ArticleScientific reports2017
Estimating the epidemic risk using non-uniformly sampled contact data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling.PLoS computational biology · 2024Article
- Spatial immunization to abate disease spreading in transportation hubs.Nature communications · 2023Article
- Statistical inference links data and theory in network science.Nature communications · 2022Review
- Measuring close proximity interactions in summer camps during the COVID-19 pandemic.EPJ data science · 2022Article
- Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection.Proceedings. Biological sciences · 2021Article
- Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections.PLoS computational biology · 2021Article
- Sensor-based proximity metrics for team research. A validation study across three organizational contexts.Behavior research methods · 2021Article
- The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts.Proceedings. Biological sciences · 2018Article
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Authors and funding
2 authors.
Funding
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Abstract
Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.
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Registered trials
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