters, albeit in an implicit way. Perhaps more importantly, it enables the formulation and numerical evaluation of likelihoods for epidemic and network inference as illustrated in a fully worked out example.

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Inferring network properties based on the epidemic prevalence

Cited by 8 publications

References 44 publications

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Network inference from population-level observation of epidemics

PDE-limits of stochastic SIS epidemics on networks

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