Network size, structure, and pathogen transmission: a simulation study comparing different community detection algorithms

Sumner, Kelsey M.; McCabe, Collin M.; Nunn, Charles L.

doi:10.1163/1568539x-00003508

Cited by 9 publications

(9 citation statements)

References 41 publications

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“…In addition to the properties of individuals, variation in pairwise interactions can produce structure in the overall network. In primate social groups, for example, subgroups often emerge, within which interactions are more frequent relative to interactions between subgroups [59,60]. In social network theory, subgrouping is measured using metrics such as community modularity [61].…”

Section: (B) Social Network and Transmissionmentioning

confidence: 99%

“…In social network theory, subgrouping is measured using metrics such as community modularity [61]. High modularity can decrease transmission because fragmentation of the network and cohesion within subgroups tend to 'trap' parasites within a subgroup, leading to lower outbreak size and delayed transmission dynamics [53,57,58,60,62]. These features of network structure and individual centrality can, therefore, have important applications to conservation management strategies.…”

Section: (B) Social Network and Transmissionmentioning

confidence: 99%

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Behavioural ecology and infectious disease: implications for conservation of biodiversity

Herrera

Nunn

2019

Phil. Trans. R. Soc. B

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Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

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Section: (B) Social Network and Transmissionmentioning

confidence: 99%

Section: (B) Social Network and Transmissionmentioning

confidence: 99%

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Herrera

Nunn

2019

Phil. Trans. R. Soc. B

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“…For an overview, see Yang, Algesheimer, and Tessone (2016). Until recently, no research had investigated what would be the impact of choosing different community detection algorithms in the results (Aldecoa & Marín, 2013; Sumner, McCabe, & Nunn, 2018). Sumner et al (2018) showed possible variations between those different algorithms; therefore, we recommend to choose carefully an appropriate community detection algorithm for the question of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Until recently, no research had investigated what would be the impact of choosing different community detection algorithms in the results (Aldecoa & Marín, 2013; Sumner, McCabe, & Nunn, 2018). Sumner et al (2018) showed possible variations between those different algorithms; therefore, we recommend to choose carefully an appropriate community detection algorithm for the question of interest. Unfortunately, it is only recently that these questions have been addressed and a general guideline cannot be provided except that multiple algorithms may be used and the results may be compared.…”

Section: Introductionmentioning

confidence: 99%

Network measures in animal social network analysis: Their strengths, limits, interpretations and uses

2020

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We provide an overview of the most commonly used social network measures in animal research for static networks or time‐aggregated networks. For each of these measures, we provide clear explanations as to what they measure, we describe their respective variants, we underline the necessity to consider these variants according to the research question addressed, and we indicate considerations that have not been taken so far. We provide a guideline indicating how to use them depending on the data collection protocol, the social system studied and the research question addressed. Finally, we inform about the existent gaps and remaining challenges in the use of several variants and provide future research directions.

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“…We focus on modularity over other indices, such as nestedness or centralization, because the epidemiological consequences of modularity are better studied and intuitive: modularity reflects subdivision of a network into more distinct units, which are expected to slow disease transmission in the same way that quarantines reduce spread in human populations. To ensure that changes in modularity reflect changes in transmission [ 61 , 62 ], we also simulate disease spread on the networks, focusing on speed of transmission.…”

Section: Introductionmentioning

confidence: 99%

Effects of host extinction and vector preferences on vector-borne disease risk in phylogenetically structured host-hector communities

et al. 2021

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Anthropogenic disturbance impacts the phylogenetic composition and diversity of ecological communities. While changes in diversity are known to dramatically change species interactions and alter disease dynamics, the effects of phylogenetic changes in host and vector communities on disease have been relatively poorly studied. Using a theoretical model, we investigated how phylogeny and extinction influence network structural characteristics relevant to disease transmission in disturbed environments. We modelled a multi-host, multi-vector community as a bipartite ecological network, where nodes represent host and vector species and edges represent connections among them through vector feeding, and we simulated vector preferences and threat status on host and parasite phylogenies. We then simulated loss of hosts, including phylogenetically clustered losses, to investigate how extinction influences network structure. We compared effects of phylogeny and extinction to those of host specificity, which we predicted to strongly increase network modularity and reduce disease prevalence. The simulations revealed that extinction often increased modularity, with higher modularity as species loss increased, although not as much as increasing host specificity did. These results suggest that extinction itself, all else being equal, may reduce disease prevalence in disturbed communities. However, in real communities, systematic patterns in species loss (e.g. favoring high competence species) or changes in abundance may counteract these effects. Unexpectedly, we found that effects of phylogenetic signal in host and vector traits were relatively weak, and only important when phylogenetic signal of host and vector traits were similar, or when these traits both varied.

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Network size, structure, and pathogen transmission: a simulation study comparing different community detection algorithms

Cited by 9 publications

References 41 publications

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Network measures in animal social network analysis: Their strengths, limits, interpretations and uses

Effects of host extinction and vector preferences on vector-borne disease risk in phylogenetically structured host-hector communities

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