Phase transitions and social distancing control measures for SARS-CoV-2 on small world networks

We investigate the efficacy of three social distancing controls on the spread of SARS-CoV-2 using an agent based SIR model on a small world network structure: 1) Global social distancing with a fixed probability of adherence. 2) Individually initiated social isolation when a threshold number of contacts are infected. 3) Use of personal protective equipment (PPE) to reduce viral shedding and resultant infectivity. The primary driver of total number of infections is the viral shedding rate, with probability of social distancing being the next critical factor. These results suggest that higher compliance with PPE usage and personal hygiene has the potential to decrease the number of infections and shorten epidemic duration. Individually initiated social isolation was effective when initiated in response to a single infected contact. The combination of social isolation and PPE resulted in very low levels of infection. Our model suggests that widespread application of social distancing through government control can drastically reduce viral spread; even in the absence of widely adopted social distancing protocols, high use of PPE can also dramatically reduce the viral spread while short-duration quarantine following exposure to an infected individual was less effective.

[1]  S. Omer,et al.  Perceptions of the adult US population regarding the novel coronavirus outbreak , 2020, medRxiv.

[2]  D. C. Nath,et al.  Time-to-Death approach in revealing Chronicity and Severity of COVID-19 across the World , 2020, PloS one.

[3]  Daniela Perrotta,et al.  Towards a data-driven characterization of behavioral changes induced by the seasonal flu , 2020, PLoS Comput. Biol..

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  Geo temporal distribution of 1,688 Chinese healthcare workers infected with COVID-19 in severe conditions, a secondary data analysis , 2020, medRxiv.

[6]  Benjamin Cornwell,et al.  The Small-World Network of College Classes: Implications for Epidemic Spread on a University Campus , 2020 .

[7]  P. Beutels,et al.  Behavioural change models for infectious disease transmission: a systematic review (2010–2015) , 2016, Journal of The Royal Society Interface.

[8]  P. Klepac,et al.  Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts , 2020, The Lancet Global Health.

[9]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Lun Zhang,et al.  The Watts–Strogatz network model developed by including degree distribution: theory and computer simulation , 2007 .

[11]  F. Questier,et al.  Face Masks Against COVID-19: An Evidence Review , 2020 .

[12]  Fotios Petropoulos,et al.  Forecasting the novel coronavirus COVID-19 , 2020, PloS one.

[13]  Jian Wang,et al.  Clinical findings of patients with coronavirus disease 2019 in Jiangsu province, China: A retrospective, multi-center study , 2020, PLoS neglected tropical diseases.

[14]  David G. Rand,et al.  Using social and behavioural science to support COVID-19 pandemic response , 2020, Nature Human Behaviour.

[15]  E. Kostelich,et al.  To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic , 2020, Infectious Disease Modelling.

[16]  G. Gaeta A simple SIR model with a large set of asymptomatic infectives , 2020, Mathematics in Engineering.

[17]  Hui-yao Wang,et al.  The psychological distress and coping styles in the early stages of the 2019 coronavirus disease (COVID-19) epidemic in the general mainland Chinese population: A web-based survey , 2020, medRxiv.

[18]  Joshua M. Epstein,et al.  Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations , 2007, PloS one.

[19]  C. Anastassopoulou,et al.  Data-based analysis, modelling and forecasting of the COVID-19 outbreak , 2020, medRxiv.

[20]  C. Packer,et al.  Disease transmission in territorial populations: the small-world network of Serengeti lions , 2011, Journal of The Royal Society Interface.

[21]  James M Robins,et al.  Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing. , 2007, Journal of theoretical biology.

[22]  F. Piazza,et al.  Analysis and forecast of COVID-19 spreading in China, Italy and France , 2020, Chaos, Solitons & Fractals.

[23]  M. Bellis,et al.  Behind the mask. Journey through an epidemic: some observations of contrasting public health responses to SARS , 2003, Journal of epidemiology and community health.

[24]  N. Lo,et al.  Scientific and ethical basis for social-distancing interventions against COVID-19 , 2020, The Lancet Infectious Diseases.

[25]  Gurjit S. Randhawa,et al.  Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study , 2020, bioRxiv.

[26]  Gregory L. Watson,et al.  Face Masks Against COVID-19: An Evidence Review , 2020 .

[27]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[29]  Marcel Salathé,et al.  Complex social contagion makes networks more vulnerable to disease outbreaks , 2012, Scientific Reports.