ArticlePLoS computational biology2013
Epidemic spread on weighted networks.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hotspot model shows how location-based superspreading accelerates and reshapes epidemics.PNAS nexus · 2025Article
- New approaches to epidemic modeling on networks.Scientific reports · 2023Article
- Multi-layer network approach in modeling epidemics in an urban town.The European physical journal. B · 2023Article
- Efficient spread-size approximation of opinion spreading in general social networks.Physical review. E · 2019Article
- Prediction of Pig Trade Movements in Different European Production Systems Using Exponential Random Graph Models.Frontiers in veterinary science · 2017Article
- Super-Spreader Identification Using Meta-Centrality.Scientific reports · 2016Article
- Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.PLoS computational biology · 2016Article
- How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?PLoS computational biology · 2016Article
- Mathematical and computational approaches to epidemic modeling: a comprehensive review.Frontiers of computer science · 2015Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion (r0) and the basic reproductive ratio (R0), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases.
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