Literature DB >> 34328080

Social fluidity mobilizes contagion in human and animal populations.

Ewan Colman1,2, Vittoria Colizza3, Ephraim M Hanks4, David P Hughes5, Shweta Bansal1.   

Abstract

Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.
© 2021, Colman et al.

Entities:  

Keywords:  animal behaviour; computational biology; density dependence; epidemiology; frequency dependence; global health; infectious disease; mathematical modelling; none; sociality; systems biology

Mesh:

Year:  2021        PMID: 34328080      PMCID: PMC8324292          DOI: 10.7554/eLife.62177

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


  45 in total

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Review 2.  The architecture of complex weighted networks.

Authors:  A Barrat; M Barthélemy; R Pastor-Satorras; A Vespignani
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3.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

Authors:  O Diekmann; J A Heesterbeek; J A Metz
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4.  Stress response, gut microbial diversity and sexual signals correlate with social interactions.

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5.  Population viscosity suppresses disease emergence by preserving local herd immunity.

Authors:  Timothy C Reluga; Eunha Shim
Journal:  Proc Biol Sci       Date:  2014-12-07       Impact factor: 5.349

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Authors:  Pratha Sah; Janet Mann; Shweta Bansal
Journal:  J Anim Ecol       Date:  2018-01-22       Impact factor: 5.091

7.  Using Social Network Measures in Wildlife Disease Ecology, Epidemiology, and Management.

Authors:  Matthew J Silk; Darren P Croft; Richard J Delahay; David J Hodgson; Mike Boots; Nicola Weber; Robbie A McDonald
Journal:  Bioscience       Date:  2017-02-01       Impact factor: 8.589

8.  Time varying networks and the weakness of strong ties.

Authors:  Márton Karsai; Nicola Perra; Alessandro Vespignani
Journal:  Sci Rep       Date:  2014-02-10       Impact factor: 4.379

9.  Predicting epidemic risk from past temporal contact data.

Authors:  Eugenio Valdano; Chiara Poletto; Armando Giovannini; Diana Palma; Lara Savini; Vittoria Colizza
Journal:  PLoS Comput Biol       Date:  2015-03-12       Impact factor: 4.475

10.  The reachability of contagion in temporal contact networks: how disease latency can exploit the rhythm of human behavior.

Authors:  Ewan Colman; Kristen Spies; Shweta Bansal
Journal:  BMC Infect Dis       Date:  2018-05-15       Impact factor: 3.090

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  1 in total

1.  A Metapopulation Model for Preventing the Reintroduction of Bovine Viral Diarrhea Virus to Naïve Herds: Scotland Case Study.

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