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Special issue on Epidemics Dynamics & Control on Networks

New Content Item

Networks are ubiquitous in natural, technological and social systems. They offer a fertile framework for understanding and controlling the diffusion of ideas, rumors, and infectious diseases of plants, animals, and humans. Despite recent advances, many challenging scientific questions remain about the correct tools and their practical role in epidemics dynamics and effective strategies supporting public health decision making. The goal of this special issue is to offer a platform to the interdisciplinary community of scientists working on the diffusion process on networks and its plethora of applications. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical data with a special emphasis on epidemic spreading.

Survey and review papers are welcome.

The Lead guest editor
Matjaž Perc,
University of Maribor, Slovenia

The Guest editors (TBU)

Benjamin M. Althouse,
University of Washington, USA

Hocine Cherifi,
University of Burgundy, France

Joel C Miller,
La Trobe University, Australia

Chiara Poletto,
Inserm, France

Giulio Rosseti,
University of Pisa, Italy

Onur Varol,
Northeastern University, USA

  1. Hospitals constitute highly interconnected systems that bring into contact an abundance of infectious pathogens and susceptible individuals, thus making infection outbreaks both common and challenging. In rece...

    Authors: Ashleigh C. Myall, Robert L. Peach, Andrea Y. Weiße, Siddharth Mookerjee, Frances Davies, Alison Holmes and Mauricio Barahona
    Citation: Applied Network Science 2021 6:34
  2. We study network centrality measures that take into account the specific structure of networks with time-stamped edges. In particular, we explore how such measures can be used to identify nodes most relevant f...

    Authors: Patrick Hoscheit, Éric Anthony and Elisabeta Vergu
    Citation: Applied Network Science 2021 6:26
  3. Understanding the role of individual nodes is a key challenge in the study of spreading processes on networks. In this work we propose a novel metric, the reachability-heterogeneity (RH), to quantify the contr...

    Authors: Iacopo Pozzana, Christos Ellinas, Georgios Kalogridis and Konstantinos Sakellariou
    Citation: Applied Network Science 2021 6:25
  4. We present a modelling framework for the spreading of epidemics on temporal networks from which both the individual-based and pair-based models can be recovered. The proposed temporal pair-based model that is ...

    Authors: Rory Humphries, Kieran Mulchrone, Jamie Tratalos, Simon J. More and Philipp Hövel
    Citation: Applied Network Science 2021 6:23
  5. The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agen...

    Authors: Kara Layne Johnson, Jennifer L. Walsh, Yuri A. Amirkhanian, John J. Borkowski and Nicole Bohme Carnegie
    Citation: Applied Network Science 2021 6:22
  6. Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected ...

    Authors: Kate Barnes, Tiernon Riesenmy, Minh Duc Trinh, Eli Lleshi, Nóra Balogh and Roland Molontay
    Citation: Applied Network Science 2021 6:21
  7. The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previo...

    Authors: Anne-Sophie Ruget, Gianluigi Rossi, P. Theo Pepler, Gaël Beaunée, Christopher J. Banks, Jessica Enright and Rowland R. Kao
    Citation: Applied Network Science 2021 6:15
  8. Infectious disease surveillance is often case-based, focused on people diagnosed and their contacts in a predefined time window, and treated as independent across infections. Network analysis of partners and c...

    Authors: Dana K. Pasquale, Irene A. Doherty, Peter A. Leone, Ann M. Dennis, Erika Samoff, Constance S. Jones, John Barnhart and William C. Miller
    Citation: Applied Network Science 2021 6:13
  9. To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning...

    Authors: John Bollenbacher, Diogo Pacheco, Pik-Mai Hui, Yong-Yeol Ahn, Alessandro Flammini and Filippo Menczer
    Citation: Applied Network Science 2021 6:12
  10. The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlemen...

    Authors: Christopher Small and Daniel Sousa
    Citation: Applied Network Science 2021 6:10
  11. Vaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high co...

    Authors: Tomer Lev and Erez Shmueli
    Citation: Applied Network Science 2021 6:6
  12. The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mix...

    Authors: Rohan Patil, Raviraj Dave, Harsh Patel, Viraj M. Shah, Deep Chakrabarti and Udit Bhatia
    Citation: Applied Network Science 2021 6:4
  13. Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible–Infectious–Recovered (SIR) epidemic model o...

    Authors: Clara Pizzuti, Annalisa Socievole, Bastian Prasse and Piet Van Mieghem
    Citation: Applied Network Science 2020 5:91
  14. The role of misinformation diffusion during a pandemic is crucial. An aspect that requires particular attention in the analysis of misinfodemics is the rationale of the source of false information, in particul...

    Authors: Lorenzo Prandi and Giuseppe Primiero
    Citation: Applied Network Science 2020 5:82