Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Mean Field at Distance One
Stockholm University, Faculty of Science, Department of Mathematics. Utrecht University, The Netherlands; University Medical Center Utrecht, The Netherlands.ORCID iD: 0000-0002-7596-7641
2017 (English)In: Temporal Network Epidemiology / [ed] Naoki Masuda and Petter Holme, Singapore: Springer, 2017, p. 105-128Chapter in book (Refereed)
Abstract [en]

To be able to understand how infectious diseases spread on networks, it is important to understand the network structure itself in the absence of infection. In this text we consider dynamic network models that are inspired by the (static) configuration network. The networks are described by population-level averages such as the fraction of the population with k partners, k = 0, 1, 2,  This means that the bookkeeping contains information about individuals and their partners, but no information about partners of partners. Can we average over the population to obtain information about partners of partners? The answer is ‘it depends’, and this is where the mean field at distance one assumption comes into play. In this text we explain that, yes, we may average over the population (in the right way) in the static network. Moreover, we provide evidence in support of a positive answer for the network model that is dynamic due to partnership changes. If, however, we additionally allow for demographic changes, dependencies between partners arise. In earlier work we used the slogan ‘mean field at distance one’ as a justification of simply ignoring the dependencies. Here we discuss the subtleties that come with the mean field at distance one assumption, especially when demography is involved. Particular attention is given to the accuracy of the approximation in the setting with demography. Next, the mean field at distance one assumption is discussed in the context of an infection superimposed on the network. We end with the conjecture that an extension of the bookkeeping leads to an exact description of the network structure.

Place, publisher, year, edition, pages
Singapore: Springer, 2017. p. 105-128
Series
Theoretical Biology, ISSN 2522-0438
National Category
Biological Sciences Mathematics
Identifiers
URN: urn:nbn:se:su:diva-151759DOI: 10.1007/978-981-10-5287-3_5ISBN: 978-981-10-5286-6 (print)ISBN: 978-981-10-5287-3 (electronic)OAI: oai:DiVA.org:su-151759DiVA, id: diva2:1175488
Available from: 2018-01-18 Created: 2018-01-18 Last updated: 2018-01-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Leung, Ka Yin
By organisation
Department of Mathematics
Biological SciencesMathematics

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf