Social Mixing Research Articles

Vaccinating Adolescents and Children Significantly Reduces COVID-19 Morbidity and Mortality across All Ages: A Population-Based Modeling Study Using the UK as an Example.

Debate persists around the risk–benefit balance of vaccinating adolescents and children against COVID-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups. In this study, we present a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the COVID-19 pandemic across all age groups, using the UK as an example. The base case model demonstrates significant increases in COVID-19 disease burden in the UK following a relaxation of restrictions, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). Including adolescents and children in the vaccination program could reduce overall COVID-related mortality by 57%, and reduce cases of long COVID by 75%. This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections, and subsequent COVID-19 morbidity and mortality, across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy.

Measuring the Differentiated Impact of New Low-Income Housing Tax Credit (LIHTC) Projects on Households’ Movements by Income Level within Urban Areas

Social mixing is one of the key objectives of the housing policy in OECD countries. The Low-Income Housing Tax Credit (LIHTC) program, the largest affordable housing construction program in the US since 1986, has recently set creating mixed-income communities as one of the standards. As a project-based program, LIHTC developments are likely to influence residential mobility; however, little is known about its empirical effects. This study investigated whether new LIHTC projects are effective at attracting heterogeneous income groups to LIHTC neighborhoods, thereby contributing to creating mixed-income communities. Using unique individual-level household movement data combined with origin–destination neighborhood characteristics, we developed zero-inflated negative binomial (ZINB) models to analyze the LIHTC’s impact on residential mobility patterns in Franklin County, Ohio, US, from 2011 to 2015. The results suggest that the LIHTC attracts low-income households while deterring higher-income families, and therefore the program is not proved to be effective at creating mixed-income neighborhoods.

Intracluster correlation coefficients in a large cluster randomized vaccine trial in schools: Transmission and impact of shared characteristics.

Cluster randomized trials (cRCT) to assess vaccine effectiveness incorporate indirect effects of vaccination, helping to inform vaccination policy. To calculate the sample size for a cRCT, an estimate of the intracluster correlation coefficient (ICC) is required. For infectious diseases, shared characteristics and social mixing behaviours may increase susceptibility and exposure, promote transmission and be a source of clustering. We present ICCs from a school-based cRCT assessing the effectiveness of a meningococcal B vaccine (Bexsero, GlaxoSmithKline) on reducing oropharyngeal carriage of Neisseria meningitidis (Nm) in 34,489 adolescents from 237 schools in South Australia in 2017/2018. We also explore the contribution of shared behaviours and characteristics to these ICCs. The ICC for carriage of disease-causing Nm genogroups (primary outcome) pre-vaccination was 0.004 (95% CI: 0.002, 0.007) and for all Nm was 0.007 (95%CI: 0.004, 0.011). Adjustment for social behaviours and personal characteristics reduced the ICC for carriage of disease-causing and all Nm genogroups by 25% (to 0.003) and 43% (to 0.004), respectively. ICCs are also reported for risk factors here, which may be outcomes in future research. Higher ICCs were observed for susceptibility and/or exposure variables related to Nm carriage (having a cold, spending ≥1 night out socializing or kissing ≥1 person in the previous week). In metropolitan areas, nights out socializing was a highly correlated behaviour. By contrast, smoking was a highly correlated behaviour in rural areas. A practical example to inform future cRCT sample size estimates is provided.

Theory-Based Roadmap for Assessing Sustainability in the Collaborative Economy.

This study aims to investigate the current state of sustainability for the collaborative economy (CE). By utilizing the triple bottom line as a founding conceptual framework, the study summarizes and discusses the sustainability of the CE from three dimensions: environment, economy, and society. The study further proposes some targeted measures and suggestions to measure the level of sustainability of the CE and CE platforms. The result shows that the CE has partially fulfilled some of its initial promises pertaining to sustainability, such as creating new job opportunities, economic growth, the efficient use of space and physical resources, as well as social mixing. However, the current sustainability benefits remain much smaller than some claim and hope for. Therefore, governments, platforms, and the public should work together to solve current challenges pertaining to the CE to tap its sustainability potential.

Lessons learnt from the first large outbreak of COVID-19 in health-care settings in Tasmania, Australia.

ProblemOne month after the initial case of coronavirus disease 2019 (COVID-19) in Tasmania, an island state of Australia, two health-care workers (HCWs) from a single regional hospital were notified to public health authorities following positive tests for SARS-CoV-2 nucleic acid. These were the first recognized cases in an outbreak that overwhelmed the hospital’s ability to function.ContextThe outbreak originated from two index cases. Both had returned to Tasmania following travel on a cruise ship and required hospital admission for management of COVID-19. A total of 138 cases were subsequently linked to this outbreak: 81 HCWs (most being nurses) and 23 patients across three hospitals, one resident of an aged-care facility and 33 close contacts.ActionThe outbreak was controlled through the identification and isolation of cases, identification and quarantining of close contacts and their household members, closure of the affected facilities and community-level restrictions to reduce social mixing in the affected region.Lessons learntFactors that were likely to have contributed to ongoing transmission in this setting included workplace practices that prevented adequate physical distancing, attending work while symptomatic, challenges in rapidly identifying contacts, mobility of staff and patients between facilities, and challenges in the implementation of infection control practices.DiscussionMany commonly accepted hospital practices before the COVID-19 pandemic amplified the outbreak. The lessons learnt from this investigation changed work practices for HCWs and led to wider public health interventions in the management of potential primary and secondary contacts.

Association between tuberculosis in men and social network structure in Kampala, Uganda

BackgroundGlobally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases.MethodsWe compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household).ResultsThe observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children.ConclusionsThis analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

Being prepared for paediatric respiratory viral infections post-COVID-19 restrictions.

In a previous article in autumn 20201 we wrote of the potential dangers of managing the usual seasonal winter peak of paediatric viral infections during the COVID-19 pandemic. In reality, there have been negligible winter rates of respiratory syncytial virus (RSV) and influenza2 due to dramatically reduced social mixing. This is a pattern that has been repeated around the world, with low rates of respiratory viral infections reported in the US, …

Compact development and preferences for social mixing in location choices: Results from revealed preferences in Santiago, Chile

AbstractEven though densification and social mixing are declared objectives of many nowadays urban planning paradigms, their simultaneous implementation is usually questioned by different actors and is not frequent in practice. In a market economy, understanding potential demand for this class of development, from different types of households, is essential to define public policies oriented to achieve both compact development (CD) and social mixture. To understand the preferences of households and potential demand, we implement a location choice model based on a bid–rent framework and spatial latent classes (LC), using census data and location attributes. By using spatial LC, we do not impose exogenous definitions of which zones are perceived as CD or suburban, rendering a robust method to identify variation in preferences. We apply the model to Santiago de Chile, where social mixing in dense and well‐located areas is being intensely discussed. We find strong differences in households' valuation of attributes between spatial classes. Results show that social mixing is more difficult in dense, well‐connected areas than in suburban areas because higher‐income households are more sensitive to the socioeconomic context of the location in compact areas. Besides showing evidence on household preferences and their implications for social‐mixing policies, this paper also provides a proof of concept for the use of spatial LC (proposed in previous work by the authors), showing this is a robust methodology allowing to generate behavior‐based classifications for urban areas. The paper also contributes methodologically, by deriving the elasticity formulation for bid‐auction location choice models, which allows quantifying the importance of location attributes in location probability.

Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis.

Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as severe acute respiratory syndrome-coronavirus-2 during the coronavirus disease 2019 pandemic. Contact matrices are typically used when evaluating such public health interventions to account for the heterogeneity in social mixing of individuals, but the surveys used to obtain the number of contacts often lack detailed information on the time individuals spend on daily activities. The present work addresses this problem by combining the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0--15, 16--24, 25-44, 45-64, 65+ years) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). This augmentation allows exploring the impact of fewer contacts when individuals reduce the time they spend on selected daily activities as well as when lifting such restrictions again. For illustration, the derived matrices were then applied to an age-structured dynamic-transmission model of coronavirus disease 2019. Findings show how contact matrices can be successfully augmented with time-use data to inform the relative reductions in contacts by activity, which allows for more fine-grained mixing patterns and infectious disease modelling.

In the name of “social mixing”: The privatization of public housing to non-governmental organizations

ABSTRACT This paper explores the invocation of “social mixing” to camouflage the reduction of public housing through state-led gentrification processes. I focus on the use of social mixing in Israel to justify the privatization of public housing projects, transferring state assets to not-for-profit organizations. These organizations encourage middle-class families and students to relocate to disadvantaged neighborhoods. Exploring this practice reveals tensions between stakeholders regarding their perceptions of social mixing. Data for this study were drawn from interviews conducted with policymakers, NGO members, and public housing tenants, supplemented by analysis of press articles and policy and NGO documents. Findings highlight use of the social mixing discourse to contextualize benefits accrued via differential access to public housing stock. Such benefits are viewed specifically by long-term tenants as displacement and gentrification. This study further assesses the advantages and disadvantages of this policy, explicating the links between gentrification and social mixing.

Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

A general model for the demographic signatures of the transition from pandemic emergence to endemicity.

Anticipating the medium- and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens, the burden of disease depends on age and previous exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develop a realistic age-structured mathematical model that integrates demography, social mixing, and immunity to establish a plausible range for future age incidence and mortality. With respect to COVID-19, we identify a plausible transition in the age structure of risks once the disease reaches seasonal endemism across a range of immunity durations and relative severity of primary versus subsequent reinfections. We train the model using diverse real-world demographies and age-structured mixing to bound expectations for changing age incidence and disease burden. The mathematical framework is flexible and can help tailor mitigation strategies in countries worldwide with varying demographies and social mixing patterns.

“鱼龙混杂 yúlóng-hùnzá” - fish and dragons (bad and good people) mixing together: young people, urban life and alcohol, drinking, drunkenness in China

ABSTRACT Against a backdrop where since the establishment of the People’s Republic of China in 1949, there has been little political, policy, policing or popular resonances of alcohol, drinking, drunkenness as being problematic, this paper explores ambivalence toward strangers, avoidance of social mixing and sensitivity to integrational socio-spatial relations as key to understanding the lives of young people. Presenting ethnographic research from diverse public spaces and commercial venues we interrogate Chinese scholars’ discussion of 公gong /私si (public/private) relations with reference to international literature on encounter and friendship. Discussing differences, similarities, connectivities and mobilities between our study and depictions of “moral panic”, “risky behaviour”, violence and disorder and/or conviviality, sociability and celebratory “rites of passage” to adulthood in cities across the global North/South, we signpost fruitful and innovative new directions for relational comparison of alcohol, drinking, drunkenness no matter where research is undertaken.

Social fluidity mobilizes contagion in human and animal populations.

Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.

Accurate influenza forecasts using type-specific incidence data for small geographic units.

Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.

State-led gentrification in three Latin American cities

ABSTRACT Many authors agree that gentrification in Latin America depends on the intervention of the state. However, for the renovation of large urban areas that have long lacked public attention, state intervention is a pivotal driver. This comparative analysis involved fieldwork analysis and short ethnographies in Mexico, Argentina, and Brazil. Results show variations and some common threads, namely land valorization policies, schemes designed to promote the creation of economic opportunities for upper-income investors and developers, the establishment of creative or cultural industrial hubs, investment in new transport and cultural infrastructure, city rebranding, and the use of iconic architecture. Displacement policies have been implemented in Brazil, although with limited success. In Brazil and Mexico, attempts have been made to control social behaviors within particular spaces, often in a racialized manner. We arrive at a narrative that differs from that of the Global North, which considers state-led gentrification to constitute the privatization of social housing under the hegemonic imposition of discourses of “social mixing.” The concept of state and its trajectory differ considerably in each hemisphere.

June: open-source individual-based epidemiology simulation.

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Paleopathology of the Ychsma: Evidence of respiratory disease during the Late Intermediate Period (AD 1000-1476) at the Central Coastal site of Pachacamac, Peru

ObjectiveTo investigate evidence for maxillary sinusitis and pulmonary inflammation in archaeological skeletons dating to the Late Intermediate Period (AD 1000-1476) at the site of Pachacamac, Peru. MaterialsThirty-nine individuals (male, female, and unknown sex; 16+ years age-at-death) were analyzed for inflammatory periosteal reaction (IPR) on the visceral (inner) surfaces of the ribs, and 16 individuals were analyzed for evidence of maxillary sinusitis. MethodsAll individuals were macroscopically examined for bony changes in the maxillary sinuses and new bone formation on the ribs according to pre-established criteria. ResultsSome 33.3% (13/39) of individuals had IPR on the ribs and 93.8% (15/16) had bony changes in the maxillary sinuses. ConclusionsRespiratory disease was likely prevalent in people buried at Pachacamac during the Late Intermediate Period. A number of factors may have increased the risk of developing respiratory disease, including exposure to poor air quality and increased crowding and social mixing, resulting from pilgrimage to this important ritual center. SignificanceThis paper represents one of the first systematic analyses of evidence for respiratory disease in Peruvian and South American human skeletal remains, demonstrating the suitability of the region for further study. LimitationsA limited sample was available for analysis. Additionally, the site’s skeletal preservation was excellent, meaning the sample available for assessment of maxillary sinusitis was smaller, being limited to individuals with post-mortem breakage. Further ResearchThe results of this study should stimulate further much needed systematic investigation of evidence for respiratory disease in other Peruvian and South American populations.

“You become one with the place”: Social mixing, social capital, and the lived experience of urban desegregation in the Roma community

Policymakers are eager to create socially mixed neighbourhoods, even though evidence for the positive impact of social mixing is patchy. In this paper, we examine the relationship between social mixing, social capital, and the lived experience of residents affected by the desegregation of an urban Roma area. Using qualitative data from a participatory action research (PAR) process in Hungary, we explore the two-way relationship between desegregation and bonding, bridging, and linking forms of social capital. Our results suggest that desegregation has complex and contradictory impacts on lived experience, and that these relate to changes in social forms of capital. Our findings speak to the heterogeneity of the Roma segregated community, and to the multifaceted nature of social capital as a resource. We conclude that the presence of strong negative neighbourhood effects, acknowledged by marginalized residents themselves, indicates that social mixing can contribute to the enhancement of wellbeing and social mobility for many segregated Roma families. However, desegregation alone is insufficient, and further policies are required to increase socio-spatial integration and deal with the social, economic, and cultural causes of extreme poverty.

Exploring night and day socio-spatial segregation based on mobile phone data: The case of Medellin (Colombia)

Social segregation research has a long tradition in urban studies. Usually, these studies focus on the residential dimension, using official registries (e.g., census data), which show population distribution at night. Nevertheless, these studies disregard the fact that the population in cities is highly mobile, and its spatial distribution dramatically changes between night and day. The emergence of new data sources (Big Data) creates perfect conditions to consider segregation as a process, by providing the opportunity to dynamically analyse temporal changes in social segregation.This study uses mobile phone data to analyse changes in social segregation between night and day. Our case study is Medellin (Colombia), a highly socially-segregated, South American city, where social integration policies are being developed, targeting the population in the most disadvantaged neighbourhoods. We use several complementary indicators of social segregation, supplementing them with mobility indicators that help explain changes in spatial segregation between night and day.The main conclusion is that daily mobility reduces the concentration of a particular group within neighbourhoods and increases the degree of social mixing (exposure) in local settings. This greater social exposure softens local contrasts (outliers) and increases the extension of spatial clusters (positive spatial autocorrelation), so general clustering trends emerge more clearly. The study also makes clear that increased exposure during the day mainly occurs due to the mobility of the low-income population, who are the most likely to leave their neighbourhood during the day and who travel the greatest distances to the most diverse set of destinations.