The role of age in the spreading of COVID-19 across a social network in Bucharest

Abstract We analyse officially procured data detailing the COVID-19 transmission in Romania’s capital Bucharest between 1st August and 31st October 2020. We apply relational hyperevent models on 19,713 individuals with 13,377 infection ties to determine to what degree the disease spread is affected by age whilst controlling for other covariate and human-to-human transmission network effects. We find that positive cases are more likely to nominate alters of similar age as their sources of infection, thus providing evidence for age homophily. We also show that the relative infection risk is negatively associated with the age of peers, such that the risk of infection increases as the average age of contacts decreases. Additionally, we find that adults between the ages 35 and 44 are pivotal in the transmission of the disease to other age groups. Our results may contribute to better controlling future COVID-19 waves, and they also point to the key age groups which may be essential for vaccination given their prominent role in the transmission of the virus.

[1]  M. Perc,et al.  An action plan for pan-European defence against new SARS-CoV-2 variants , 2021, The Lancet.

[2]  Matt J Keeling,et al.  Contact tracing and disease control , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[3]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[4]  Frank Dignum,et al.  Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation , 2020, Minds and Machines.

[5]  K. Nagarajan,et al.  Social network analysis methods for exploring SARS-CoV-2 contact tracing data , 2020, BMC Medical Research Methodology.

[6]  Yong-Yeol Ahn,et al.  The effectiveness of backward contact tracing in networks , 2020, Nature Physics.

[7]  M. Perc,et al.  The impact of human mobility networks on the global spread of COVID-19 , 2020, J. Complex Networks.

[8]  M. Perc,et al.  Assortativity provides a narrow margin for enhanced cooperation on multilayer networks , 2019, New Journal of Physics.

[9]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  J. Lewnard,et al.  Epidemiology and transmission dynamics of COVID-19 in two Indian states , 2020, Science.

[11]  N. Askitas,et al.  Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study , 2021, Scientific Reports.

[12]  Mark Tranmer,et al.  REM beyond dyads: relational hyperevent models for multi-actor interaction networks , 2019, ArXiv.

[13]  Ernesto Estrada COVID-19 and SARS-CoV-2. Modeling the present, looking at the future , 2020, Physics Reports.

[14]  N. G. Davies,et al.  Age-dependent effects in the transmission and control of COVID-19 epidemics , 2020, Nature Network Boston.

[15]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[16]  M. McPherson,et al.  Network Effects in Blau Space: Imputing Social Context from Survey Data , 2019, Socius : sociological research for a dynamic world.

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

[18]  M. Perc,et al.  Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks , 2020, Royal Society Open Science.

[19]  S. Feld The Focused Organization of Social Ties , 1981, American Journal of Sociology.

[20]  Y. Liu,et al.  What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization , 2020, EClinicalMedicine.

[21]  Dawei Zhao,et al.  Statistical physics of vaccination , 2016, ArXiv.

[22]  M. Lipsitch,et al.  On the Effect of Age on the Transmission of SARS-CoV-2 in Households, Schools, and the Community , 2020, The Journal of infectious diseases.

[23]  Young Joon Park,et al.  Contact Tracing during Coronavirus Disease Outbreak, South Korea, 2020 , 2020, Emerging infectious diseases.

[24]  Alessandro Lomi,et al.  Dynamic network analysis of contact diaries , 2021, Soc. Networks.

[25]  M. You,et al.  A social network analysis of the spread of COVID-19 in South Korea and policy implications , 2021, Scientific Reports.

[26]  A. Barrat,et al.  Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection , 2020, Science Advances.

[27]  Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection , 2021, Science Advances.

[28]  Mario Ventresca,et al.  Evaluation of strategies to mitigate contagion spread using social network characteristics , 2013, Soc. Networks.

[29]  J. M. McPherson,et al.  Evolution on a Dancing Landscape: Organizations and Networks in Dynamic Blau Space , 1991 .

[30]  Christopher C. Pain,et al.  Optimal vaccination strategies for COVID-19 based on dynamical social networks with real-time updating , 2021, medRxiv.

[31]  D. Watts Networks, Dynamics, and the Small‐World Phenomenon1 , 1999, American Journal of Sociology.

[32]  Mario Gaviria,et al.  A network analysis of COVID-19 mRNA vaccine patents , 2021, Nature Biotechnology.

[33]  Alessandro Lomi,et al.  Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events , 2019, Network Science.

[34]  Age groups that sustain resurging COVID-19 epidemics in the United States , 2021, Science.

[35]  George G. Vega Yon,et al.  Diffusion/Contagion Processes on Social Networks , 2020, Health education & behavior : the official publication of the Society for Public Health Education.

[36]  Yongsheng Wu,et al.  Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study , 2020, The Lancet Infectious Diseases.

[37]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[38]  Marian-Gabriel Hancean,et al.  Micro-level network dynamics of scientific collaboration and impact: relational hyperevent models for the analysis of coauthor networks , 2021 .

[39]  Rui Cong,et al.  Pool-rewarding in N-person snowdrift game , 2021 .

[40]  Attila Szolnoki,et al.  Evolutionary dynamics of cooperation in a population with probabilistic corrupt enforcers and violators , 2019, Mathematical Models and Methods in Applied Sciences.

[41]  Emil N. Iftekhar,et al.  Calling for pan-European commitment for rapid and sustained reduction in SARS-CoV-2 infections , 2020, The Lancet.

[42]  M. Perc,et al.  Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment , 2021, Results in Physics.

[43]  A. Gundlapalli,et al.  Changing Age Distribution of the COVID-19 Pandemic — United States, May–August 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[44]  Dafne Muntanyola-Saura,et al.  Heterophily in social groups formation: a social network analysis , 2018, Quality & Quantity.

[45]  Douglas D. Heckathorn,et al.  Respondent-driven sampling : A new approach to the study of hidden populations , 1997 .

[46]  A. Vespignani,et al.  Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China , 2020, Science.

[47]  An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places , 2021, International journal of environmental research and public health.

[48]  Adam M. Kleinbaum,et al.  Social Networks and Cognition , 2020, Annual Review of Sociology.

[49]  Isabel J. Raabe,et al.  Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world , 2020, Nature Human Behaviour.

[50]  S. Saraswathi,et al.  Social network analysis of COVID-19 transmission in Karnataka, India , 2020, Epidemiology and Infection.

[51]  Rui Cong,et al.  Sentiment contagion dilutes prisoner's dilemmas on social networks , 2020, EPL (Europhysics Letters).

[52]  A. Siddique,et al.  Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan , 2021, Biology.

[53]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[54]  Sara B. Soderstrom,et al.  Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms , 2010 .