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Learn about our response to COVID-19, including freely available research and expanded remote access support.

Coronavirus (COVID-19) Collection

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In support of global efforts to address the COVID-19 pandemic, the American Physical Society (APS) has committed to making potentially relevant, peer-reviewed articles from our Physical Review journals more discoverable, accessible, and usable.

We have identified a collection of articles potentially relevant to researchers, health professionals, and others working on the COVID-19 pandemic, and are making this collection free to read for the duration of the crisis. The scope of the collection includes any articles that mention coronavirus, as well as those classified as relevant to epidemiology and epidemic spreading models. We will continue to add to the collection as additional potentially relevant articles are identified, and as new articles with potential relevance are published.

Read the up-to-date informational page for more details about the Physical Review journals' response to the COVID-19 pandemic.

For broader information regarding all APS activities, check the Society’s regularly.

Letter
Oliver McRae, Kenneth R. Mead, and James C. Bird
Phys. Rev. Fluids 6, L031601 (2021) – Published 8 March 2021

The authors study how pathogens can be exposed to damaging hydrodynamic stressors during the aerosolization process.

Editors' Suggestion Rapid Communication
Fan Yang, Amir A. Pahlavan, Simon Mendez, Manouk Abkarian, and Howard A. Stone
Phys. Rev. Fluids 5, 122501(R) (2020) – Published 1 December 2020

An examination of the concentration of a pathogen exhaled while speaking in a poorly ventilated space suggests that the probability of infection is relatively high for a few minutes of contact time at a separation of 1 meter separation distance and double that time at a separation of 2 meters.

Featured in Physics Editors' Suggestion Rapid Communication 5 citations
M. Abkarian and H. A. Stone
Phys. Rev. Fluids 5, 102301(R) (2020) – Published 2 October 2020

High-speed visualization identifies the formation mechanism of microscopic saliva droplets during the phonation of plosive consonants: as moist lips open, there is a sequence of film formation and rupture into vertically attached filaments, which subsequently extend over centimeter-scales and destabilize into droplets due to the fast airflow of speech. The formation process ties this aerosolization mechanism to drop formation in wind instruments and to meter-long, speech-driven transport important to asymptomatic transmission of airborne pathogens.

Editors' Suggestion 4 citations
Amit R. Singh, Andrej Košmrlj, and Robijn Bruinsma
Phys. Rev. Lett. 124, 158101 (2020) – Published 13 April 2020

Numerical simulations reveal details of thermal melting, buckling and collapse transitions of viral capsids.

13 citations
Guilherme Ferraz de Arruda, Giovanni Petri, and Yamir Moreno
Phys. Rev. Research 2, 023032 (2020) – Published 10 April 2020

The authors show that social contagion dynamics display a complex phase space, characterized by first and second order transitions, bistability, and hysteresis. The authors also extend the concept of latent heat to social contexts, which could provide insights into oscillatory social behaviors

2 citations
Yuyuan Luo and Laura P. Schaposnik
Phys. Rev. Research 2, 023001 (2020) – Published 1 April 2020

The paper studies the interaction between dynamical systems and percolation models, with views towards the study of diseases that have time-dependent infection rates. The work introduces F(t)-bootstrap percolation where a vertex is infected if the number of its neighbors which are infected at time t is the value of a certain percolation function, F(t). This model serves to describe scenarios such as the propagation of diseases that become resilient to treatments over time and assess vaccination programs

5 citations
Claudio Castellano and Romualdo Pastor-Satorras
Phys. Rev. X 10, 011070 (2020) – Published 24 March 2020

During the spread of an epidemic, highly connected individuals can maintain infection throughout the population by reinfecting each other even when not in direct contact.

Featured in Physics 11 citations
Sam Moore and Tim Rogers
Phys. Rev. Lett. 124, 068301 (2020) – Published 12 February 2020

A new analysis predicts the speed at which an infectious disease spreads to specific individuals in a network.

6 citations
Guilherme Ferraz de Arruda, Giovanni Petri, Francisco A. Rodrigues, and Yamir Moreno
Phys. Rev. Research 2, 013046 (2020) – Published 14 January 2020

The authors study a general epidemic model with arbitrary recovery rate distribution and show that heterogeneity in the dynamical parameters can be as significant as the more studied structural heterogeneity. Specifically, the paper uncovers that the critical point tends to be smaller than typically expected, which can be linked to the variance of the recovery rates.

4 citations
Jie Chen, Mao-Bin Hu, and Ming Li
Phys. Rev. E 101, 012301 (2020) – Published 9 January 2020
3 citations
Eun Lee, Scott Emmons, Ryan Gibson, James Moody, and Peter J. Mucha
Phys. Rev. E 100, 062305 (2019) – Published 10 December 2019
Byeongjin Choe, Yishi Lin, Sungsu Lim, John C. S. Lui, and Kyomin Jung
Phys. Rev. E 100, 052311 (2019) – Published 25 November 2019
1 citation
Marcelo M. de Oliveira, Sidiney G. Alves, and Silvio C. Ferreira
Phys. Rev. E 100, 052302 (2019) – Published 4 November 2019
2 citations
Francesco Vincenzo Surano, Christian Bongiorno, Lorenzo Zino, Maurizio Porfiri, and Alessandro Rizzo
Phys. Rev. E 100, 042306 (2019) – Published 15 October 2019
8 citations
Diogo H. Silva, Silvio C. Ferreira, Wesley Cota, Romualdo Pastor-Satorras, and Claudio Castellano
Phys. Rev. Research 1, 033024 (2019) – Published 15 October 2019

The authors show how the accuracy of mean-field estimates of the epidemic threshold in real and synthetic complex networks are related to their spectral properties. The results allow to gauge the predictive effectiveness of the different theories, enabling the selection of the minimal representative approach in order to obtain the desired accuracy in predictions for real-world topologies.

6 citations
Paulo Cesar Ventura da Silva, Fátima Velásquez-Rojas, Colm Connaughton, Federico Vazquez, Yamir Moreno, and Francisco A. Rodrigues
Phys. Rev. E 100, 032313 (2019) – Published 24 September 2019
17 citations
Tiago P. Peixoto
Phys. Rev. Lett. 123, 128301 (2019) – Published 18 September 2019
2 citations
P. Van Mieghem and Qiang Liu
Phys. Rev. E 100, 022317 (2019) – Published 26 August 2019
8 citations
Ivan Bonamassa, Bnaya Gross, Michael M. Danziger, and Shlomo Havlin
Phys. Rev. Lett. 123, 088301 (2019) – Published 22 August 2019
3 citations
Jason Hindes and Michael Assaf
Phys. Rev. Lett. 123, 068301 (2019) – Published 9 August 2019
5 citations
Akari Matsuki and Gouhei Tanaka
Phys. Rev. E 100, 022302 (2019) – Published 5 August 2019
6 citations
Andreas Koher, Hartmut H. K. Lentz, James P. Gleeson, and Philipp Hövel
Phys. Rev. X 9, 031017 (2019) – Published 2 August 2019

A new model of contagious spreading on temporal networks focuses on the interactions between individuals to derive criteria essential for risk assessment.

Rapid Communication 1 citation
Ali Faqeeh, Saeed Osat, Filippo Radicchi, and James P. Gleeson
Phys. Rev. E 100, 010401(R) (2019) – Published 2 July 2019
6 citations
Hsuan-Wei Lee, Nishant Malik, Feng Shi, and Peter J. Mucha
Phys. Rev. E 99, 062301 (2019) – Published 4 June 2019
3 citations
Mi Jin Lee and Deok-Sun Lee
Phys. Rev. E 99, 032309 (2019) – Published 29 March 2019
12 citations
Jonas S. Juul and Mason A. Porter
Phys. Rev. E 99, 022313 (2019) – Published 26 February 2019
Rapid Communication 1 citation
Yongjoo Baek, Kihong Chung, Meesoon Ha, Hawoong Jeong, and Daniel Kim
Phys. Rev. E 99, 020301(R) (2019) – Published 25 February 2019
9 citations
L. G. Alvarez-Zuzek, M. A. Di Muro, S. Havlin, and L. A. Braunstein
Phys. Rev. E 99, 012302 (2019) – Published 2 January 2019
13 citations
Ping Hu, Li Ding, and Xuming An
Phys. Rev. E 98, 062322 (2018) – Published 28 December 2018
9 citations
Michele Tizzani, Simone Lenti, Enrico Ubaldi, Alessandro Vezzani, Claudio Castellano, and Raffaella Burioni
Phys. Rev. E 98, 062315 (2018) – Published 18 December 2018
13 citations
Quan-Hui Liu, Xinyue Xiong, Qian Zhang, and Nicola Perra
Phys. Rev. E 98, 062303 (2018) – Published 3 December 2018
5 citations
Peng-Bi Cui (崔鹏碧), Wei Wang, Shi-Min Cai, Tao Zhou, and Ying-Cheng Lai
Phys. Rev. E 98, 052311 (2018) – Published 28 November 2018
7 citations
Claudio Castellano and Romualdo Pastor-Satorras
Phys. Rev. E 98, 052313 (2018) – Published 27 November 2018
Flavio Iannelli, Igor M. Sokolov, and Felix Thiel
Phys. Rev. E 98, 032313 (2018) – Published 28 September 2018
5 citations
Petter Holme
Phys. Rev. E 98, 022313 (2018) – Published 15 August 2018
4 citations
Rebekka Burkholz and Frank Schweitzer
Phys. Rev. E 98, 022306 (2018) – Published 9 August 2018
Featured in Physics 20 citations
D. Soriano-Paños, L. Lotero, A. Arenas, and J. Gómez-Gardeñes
Phys. Rev. X 8, 031039 (2018) – Published 9 August 2018

A new network model reveals that social mixing and mobility can determine the areas of a city that are critical in provoking an epidemic outbreak.

6 citations
Yang Liu, Xi Wang, and Jürgen Kurths
Phys. Rev. E 98, 012313 (2018) – Published 23 July 2018
5 citations
Wonjun Choi, Deokjae Lee, J. Kertész, and B. Kahng
Phys. Rev. E 98, 012311 (2018) – Published 19 July 2018
16 citations
Wesley Cota, Angélica S. Mata, and Silvio C. Ferreira
Phys. Rev. E 98, 012310 (2018) – Published 18 July 2018
2 citations
Qiang Liu and Piet Van Mieghem
Phys. Rev. E 97, 062309 (2018) – Published 11 June 2018
4 citations
Robert R. Wilkinson and Kieran J. Sharkey
Phys. Rev. E 97, 052403 (2018) – Published 11 May 2018
14 citations
Clara Granell and Peter J. Mucha
Phys. Rev. E 97, 052302 (2018) – Published 3 May 2018
3 citations
N. Sherborne, K. B. Blyuss, and I. Z. Kiss
Phys. Rev. E 97, 042306 (2018) – Published 9 April 2018
Rapid Communication 3 citations
Filippos Lazaridis, Bnaya Gross, Michael Maragakis, Panos Argyrakis, Ivan Bonamassa, Shlomo Havlin, and Reuven Cohen
Phys. Rev. E 97, 040301(R) (2018) – Published 4 April 2018
10 citations
Benjamin Steinegger, Alessio Cardillo, Paolo De Los Rios, Jesús Gómez-Gardeñes, and Alex Arenas
Phys. Rev. E 97, 032308 (2018) – Published 19 March 2018
3 citations
David Juher and Joan Saldaña
Phys. Rev. E 97, 032303 (2018) – Published 9 March 2018
5 citations
Giovanni Strona and Claudio Castellano
Phys. Rev. E 97, 022308 (2018) – Published 20 February 2018
33 citations
Eugenio Valdano, Michele Re Fiorentin, Chiara Poletto, and Vittoria Colizza
Phys. Rev. Lett. 120, 068302 (2018) – Published 6 February 2018
36 citations
Antoine Moinet, Romualdo Pastor-Satorras, and Alain Barrat
Phys. Rev. E 97, 012313 (2018) – Published 31 January 2018
18 citations
Petter Holme
Phys. Rev. E 96, 062305 (2017) – Published 5 December 2017
4 citations
Bryan Iotti, Alberto Antonioni, Seth Bullock, Christian Darabos, Marco Tomassini, and Mario Giacobini
Phys. Rev. E 96, 052316 (2017) – Published 30 November 2017
23 citations
Iacopo Pozzana, Kaiyuan Sun, and Nicola Perra
Phys. Rev. E 96, 042310 (2017) – Published 26 October 2017
17 citations
Tommaso Spanio, Jorge Hidalgo, and Miguel A. Muñoz
Phys. Rev. E 96, 042301 (2017) – Published 2 October 2017
1 citation
V. A. T. Nguyen and D. C. Vural
Phys. Rev. E 96, 032314 (2017) – Published 26 September 2017
13 citations
Ying Liu, Ming Tang, Younghae Do, and Pak Ming Hui
Phys. Rev. E 96, 022323 (2017) – Published 31 August 2017
6 citations
Takehisa Hasegawa and Koji Nemoto
Phys. Rev. E 96, 022311 (2017) – Published 11 August 2017
21 citations
Peng-Bi Cui (崔鹏碧), Francesca Colaiori, and Claudio Castellano
Phys. Rev. E 96, 022301 (2017) – Published 1 August 2017
8 citations
Bertrand Ottino-Löffler, Jacob G. Scott, and Steven H. Strogatz
Phys. Rev. E 96, 012313 (2017) – Published 13 July 2017
Sanjeev Kumar Chauhan
Phys. Rev. E 96, 012305 (2017) – Published 5 July 2017
1 citation
Alessandro S. Barros and Suani T. R. Pinho
Phys. Rev. E 95, 062135 (2017) – Published 29 June 2017
Editors' Suggestion 9 citations
Wonjun Choi, Deokjae Lee, and B. Kahng
Phys. Rev. E 95, 062115 (2017) – Published 12 June 2017

Using a two-step extension of a well-known epidemic model with multiple seeds, the authors observe two different spreading behaviors. Depending on the concentration of initially infected seeds, the epidemic transition can be a hybrid one showing both continuous and discontinuous behavior, or a continuous one.

14 citations
Fátima Velásquez-Rojas and Federico Vazquez
Phys. Rev. E 95, 052315 (2017) – Published 22 May 2017
1 citation
Shanshan Li
Phys. Rev. E 95, 032306 (2017) – Published 6 March 2017
35 citations
L. Böttcher, J. Nagler, and H. J. Herrmann
Phys. Rev. Lett. 118, 088301 (2017) – Published 23 February 2017
45 citations
M. Saeedian, M. Khalighi, N. Azimi-Tafreshi, G. R. Jafari, and M. Ausloos
Phys. Rev. E 95, 022409 (2017) – Published 21 February 2017
36 citations
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
Phys. Rev. X 7, 011014 (2017) – Published 2 February 2017

Multilayer networks can be used to describe many phenomena such as the flow of information and the spread of disease. A new mathematical description of these networks in the context of disease transmission reveals behaviors such as multiple transmission rates and localization of disease in a network layer.

26 citations
Filippo Radicchi and Claudio Castellano
Phys. Rev. E 95, 012318 (2017) – Published 18 January 2017
15 citations
Yong Zhuang, Alex Arenas, and Osman Yağan
Phys. Rev. E 95, 012312 (2017) – Published 17 January 2017
37 citations
Flavio Iannelli, Andreas Koher, Dirk Brockmann, Philipp Hövel, and Igor M. Sokolov
Phys. Rev. E 95, 012313 (2017) – Published 17 January 2017
18 citations
Hongrun Wu, Alex Arenas, and Sergio Gómez
Phys. Rev. E 95, 012301 (2017) – Published 3 January 2017
5 citations
Hengcong Liu, Muhua Zheng, Dayu Wu, Zhenhua Wang, Jinming Liu, and Zonghua Liu
Phys. Rev. E 94, 062318 (2016) – Published 29 December 2016
5 citations
Bin Wu, Shanjun Mao, Jiazeng Wang, and Da Zhou
Phys. Rev. E 94, 062314 (2016) – Published 23 December 2016
3 citations
César Parra-Rojas, Thomas House, and Alan J. McKane
Phys. Rev. E 94, 062408 (2016) – Published 19 December 2016
17 citations
Ernesto Estrada, Sandro Meloni, Matthew Sheerin, and Yamir Moreno
Phys. Rev. E 94, 052316 (2016) – Published 28 November 2016
44 citations
Peter G. Fennell, Sergey Melnik, and James P. Gleeson
Phys. Rev. E 94, 052125 (2016) – Published 16 November 2016
22 citations
Renan S. Sander, Guilherme S. Costa, and Silvio C. Ferreira
Phys. Rev. E 94, 042308 (2016) – Published 14 October 2016
7 citations
Joel C. Miller
Phys. Rev. E 94, 032313 (2016) – Published 19 September 2016
3 citations
Menachem Lachiany and Yoram Louzoun
Phys. Rev. E 94, 022409 (2016) – Published 11 August 2016
37 citations
Jason Hindes and Ira B. Schwartz
Phys. Rev. Lett. 117, 028302 (2016) – Published 6 July 2016
41 citations
James P. Gleeson, Kevin P. O’Sullivan, Raquel A. Baños, and Yamir Moreno
Phys. Rev. X 6, 021019 (2016) – Published 13 May 2016

The internet is filled with memes that have “gone viral.” A study of meme popularity uses an analytically tractable model that sheds light on the fundamental drivers of meme popularity in social networks.

5 citations
Basil S. Bayati
Phys. Rev. E 93, 052124 (2016) – Published 13 May 2016
5 citations
Takehisa Hasegawa and Koji Nemoto
Phys. Rev. E 93, 032324 (2016) – Published 30 March 2016
29 citations
Silvio C. Ferreira, Renan S. Sander, and Romualdo Pastor-Satorras
Phys. Rev. E 93, 032314 (2016) – Published 14 March 2016
3 citations
Ping Li, Xian Sun, Kai Zhang, Jie Zhang, and Jürgen Kurths
Phys. Rev. E 93, 032312 (2016) – Published 11 March 2016
3 citations
Babak Fotouhi and Mehrdad Khani Shirkoohi
Phys. Rev. E 93, 012301 (2016) – Published 6 January 2016

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