ArticlePloS one2013
Incorporating disease and population structure into models of SIR disease in contact networks.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Generalized contact matrices allow integrating socioeconomic variables into epidemic models.Science advances · 2024Article
- John Cross, epidemic theory, and mathematically modeling the Norwich smallpox epidemic of 1819.PloS one · 2024Article
- Epidemic graph diagrams as analytics for epidemic control in the data-rich era.Nature communications · 2023Article
- How Asymptomatic Transmission Influences Mitigation and Suppression Strategies during a Pandemic.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Networks of necessity: Simulating COVID-19 mitigation strategies for disabled people and their caregivers.PLoS computational biology · 2022Article
- Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study.IEEE transactions on computational social systems · 2021Article
- The role of social structure and dynamics in the maintenance of endemic disease.Behavioral ecology and sociobiology · 2021Review
- A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies.PLoS computational biology · 2020Article
- Data-driven contact structures: From homogeneous mixing to multilayer networks.PLoS computational biology · 2020Article
- Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC.Biomath (Sofia, Bulgaria) · 2019Article
- SIR dynamics in random networks with communities.Journal of mathematical biology · 2018Article
- Dynamics of social contagions with local trend imitation.Scientific reports · 2018Article
- Determining whether a class of random graphs is consistent with an observed contact network.Journal of theoretical biology · 2018Article
- Multiple peaks patterns of epidemic spreading in multi-layer networks.Chaos, solitons, and fractals · 2018Article
- Modeling livestock population structure: a geospatial database for Ontario swine farms.BMC veterinary research · 2018Article
- Spread of competing viruses on heterogeneous networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017Article
- Mathematical models of SIR disease spread with combined non-sexual and sexual transmission routes.Infectious Disease Modelling · 2017Article
- Article
- Stochastic epidemic dynamics on extremely heterogeneous networks.Physical review. E · 2016Article
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2 authors.
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Abstract
We consider the recently introduced edge-based compartmental models (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks. These models differ from standard infectious disease models by focusing on the status of a random partner in the population, rather than a random individual. This change in focus leads to simple analytic models for the spread of SIR diseases in random networks with heterogeneous degree. In this paper we extend this approach to handle deviations of the disease or population from the simplistic assumptions of earlier work. We allow the population to have structure due to effects such as demographic features or multiple types of risk behavior. We allow the disease to have more complicated natural history. Although we introduce these modifications in the static network context, it is straightforward to incorporate them into dynamic network models. We also consider serosorting, which requires using dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions.
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