Evidence map›Paper›PMID 29403006›Full record

ArticleScientific reports2018

Epidemic spreading in modular time-varying networks.

Matthieu Nadini, Kaiyuan Sun, Enrico Ubaldi, Michele Starnini, Alessandro Rizzo, Nicola Perra

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

  1. Article
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  4. Network indicators of cultural resilience to anthropogenic removals in animal societies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
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  9. An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens.Network modeling and analysis in health informatics and bioinformatics · 2022
    Article
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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

6 authors.

Matthieu NadiniDepartment of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY, 11201, USA.
Kaiyuan SunLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, 02115, USA.
Enrico UbaldiInstitute for Scientific Interchange, ISI Foundation, Turin, Italy.ORCID http://orcid.org/0000-0003-1685-9939
Michele StarniniDepartament de Física Fondamental, Universitat de Barcelona, Martí i Franquès 1, 08028, Barcelona, Spain.
Alessandro RizzoDipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy.ORCID http://orcid.org/0000-0002-2386-3146
Nicola PerraCentre for Business Networks Analysis, University of Greenwich, London, UK. n.perra@greenwich.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibits SIR processes, it speeds up SIS phenomena. In this case, we observe that modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal network.

Indexed as

Disease SusceptibilityEpidemicsModels, BiologicalComputer SimulationHumansNumerical Analysis, Computer-Assisted

Identifiers

PMID29403006
PMCPMC5799280

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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.