Evidence map›Paper›PMID 28692130›Full record

ArticleThe Journal of animal ecology2018

The primary case is not enough: Variation among individuals, groups and social networks modify bacterial transmission dynamics.

Carl N Keiser, Noa Pinter-Wollman, Michael J Ziemba, Krishna S Kothamasu, Jonathan N Pruitt

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of animal ecology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 37 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Carl N KeiserDepartment of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0002-4936-7810
Noa Pinter-WollmanDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.
Michael J ZiembaDepartment of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
Krishna S KothamasuDepartment of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
Jonathan N PruittDepartment of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, USA.
University of Pittsburgh · USUniversity of California, Los Angeles · USUniversity of California, Santa Barbara · US

Funding

Modeling how keystone individuals emerge and influence disease transmissionR01GM115509 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PINTER-WOLLMAN, NOA MICHAL · 2016 to 2020
$2.0M
NIGMS NIH HHS R01 GM115509
6 · The paper itself

Abstract

The traits of the primary case of an infectious disease outbreak, and the circumstances for their aetiology, potentially influence the trajectory of transmission dynamics. However, these dynamics likely also depend on the traits of the individuals with whom the primary case interacts. We used the social spider Stegodyphus dumicola to test how the traits of the primary case, group phenotypic composition and group size interact to facilitate the transmission of a GFP-labelled cuticular bacterium. We also compared bacterial transmission across experimentally generated "daisy-chain" vs. "star" networks of social interactions. Finally, we compared social network structure across groups of different sizes. Groups of 10 spiders experienced more bacterial transmission events compared to groups of 30 spiders, regardless of groups' behavioural composition. Groups containing only one bold spider experienced the lowest levels of bacterial transmission regardless of group size. We found no evidence for the traits of the primary case influencing any transmission dynamics. In a second experiment, bacteria were transmitted to more individuals in experimentally induced star networks than in daisy-chains, on which transmission never exceeded three steps. In both experimental network types, transmission success depended jointly on the behavioural traits of the interacting individuals; however, the behavioural traits of the primary case were only important for transmission on star networks. Larger social groups exhibited lower interaction density (i.e. had a low ratio of observed to possible connections) and were more modular, i.e. they had more connections between nodes within a subgroup and fewer connections across subgroups. Thus, larger groups may restrict transmission by forming fewer interactions and by isolating subgroups that interacted with the primary case. These findings suggest that accounting for the traits of single exposed hosts has less power in predicting transmission dynamics compared to the larger scale factors of the social groups in which they reside. Factors like group size and phenotypic composition appear to alter social interaction patterns, which leads to differential transmission of microbes.

Indexed as

Bacterial Physiological PhenomenaBehavior, AnimalAnimalsPopulation DensitySocial BehaviorSpidersprimary casesocial networksocial spidertrait variationtransmission heterogeneity

Identifiers

PMID28692130
PMCPMC5871623
OpenAlexW2736300530

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.