Social Contact Networks Research Articles

An enlightened aristocrat at the crossroads of countries and languages

This paper focuses on the personality of an important aristocrat from the Age of Enlightenment, Ignác Dominik Chorynský, as a person living and acting on the border of countries and languages, creating an extensive network of social and intellectual contacts around him. The vast personal archive of this nobleman conserved in Opava still awaits closer examination. Ignác Dominik Chorynský, a member of an old Moravian family, settled after 1750 in Velké Hoštice under Prussian domination, due to his marriage to Countess Barbora Hodic, while also being active in Opava, on Austrian territory. This paper examines the “frontier” position of Ignác Dominik from several aspects. The first is from the political aspect: the count was a subject of the King of Prussia and, at the same time, a regional governor in the Prussian and Austrian parts of Opava, still subordinate to the House of Habsburg. Moreover, his brother Jan Nepomuk fell in the Seven Years’ War against Prussia. Obviously, the position on the border also played a role in the representation of the count, as evidenced by the “Banquet Hall of Three Lands”, which he had built in Velké Hoštice. In the following portion, this paper presents Ignác Dominik’s wide network of contacts: amongst the local nobility in Silesia, amongst actors on the Prussian side (Frederick II, the nobles and intellectuals of Wrocław), and in Moravia (his brothers Matyáš and František Jan, Count Mittrowsky, etc.). This paper then delves into the theme of artistic transfers, as Ignác Dominik contributed to the cultural development of his region with his activities (building the castle in Velké Hoštice, rebuilding the so-called “Blücher Palace” in Opava, collecting works of art, etc.). Finally, this paper focuses on the possible role of Silesian and Prussian milieu on Ignác Dominik’s cultural activities. This paper also demonstrates the unique position of the Opava and Hlučín regions after the Wars of Austrian Succession and the imminent role of Ignác Dominik and his close friends in the cultural development of the region.

Vaccination homophily in ego contact networks during the COVID-19 pandemic

Vaccine hesitancy is an inevitable risk for societies as it contributes to outbreaks of diseases. Prior research suggests that vaccination decisions of individuals tend to spread within social networks, resulting in a tendency to vaccination homophily. The clustering of individuals resistant to vaccination can substantially make the threshold necessary to achieve herd immunity harder to reach. In this study, we examined the extent of vaccination homophily among social contacts and its association with vaccine uptake during the COVID-19 pandemic in Hungary using a contact diary approach in two cross-sectional surveys. The results indicate strong clustering among both vaccinated and unvaccinated groups. The most powerful predictor of vaccine uptake was the perceived vaccination rate within the egos’ social contact network. Vaccination homophily and the role of the interpersonal contact network in vaccine uptake were particularly pronounced in the networks of close relationships, including family, kinship, and strong social ties of the ego. Our findings have important implications for understanding COVID-19 spread dynamics by showing that the strong clustering of unvaccinated individuals posed a great risk in preventing the spread of the disease.

Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub

UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.

Mobile Phone-Based Confidential Social Network Referrals for HIV Testing (CONSORT): Protocol for a Randomized Controlled Trial.

Critical to efforts to end the HIV epidemic is the identification of persons living with HIV who have yet to be diagnosed and engaged in care. Expanded HIV testing outreach efforts need to be both efficient and ambitious, targeting the social networks of persons living with HIV and those at above-average risk of undiagnosed HIV infection. The ubiquity of mobile phones across many high HIV prevalence settings has created opportunities to leverage mobile health (mHealth) technologies to engage social networks for HIV testing outreach, prevention, and treatment. The purpose of this study is to evaluate the acceptability and efficacy of a novel mHealth intervention, "Confidential Social Network Referrals for HIV Testing (CONSORT)," to nudge at-risk individuals to test for HIV using SMS text messages. We will conduct the CONSORT study in Moshi, Tanzania, the commercial center and administrative capital of the Kilimanjaro Region in northern Tanzania. After qualitative formative work and pilot testing, we will enroll 400 clients presenting for HIV counseling and testing and 200 persons living with HIV and receiving care at HIV care and treatment centers as "inviters" into a randomized controlled trial. Eligible participants will be aged 18 years or older and live, work, or regularly receive care in Moshi. We will randomize inviters into 1 of 2 study arms. All inviters will be asked to complete a survey of their HIV testing and risk behaviors and to think of social network contacts who would benefit from HIV testing. They will then be asked to whom they would prefer to extend an HIV testing invitation in the form of a physical invitation card. Arm 1 participants will also be given the opportunity to extend CONSORT invitations in the form of automated confidential SMS text messages to any of their social network contacts or "invitees." Arm 2 participants will be offered physical invitation cards alone. The primary outcome will be counselor-documented uptake of HIV testing by invitees within 30 days of inviter enrollment. Secondary outcomes will include the acceptability of CONSORT among inviters, the number of new HIV diagnoses, and the HIV risk of invitees who present for testing. Enrollment in the randomized controlled trial is expected to start in September 2024. The findings will be disseminated to stakeholders and published in peer-reviewed journals. If CONSORT is acceptable and effective for increasing the uptake of HIV testing, given the minimal costs of SMS text reminders and the potential for exponential but targeted growth using chain referrals, it may shift current practices for HIV testing programs in the area. ClincalTrials.gov NCT05967208; https://clinicaltrials.gov/study/NCT05967208. PRR1-10.2196/55068.

Social Network Contact Frequency and Life Satisfaction of the Elderly: Focusing on the Moderating Effect of Digital Capabilities

The aims of this study were to identify (a) the social network contact frequency of the elderly with children, relatives, and friends; (b) the impact of contact frequency (face-to-face/non-face-to-face) on life satisfaction of the elderly; and (c) the moderating effect of digital capabilities of the elderly on the relationship between social contact frequency and life satisfaction. Data were obtained from the National Survey of Older Koreans 2020. The sample comprised 6,119 adults aged 65+ who were in single or couple households. The principal findings were as follows. First, couple households, higher levels of education, and better health status increased life satisfaction of the elderly. Second, the higher the frequency of face-to-face contact with children and the higher the frequency of non-face-to-face contact with friends, the more positive the effect on life satisfaction of the elderly. Third, the interaction effect of the digital capabilities of the elderly differed according to children, relatives and friends. There was a significant and positive moderating effect on the relationship between life satisfaction and the frequency of face-to-face/non-face-to-face contact with children and the frequency of face-to-face contact with relatives. Conversely, there was a significant negative effect on the relationship between life satisfaction and the frequency of face-to-face/non-face-to-face contact with friends. By examining the impact of non-face-toface contact on life satisfaction of the elderly in the era of digital transformation, the findings have significance in that they provide basic data to support policies and education programs aimed at improving the digital capabilities of the elderly.

Mid-life social participation in people with intellectual disability: The 1958 British birth cohort study.

Low social participation is a potentially modifiable risk factor for cognitive deterioration in the general population and related to lower quality of life (QoL). We aimed to find out whether social participation is linked to cognitive deterioration and QoL for people with borderline intellectual functioning and mild intellectual disability. We used data from the National Child Development Study, consisting of people born during one week in 1958, to compare midlife social participation in people with mild intellectual disability, borderline intellectual functioning, and without intellectual impairment. We defined social participation as 1. confiding/emotional support from the closest person and social network contact frequency at age 44, and 2. confiding relationships with anyone at age 50. We then assessed the extent to which social participation mediated the association between childhood intellectual functioning and cognition and QoL at age 50. 14,094 participants completed cognitive tests at age 11. People with borderline intellectual functioning and mild intellectual disability had more social contact with relatives and confiding/emotional support from their closest person, but fewer social contacts with friends and confiding relationships with anyone than those without intellectual disability. Having a confiding relationship partially mediated the association at age 50 between IQ and cognition (6.4%) and QoL (27.4%) for people with borderline intellectual functioning. We found adults with intellectual disability have positive family relationships but fewer other relationships. Even at the age of 50, confiding relationships may protect cognition for people with borderline intellectual functioning and are important for QoL.

Dynamic contact networks of residents of an urban jail in the era of SARS-CoV-2

BackgroundIn custodial settings such as jails and prisons, infectious disease transmission is heightened by factors such as overcrowding and limited healthcare access. Specific features of social contact networks within these settings have not been sufficiently characterized, especially in the context of a large-scale respiratory infectious disease outbreak. The study aims to quantify contact network dynamics within the Fulton County Jail in Atlanta, Georgia. MethodsJail roster data were utilized to construct social contact networks. Rosters included resident details, cell locations, and demographic information. This analysis involved 6702 male residents over 140,901 person days. Network statistics, including degree, mixing, and dissolution (movement within and out of the jail) rates, were assessed. We compared outcomes for two distinct periods (January 2022 and April 2022) to understand potential responses in network structures during and after the SARS-CoV-2 Omicron variant peak. ResultsWe found high cross-sectional network degree at both cell and block levels. While mean degree increased with age, older residents exhibited lower degree during the Omicron peak. Block-level networks demonstrated higher mean degrees than cell-level networks. Cumulative degree distributions increased from January to April, indicating heightened contacts after the outbreak. Assortative age mixing was strong, especially for younger residents. Dynamic network statistics illustrated increased degrees over time, emphasizing the potential for disease spread. ConclusionsDespite some reduction in network characteristics during the Omicron peak, the contact networks within the Fulton County Jail presented ideal conditions for infectious disease transmission. Age-specific mixing patterns suggested unintentional age segregation, potentially limiting disease spread to older residents. This study underscores the necessity for ongoing monitoring of contact networks in carceral settings and provides valuable insights for epidemic modeling and intervention strategies, including quarantine, depopulation, and vaccination, laying a foundation for understanding disease dynamics in such environments.Top of Form

Exploring the differential impacts of social isolation, loneliness, and their combination on the memory of an aging population: A 6-year longitudinal study of the CLSA

Memory plays a crucial role in cognitive health. Social isolation (SI) and loneliness (LON) are recognized risk factors for global cognition, although their combined effects on memory have been understudied in the literature. This study used three waves of data over six years from the Canadian Longitudinal Study on Aging to examine whether SI and LON are individually and jointly associated with memory in community-dwelling middle-aged and older adults (n = 14,208). LON was assessed with the question: "In the last week, how often did you feel lonely?". SI was measured using an index based on marital/cohabiting status, retirement status, social activity participation, and social network contacts. Memory was evaluated with combined z-scores from two administrations of the Rey Auditory Verbal Learning Test (immediate-recall, delayed-recall). We conducted our analyses using all available data across the three timepoints and retained participants with missing covariate data. Linear mixed models were used to regress combined memory scores onto SI and LON, adjusting for sociodemographic, health, functional ability, and lifestyle variables. Experiencing both SI and LON had the greatest inverse effect on memory (least-squares mean: -0.80 [95 % confidence-interval: -1.22, -0.39]), followed by LON alone (-0.73 [-1.13, -0.34]), then SI alone (-0.69 [-1.09, -0.29]), and lastly by being neither lonely nor isolated (-0.65 [-1.05, -0.25]). Sensitivity analyses confirmed this hierarchy of effects. Policies developed to enhance memory in middle-aged and older adults might achieve greater benefits when targeting the alleviation of both SI and LON rather than one or the other individually.

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity.As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores.Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2 K to 29.9 K (a 6% improvement) nationwide.

Social learning and the acquisition of information and knowledge—a network approach for the case of technology adoption

Abstract The complexity of agricultural innovations and heterogeneity of circumstances of technology application, outcomes, and social network structures have often led to obstacles in social learning and suboptimal adoption. In this article, we examine technology diffusion by focusing on the roles of peer adoption decision, peer experience, and network structures, using survey data from 500 farm households in Northern Ghana to generate social network contacts. Our results reveal that learning from peer experiences matter more in the diffusion process than just peer adoption decisions alone. We also find that social learning and the likelihood of adoption are higher when peers are central nodes, and particularly when they belong to cohesive subgroups, but lower when they are in highly segregated networks. Our results shed new light on the role of central agents, since highly cohesive neighbourhoods seem to promote diffusion more in high-modularity networks than central nodes.

A comparative cost assessment of coalescing epidemic control strategies in heterogeneous social-contact networks

The outcome of an epidemic is contingent upon the mitigating control strategies deployed by policymakers. The deployment of control strategies is constrained by the cost of economic, social, and technological factors, which often depend on regional conditions. Our model facilitates a comparative assessment of costs between two interconnected subpopulations within a heterogeneous social-contact network, which may deploy either aligned or divergent control strategies, as is frequently the case for neighboring countries. Depending on their respective asymmetric resources, each subpopulation can deploy a contact confinement strategy, a vaccination strategy, or a hybrid of both. The multi-objective goal of minimizing both the epidemic burden and the economic cost from policy implementation and productivity losses is compounded using a scaled scalarization method. Due to the inter-dependence of policy outcomes, the optimal strategy is derived from the Nash equilibrium in a 2-player compounded pay-off matrix of the total cost. Our network-based model implements a probabilistic Susceptible-Exposed-Infectious-Recovered-Dead dynamics. It also includes the effects of ego-network support and waning immunity modulated by re-exposure to viral contaminants (‘SEIRSD’). Robust outcomes of repeated network simulations with COVID-19 parameters are examined. Our method aims to equip policymakers in a negotiation process with a comparative cost matrix for divergent mitigation policies and to identify the optimal Nash-based strategy profile.

Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle

Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared “fenceline” area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. “Fenceline” contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.

A data-driven kinetic model for opinion dynamics with social network contacts – ADDENDUM

A data-driven kinetic model for opinion dynamics with social network contacts – ADDENDUM

Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition. We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency. We subsequently tested large-scale brain network FC as a potential mediator of the beneficial relationship between social engagement and cognitive performance. 112 older adults (70.7±7.3 years, range 54.6-89.7; 84 women) completed the Lubben Social Network Scale 6 (LSNS-6), National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3 (UDS-3) cognitive battery, and resting state fMRI. We completed seed-based correlational analysis in the default mode and salience networks. Significant associations between social engagement scores and cognitive performance, as well as between social engagement and FC of brain networks, informed the construction of mediation models. Social engagement was significantly associated with executive function and global cognition, with greater social engagement associated with better cognitive performance. Social engagement was significantly associated with salience network FC, with greater social engagement associated with higher connectivity. Salience network FC partially mediated associations between social engagement and both executive function and global cognition. Our results suggest that the salience network is a key mediator of the beneficial relationship between social engagement and cognition in older adults.

A data-driven kinetic model for opinion dynamics with social network contacts

Abstract Opinion dynamics is an important and very active area of research that delves into the complex processes through which individuals form and modify their opinions within a social context. The ability to comprehend and unravel the mechanisms that drive opinion formation is of great significance for predicting a wide range of social phenomena such as political polarisation, the diffusion of misinformation, the formation of public consensus and the emergence of collective behaviours. In this paper, we aim to contribute to that field by introducing a novel mathematical model that specifically accounts for the influence of social media networks on opinion dynamics. With the rise of platforms such as Twitter, Facebook, and Instagram and many others, social networks have become significant arenas where opinions are shared, discussed and potentially altered. To this aim after an analytical construction of our new model and through incorporation of real-life data from Twitter, we calibrate the model parameters to accurately reflect the dynamics that unfold in social media, showing in particular the role played by the so-called influencers in driving individual opinions towards predetermined directions.

Social contacts, neighborhoods and individual unemployment risk

PurposeThe aim of this paper is to empirically investigate whether social contacts can mediate the way in which current unemployment impacts future unemployment.Design/methodology/approachWe use 2006–2017 data from the Household Income and Labour Dynamics in Australia (HILDA) survey and a dynamic random-effects model to describe the evolution of individual unemployment status over time.FindingsOnce controlled for the local context where individuals live and create friendships, we find that above-average social contacts reduce unemployment persistence. However, social contacts seem to be slightly less effective in deprived neighborhoods. These findings are consistent with the idea that individuals obtain information about job opportunities through a network of social contacts, and unemployment may lead to a decay of social capital, making it more difficult to find employment in future periods. Our results also show that neighborhood deprivation increases individual unemployment risk, while above-average neighborhood cohesion reduces the probability of unemployment in deprived neighborhoods.Originality/valueAlthough many studies have been published on unemployment persistence, to the best of the author's knowledge, this is the first study quantifying the impact of social contacts on unemployment persistence. The study also offers fresh empirical evidence on the impact of neighborhood characteristics on unemployment risk.

Optimized Case Finding of tuberculosis among key populations in Ukraine. A follow up study.

In 2021, there were 4 million tuberculosis (TB) cases that were not detected by health systems, globally. Many of those cases are among hard-to-reach populations or key population groups. An Optimized Case Finding (OCF) strategy was introduced in Ukraine to enhance case detection and identify those "missing" cases. OCF included screening of up to eight referred household and social network contacts of an index TB case. Following the OCF project implementation, TB detection and characteristics of index cases and contacts were assessed. A cohort study using project data (July 2018 - April 2022) was conducted. In total 7,976 close contacts were engaged in the project from 1,028 index TB cases. Among the contacts, 507 were diagnosed with TB. The TB case detection was 6,356/100,000 and the number needed to investigate was 16. Multiple factors were identified as associated with TB detection including smoking, HIV, poverty, etc. About 90% of cases were identified at the initial screening of the contacts. OCF was proven to be 5.8 times more effective than the standard active case finding using household surveys and 106 times more effective than passive case finding in the general public. Our study demonstrated the effectiveness of OCF in detecting cases among key population groups and their social networks. We encourage adaptation and use of OCF by civil society organizations that already work with key vulnerable populations around the globe.

Mikrohistoria Bronisława Zielińskiego w polu translatorsko-literackim

Translators and interpreters have always played a key role as intercultural mediators, however, their biographies and functions as active agents of cultural change have only recently started to be studied. Following the sociological and material turn in translation studies, there has been a growing interest in the figure of the literary translator, not as a category of the translation process, but as a living human being who not „only” produces texts, but influences cultural transfer, introduces new poetics and shapes literary canons. Nowadays, researchers point to the necessity of describing the place and role of translators not only within Translation Studies, but above all, in the network of social contacts and relationships. The sociology of translators includes such issues as the status of translators in different cultures, pay rates, place and conditions of work, the public discourse of translation, i.e. the manifestations of the public image of the translating profession, which is visible, for example, in the press or in literary works. This area also encompasses research on translators’ attitudes towards their work, as revealed in essays, interviews, prefaces and translators’ archives. The paper focuses on the biography of the translator, placed in a broad historical and social context and thus, in the cultural and sociological dimensions of translator studies. In examining the biography of the Polish translator Bronisław Zieliński, the methodology of microhistory (adapted for translation studies by Jeremy Munday) will be used and combined with Anthony Chesterman’s theoretical proposal of translator studies.

Structural network characteristics affect epidemic severity and prediction in social contact networks

Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number (R0) have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators (R0 and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.

A simplicial epidemic model for COVID-19 spread analysis.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.