Compensating for population sampling in simulations of epidemic spread on temporal contact networks

Abstract

Data describing human interactions often suffer from incomplete sampling of the underlying population. As a consequence, the study of contagion processes using data-driven models can lead to a severe underestimation of the epidemic risk. Here we present a systematic method to alleviate this issue and obtain a better estimation of the risk in the context of epidemic models informed by high-resolution time-resolved contact data. We consider several such data sets collected in various contexts and perform controlled resampling experiments. We show how the statistical information contained in the resampled data can be used to build a series of surrogate versions of the unknown contacts. We simulate epidemic processes on the resulting reconstructed data sets and show that it is possible to obtain good estimates of the outcome of simulations performed using the complete data set. We discuss limitations and potential improvements of our method.

References in corpus (13)

• Structure and tie strengths in mobile communication networks
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• What's in a crowd? Analysis of face-to-face behavioral networks
• Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• Contact patterns among high school students
• Mitigation of infectious disease at school: targeted class closure vs school closure
• Inferring the mesoscale structure of layered, edge-valued and time-varying networks
• An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
• Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
• Quasi-stationary analysis of the contact process on annealed scale-free networks
• On the Bias of Traceroute Sampling; or, Power-law Degree Distributions in Regular Graphs
• Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread

Cited by in corpus (23)

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• The structured backbone of temporal social ties
• Predictability of real temporal networks
• Sampling of Temporal Networks: Methods and Biases
• Network reconstruction via density sampling
• How to estimate epidemic risk from incomplete contact diaries data?
• Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks
• Estimating the epidemic risk using non-uniformly sampled contact data
• Building surrogate temporal network data from observed backbones
• Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
• Estimating the outcome of spreading processes on networks with incomplete information: a mesoscale approach
• Emerging Activity Temporal Hypergraph: a model for generating realistic time-varying hypergraphs
• Dose-response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study
• Compression-based inference of network motif sets
• Generating surrogate temporal networks from mesoscale building blocks
• Compressing the chronology of a temporal network with graph commutators
• Detecting contagious spreading of urban innovations on the global city network
• Measuring close proximity interactions in summer camps during the COVID-19 pandemic
• Indirect interactions influence contact network structure and diffusion dynamics
• Diffusion on dynamic contact networks with indirect transmission links