Social encounter networks: collective properties and disease transmission

Beyond six feet: The collective behavior of social distancing

In a severe epidemic such as the COVID-19 pandemic, social distancing can be a vital tool to stop the spread of the disease and save lives. However, social distancing may induce profound negative social or economic impacts as well. How to optimize social distancing is a serious social, political, as well as public health issue yet to be resolved. This work investigates social distancing with a focus on how every individual reacts to an epidemic, what role he/she plays in social distancing, and how every individual's decision contributes to the action of the population and vice versa. Social distancing is thus modeled as a population game, where every individual makes decision on how to participate in a set of social activities, some with higher frequencies while others lower or completely avoided, to minimize his/her social contacts with least possible social or economic costs. An optimal distancing strategy is then obtained when the game reaches an equilibrium. The game is simulated with various realistic restraints including (i) when the population is distributed over a social network, and the decision of each individual is made through the interactions with his/her social neighbors; (ii) when the individuals in different social groups such as children vs. adults or the vaccinated vs. unprotected have different distancing preferences; (iii) when leadership plays a role in decision making, with a certain number of leaders making decisions while the rest of the population just follow. The simulation results show how the distancing game is played out in each of these scenarios, reveal the conflicting yet cooperative nature of social distancing, and shed lights on a self-organizing, bottom-up perspective of distancing practices.

Assessing the impacts of vaccination and viral evolution in contact networks

A key lesson learned with COVID-19 is that public health measures were very different from country to country. In this study, we provide an analysis of epidemic dynamics using three well-known stochastic network models-small-world networks (Watts-Strogatz), random networks (Erdös-Rényi), and scale-free networks (Barabási-Albert)-to assess the impact of different viral strains, lockdown strategies, and vaccination campaigns. We highlight the significant role of highly connected nodes in the spread of infections, particularly within Barabási-Albert networks. These networks experienced earlier and higher peaks in infection rates, but ultimately had the lowest total number of infections, indicating their rapid transmission dynamics. We also found that intermittent lockdown strategies, particularly those with 7-day intervals, effectively reduce the total number of infections, serving as viable alternatives to prolonged continuous lockdowns. When simulating vaccination campaigns, we observed a bimodal distribution leading to two distinct outcomes: pandemic contraction and pandemic expansion. For WS and ER networks, rapid mass vaccination campaigns significantly reduced infection rates compared to slower campaigns; however, for BA networks, differences between vaccination strategies were minimal. To account for the evolution of a virus into a more transmissible strain, we modeled vaccination scenarios that varied vaccine efficacy against the wild-type virus and noted a decline in this efficacy over time against a second variant. Our results showed that vaccination coverage above 40% significantly flattened infection peaks for the wild-type virus, while at least 80% coverage was required to similarly reduce peaks for variant 2. Furthermore, the effect of vaccine efficacy on reducing the peak of variant 2 infection was minimal. Although vaccination strategies targeting hub nodes in scale-free networks did not substantially reduce the total number of infections, they were effective in increasing the probability of preventing pandemic outbreaks. These findings underscore the need to consider the network structure for effective pandemic control.

Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably discovered through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.

The illusion of personal health decisions for infectious disease management: disease spread in social contact networks

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

Impact of random and targeted disruptions on information diffusion during outbreaks

Outbreaks are complex multi-scale processes that are impacted not only by cellular dynamics and the ability of pathogens to effectively reproduce and spread, but also by population-level dynamics and the effectiveness of mitigation measures. A timely exchange of information related to the spread of novel pathogens, stay-at-home orders, and other measures can be effective at containing an infectious disease, particularly during the early stages when testing infrastructure, vaccines, and other medical interventions may not be available at scale. Using a multiplex epidemic model that consists of an information layer (modeling information exchange between individuals) and a spatially embedded epidemic layer (representing a human contact network), we study how random and targeted disruptions in the information layer (e.g., errors and intentional attacks on communication infrastructure) impact the total proportion of infections, peak prevalence (i.e., the maximum proportion of infections), and the time to reach peak prevalence. We calibrate our model to the early outbreak stages of the SARS-CoV-2 pandemic in 2020. Mitigation campaigns can still be effective under random disruptions, such as failure of information channels between a few individuals. However, targeted disruptions or sabotage of hub nodes that exchange information with a large number of individuals can abruptly change outbreak characteristics, such as the time to reach the peak of infection. Our results emphasize the importance of the availability of a robust communication infrastructure during an outbreak that can withstand both random and targeted disruptions.

Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models

BACKGROUND

The increasing availability of data on social contact patterns and time use provides invaluable information for studying transmission dynamics of infectious diseases. Social contact data provide information on the interaction of people in a population whereas the value of time use data lies in the quantification of exposure patterns. Both have been used as proxies for transmission risks within in a population and the combination of both sources has led to investigate which contacts are more suitable to describe these transmission risks.

METHODS

We used social contact and time use data from 1707 participants from a survey conducted in Flanders, Belgium in 2010-2011. We calculated weighted exposure time and social contact matrices to analyze age- and gender-specific mixing patterns and to quantify behavioral changes by distance from home. We compared the value of both separate and combined data sources for explaining seroprevalence and incidence data on parvovirus-B19, Varicella-Zoster virus (VZV) and influenza like illnesses (ILI), respectively.

RESULTS

Assortative mixing and inter-generational interaction is more pronounced in the exposure matrix due to the high proportion of time spent at home. This pattern is less pronounced in the social contact matrix, which is more impacted by the reported contacts at school and work. The average number of contacts declined with distance. On the individual-level, we observed an increase in the number of contacts and the transmission potential by distance when travelling. We found that both social contact data and time use data provide a good match with the seroprevalence and incidence data at hand. When comparing the use of different combinations of both data sources, we found that the social contact matrix based on close contacts of at least 4 h appeared to be the best proxy for parvovirus-B19 transmission. Social contacts and exposure time were both on their own able to explain VZV seroprevalence data though combining both scored best. Compared with the contact approach, the time use approach provided the better fit to the ILI incidence data.

CONCLUSIONS

Our work emphasises the common and complementary value of time use and social contact data for analysing mixing behavior and analysing infectious disease transmission. We derived spatial, temporal, age-, gender- and distance-specific mixing patterns, which are informative for future modelling studies.

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

During the ongoing COVID-19 pandemic, exposure notification apps have been developed to scale up manual contact tracing. The apps use proximity data from mobile devices to automate notifying direct contacts of an infection source. The information they provide is limited because users receive only rare and binary alerts. Here we present network data assimilation (DA) as a new digital approach to epidemic management and control. Network DA uses the same data as exposure notification apps but uses it more effectively to provide frequently updated individual risk assessments to users.

Network DA is based on automated learning about individuals’ risk of exposure and infection from crowd-sourced health data and proximity data. The data are aggregated with models of disease transmission to produce statistical assessments of users’ risks. In an extensive simulation study of the COVID-19 epidemic in New York City (NYC), we show that network DA with diagnostic testing achieves epidemic control with fewer than half the deaths that occurred during NYC’s lockdown, while isolating a far smaller fraction of the population (typically only 5–10% of the population at any given time).

Implemented at scale, then, network DA has the potential to effectively control epidemics while minimizing economic and social disruption.

Patterns of human social contact and mask wearing in high-risk groups in China

Background

The pandemic of coronavirus disease 2019 (COVID-19) has changed human behavior in areas such as contact patterns and mask-wearing frequency. Exploring human–human contact patterns and mask-wearing habits in high-risk groups is an essential step in fully understanding the transmission of respiratory infection-based diseases. This study had aims to quantify local human–human (H–H) contacts in high-risk groups in representative provinces of China and to explore the occupation-specific assortativity and heterogeneity of social contacts.

Methods

Delivery workers, medical workers, preschoolers, and students from Qinghai, Shanghai, and Zhejiang were recruited to complete an online questionnaire that queried general information, logged contacts, and assessed the willingness to wear a mask in different settings. The “group contact” was defined as contact with a group at least 20 individuals. The numbers of contacts across different characteristics were assessed and age-specific contact matrices were established. A generalized additive mixed model was used to analyze the associations between the number of individual contacts and several characteristics. The factors influencing the frequency of mask wearing were evaluated with a logistic regression model.

Results

A total of 611,287 contacts were reported by 15,635 participants. The frequency of daily individual contacts averaged 3.14 (95% confidence interval: 3.13–3.15) people per day, while that of group contacts was 37.90 (95% CI: 37.20–38.70). Skin-to-skin contact and long-duration contact were more likely to occur at home or among family members. Contact matrices of students were the most assortative (all contacts q-index = 0.899, 95% CI: 0.894–0.904). Participants with larger household sizes reported having more contacts. Higher household income per capita was significantly associated with a greater number of contacts among preschoolers (P_{50,000–99,999} = 0.033) and students (P_{10,000–29,999} = 0.017). In each of the public places, the frequency of mask wearing was highest for delivery workers. For preschoolers and students with more contacts, the proportion of those who reported always wearing masks was lower (P < 0.05) in schools/workplaces and public transportation than preschoolers and students with fewer contacts.

Conclusions

Contact screening efforts should be concentrated in the home, school, and workplace after an outbreak of an epidemic, as more than 75% of all contacts, on average, will be found in such places. Efforts should be made to improve the mask-wearing rate and age-specific health promotion measures aimed at reducing transmission for the younger demographic. Age-stratified and occupation-specific social contact research in high-risk groups could help inform policy-making decisions during the post-relaxation period of the COVID-19 pandemic.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00988-8.

Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study

BACKGROUND

The emergence and re-emergence of infectious diseases presents a significant challenge to public health and broader society. This study utilises novel nationwide data to calculate the transmission risk and potential inequity of infectious disease outbreaks through use of network analysis.

METHODS

Nationwide employment and education microdata (∼4.7 million individuals in Aotearoa New Zealand) were used to develop the Aotearoa Co-incidence Network (ACN). The ACN considers connections generated when individuals are employed at the same workplaces or enrolled at the same schools. Through forms of network analysis, connections between geospatial areas can be established and provide proxy measures of infectious disease transmission risk. The ACN was also overlayed with nationwide population vulnerability data based on the number of older adults (>65 years) and individuals with long-term health conditions.

FINDINGS

We identify areas that have both high potential transmission risk (i.e., highly connected) and high vulnerability to infectious diseases. Community detection identified geographic boundaries that can be relevant to the application of regional restrictions for limiting infectious disease transmission.

INTERPRETATION

Integrating novel network science and geospatial analytics provides a simple way to study infectious disease transmission risk and population vulnerability to outbreaks. Our replicable method has utility for researchers globally with access to such data. It can help inform equitable preparation for, and responses to infectious disease outbreaks.

FUNDING

This project was funded by the Health Research Council of New Zealand (20/1442) and from the NZ Government via Ministry for Business Innovation and Employment and Department of Prime Minister and Cabinet.

Understanding collective regularity in human mobility as a familiar stranger phenomenon

Beyond the physical structures that contain daily routines, urban city dwellers repeatedly encounter strangers that similarly shape their environments. Familiar strangers are neither formal acquaintances nor completely anonymous faces in daily urban life. Due to data limitations, there is a lack of research focused on uncovering the structure of the “Familiar Stranger” phenomenon at a large scale while simultaneously investigating the social relationships between such strangers. Using countrywide mobile phone records from Andorra, we empirically show the existence of such a phenomenon as well as details concerning these strangers’ relative social relations. To understand the social and spatial components of familiar strangers more deeply, we study the temporal regularity and spatial structure of collective urban mobility to shed light on the mechanisms that guide these interactions. Furthermore, we explore the relationship between social distances and the number of encounters to show that more significant physical encounters correspond to a shorter social distance. Understanding these social and physical networks has essential implications for epidemics spreading, urban planning, and information diffusion.

Model-driven mitigation measures for reopening schools during the COVID-19 pandemic

Reopening schools is an urgent priority as the COVID-19 pandemic drags on. To explore the risks associated with returning to in-person learning and the value of mitigation measures, we developed stochastic, network-based models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in primary and secondary schools. We find that a number of mitigation measures, alone or in concert, may reduce risk to acceptable levels. Student cohorting, in which students are divided into two separate populations that attend in-person classes on alternating schedules, can reduce both the likelihood and the size of outbreaks. Proactive testing of teachers and staff can help catch introductions early, before they spread widely through the school. In secondary schools, where the students are more susceptible to infection and have different patterns of social interaction, control is more difficult. Especially in these settings, planners should also consider testing students once or twice weekly. Vaccinating teachers and staff protects these individuals and may have a protective effect on students as well. Other mitigations, including mask wearing, social distancing, and increased ventilation, remain a crucial component of any reopening plan.

Modelling SARS-CoV-2 transmission in a UK university setting

Around 40% of school leavers in the UK attend university and individual universities generally host thousands of students each academic year. Bringing together these student communities during the COVID-19 pandemic may require strong interventions to control transmission. Prior modelling analyses of SARS-CoV-2 transmission within universities using compartmental modelling approaches suggest that outbreaks are almost inevitable. We constructed a network-based model to capture the interactions of a student population in different settings (housing, social and study). For a single academic term of a representative campus-based university, we ran a susceptible-latent-infectious-recovered type epidemic process, parameterised according to available estimates for SARS-CoV-2. We investigated the impact of: adherence to (or effectiveness of) isolation and test and trace measures; room isolation of symptomatic students; and supplementary mass testing. With all adhering to test, trace and isolation measures, we found that 22% (7%-41%) of the student population could be infected during the autumn term, compared to 69% (56%-76%) when assuming zero adherence to such measures. Irrespective of the adherence to isolation measures, on average a higher proportion of students resident on-campus became infected compared to students resident off-campus. Room isolation generated minimal benefits. Regular mass testing, together with high adherence to isolation and test and trace measures, could substantially reduce the proportion infected during the term compared to having no testing. Our findings suggest SARS-CoV-2 may readily transmit in a university setting if there is limited adherence to nonpharmaceutical interventions and/or there are delays in receiving test results. Following isolation guidance and effective contact tracing curbed transmission and reduced the expected time an adhering student would spend in isolation.

High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

Controlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6-35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

University students and staff able to maintain low daily contact numbers during various COVID-19 guideline periods

UK universities re-opened in September 2020, amidst the coronavirus epidemic. During the first term, various national social distancing measures were introduced, including banning groups of >6 people and the second lockdown in November; however, outbreaks among university students occurred. We aimed to measure the University of Bristol staff and student contact patterns via an online, longitudinal survey capturing self-reported contacts on the previous day. We investigated the change in contacts associated with COVID-19 guidance periods: post-first lockdown (23/06/2020–03/07/2020), relaxed guidance period (04/07/2020–13/09/2020), ‘rule-of-six’ period (14/09/2020–04/11/2020) and the second lockdown (05/11/2020–25/11/2020). In total, 722 staff (4199 responses) and 738 students (1906 responses) were included in the study. For staff, daily contacts were higher in the relaxed guidance and ‘rule-of-six’ periods than the post-first lockdown and second lockdown. Mean student contacts dropped between the ‘rule-of-six’ and second lockdown periods. For both staff and students, the proportion meeting with groups larger than six dropped between the ‘rule-of-six’ period and the second lockdown period, although was higher for students than for staff. Our results suggest university staff and students responded to national guidance by altering their social contacts. Most contacts during the second lockdown were household contacts. The response in staff and students was similar, suggesting that students can adhere to social distancing guidance while at university. The number of contacts recorded for both staff and students were much lower than those recorded by previous surveys in the UK conducted before the COVID-19 pandemic.

Threshold analyses on combinations of testing, population size, and vaccine coverage for COVID-19 control in a university setting

We simulated epidemic projections of a potential COVID-19 outbreak in a residential university population in the United States under varying combinations of asymptomatic tests (5% to 33% per day), transmission rates (2.5% to 14%), and contact rates (1 to 25), to identify the contact rate threshold that, if exceeded, would lead to exponential growth in infections. Using this, we extracted contact rate thresholds among non-essential workers, population size thresholds in the absence of vaccines, and vaccine coverage thresholds. We further stream-lined our analyses to transmission rates of 5 to 8%, to correspond to the reported levels of face-mask-use/physical-distancing during the 2020 pandemic. Our results suggest that, in the absence of vaccines, testing alone without reducing population size would not be sufficient to control an outbreak. If the population size is lowered to 34% (or 44%) of the actual population size to maintain contact rates at 4 (or 7) among non-essential workers, mass tests at 25% (or 33%) per day would help control an outbreak. With the availability of vaccines, the campus can be kept at full population provided at least 95% are vaccinated. If vaccines are partially available such that the coverage is lower than 95%, keeping at full population would require asymptomatic testing, either mass tests at 25% per day if vaccine coverage is at 63-79%, or mass tests at 33% per day if vaccine coverage is at 53-68%. If vaccine coverage is below 53%, to control an outbreak, in addition to mass tests at 33% per day, it would also require lowering the population size to 90%, 75%, and 60%, if vaccine coverage is at 38-53%, 23-38%, and below 23%, respectively. Threshold estimates from this study, interpolated over the range of transmission rates, can collectively help inform campus level preparedness plans for adoption of face mask/physical-distancing, testing, remote instructions, and personnel scheduling, during non-availability or partial-availability of vaccines, in the event of SARS-Cov2-type disease outbreaks.

SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return

In this paper, we present work on SARS-CoV-2 transmission in UK higher education settings using multiple approaches to assess the extent of university outbreaks, how much those outbreaks may have led to spillover in the community, and the expected effects of control measures. Firstly, we found that the distribution of outbreaks in universities in late 2020 was consistent with the expected importation of infection from arriving students. Considering outbreaks at one university, larger halls of residence posed higher risks for transmission. The dynamics of transmission from university outbreaks to wider communities is complex, and while sometimes spillover does occur, occasionally even large outbreaks do not give any detectable signal of spillover to the local population. Secondly, we explored proposed control measures for reopening and keeping open universities. We found the proposal of staggering the return of students to university residence is of limited value in terms of reducing transmission. We show that student adherence to testing and self-isolation is likely to be much more important for reducing transmission during term time. Finally, we explored strategies for testing students in the context of a more transmissible variant and found that frequent testing would be necessary to prevent a major outbreak.

The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies

Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the population attributable fraction (PAF) due to groups as the relative change in the basic reproduction number when groups are prevented. Groups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95% confidence interval 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%). Under normal circumstances with pre-COVID-19 contact patterns, large groups of individuals have a relatively small epidemiological impact; small- and medium-sized groups between 10 and 50 people have a larger impact on an epidemic. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Mapping social distancing measures to the reproduction number for COVID-19

In the absence of a vaccine, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission has been controlled by preventing person-to-person interactions via social distancing measures. In order to re-open parts of society, policy-makers need to consider how combinations of measures will affect transmission and understand the trade-offs between them. We use age-specific social contact data, together with epidemiological data, to quantify the components of the COVID-19 reproduction number. We estimate the impact of social distancing policies on the reproduction number by turning contacts on and off based on context and age. We focus on the impact of re-opening schools against a background of wider social distancing measures. We demonstrate that pre-collected social contact data can be used to provide a time-varying estimate of the reproduction number (R). We find that following lockdown (when R= 0.7, 95% CI 0.6, 0.8), opening primary schools has a modest impact on transmission (R = 0.89, 95% CI 0.82-0.97) as long as other social interactions are not increased. Opening secondary and primary schools is predicted to have a larger impact (R = 1.22, 95% CI 1.02-1.53). Contact tracing and COVID security can be used to mitigate the impact of increased social mixing to some extent; however, social distancing measures are still required to control transmission. Our approach has been widely used by policy-makers to project the impact of social distancing measures and assess the trade-offs between them. Effective social distancing, contact tracing and COVID security are required if all age groups are to return to school while controlling transmission. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/2021 academic year

University students have unique living, learning and social arrangements which may have implications for infectious disease transmission. To address this data gap, we created CONQUEST (COroNavirus QUESTionnaire), a longitudinal online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. Here, we analyse results from 740 students providing 1261 unique records from the start of the 2020/2021 academic year (14/09/2020-01/11/2020), where COVID-19 outbreaks led to the self-isolation of all students in some halls of residences. Although most students reported lower daily contacts than in pre-COVID-19 studies, there was heterogeneity, with some reporting many (median = 2, mean = 6.1, standard deviation = 15.0; 8% had ≥ 20 contacts). Around 40% of students' contacts were with individuals external to the university, indicating potential for transmission to non-students/staff. Only 61% of those reporting cardinal symptoms in the past week self-isolated, although 99% with a positive COVID-19 test during the 2 weeks before survey completion had self-isolated within the last week. Some students who self-isolated had many contacts (mean = 4.3, standard deviation = 10.6). Our results provide context to the COVID-19 outbreaks seen in universities and are available for modelling future outbreaks and informing policy.

A cross-sectional study measuring contact patterns using diaries in an urban and a rural community in South Africa, 2018

BACKGROUND

Describing contact patterns is crucial to understanding infectious disease transmission dynamics and guiding targeted transmission mitigation interventions. Data on contact patterns in Africa, especially South Africa, are limited. We measured and compared contact patterns in a rural and urban community, South Africa. We assessed participant and contact characteristics associated with differences in contact rates.

METHODS

We conducted a cross-sectional study nested in a prospective household cohort study. We interviewed participants to collect information on persons in contact with for one day. We described self-reported contact rates as median number people contacted per day, assessed differences in contact rates based on participant characteristics using quantile regression, and used a Poisson model to assess differences in contact rates based on contact characteristics within age groups. We also calculated cumulative person hours in contact within age groups at different locations.

RESULTS

We conducted 535 interviews (269 rural, 266 urban), with 17,252 contacts reported. The overall contact rate was 14 (interquartile range (IQR) 9-33) contacts per day. Those ≤18 years had higher contact rates at the rural site (coefficient 17, 95% confidence interval (95%CI) 10-23) compared to the urban site, for those aged 14-18 years (13, 95%CI 3-23) compared to < 7 years. No differences were observed for adults. There was a strong age-based mixing, with age groups interacting more with similar age groups, but also interaction of participants of all ages with adults. Children aged 14-18 years had the highest cumulative person hours in contact (116.3 rural and 76.4 urban).

CONCLUSIONS

Age played an important role in the number and duration of contact events, with children at the rural site having almost double the contact rate compared to the urban site. These contact rates can be utilized in mathematical models to assess transmission dynamics of infectious diseases in similar communities.

A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create 'COVID-secure' workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels. In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.

The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China

Nonpharmaceutical interventions to control SARS-CoV-2 spread have been implemented with different intensity, timing, and impact on transmission. As a result, post-lockdown COVID-19 dynamics are heterogeneous and difficult to interpret. We describe a set of contact surveys performed in four Chinese cities (Wuhan, Shanghai, Shenzhen, and Changsha) during the pre-pandemic, lockdown and post-lockdown periods to quantify changes in contact patterns. In the post-lockdown period, the mean number of contacts increased by 5 to 17% as compared to the lockdown period. However, it remains three to seven times lower than its pre-pandemic level sufficient to control SARS-CoV-2 transmission. We find that the impact of school interventions depends nonlinearly on the intensity of other activities. When most community activities are halted, school closure leads to a 77% decrease in the reproduction number; in contrast, when social mixing outside of schools is at pre-pandemic level, school closure leads to a 5% reduction in transmission.

The value of infectious disease modeling and trend assessment: a public health perspective

INTRODUCTION

Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases.

AREAS COVERED

This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020).

EXPERT OPINION

Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.

C, Galloway D, Schweizer ML, Guclu H, Cousins J, Lingle C, Yearwood GMH, Li K, Calderone P, Noble E, Gao H, Rainey J, Uzicanin A, Read JM. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

Predicting Oil Production Sites for Planning Road Infrastructure: Trip Generation Using SIR Epidemic Model

Drilling activity produces a significant amount of road traffic through unpaved and paved local roads. Because oil production is an important contributor to the local economy in the state of North Dakota, the state and local transportation agencies make efforts to support local energy logistics through the expansion and good repair and maintenance of transportation infrastructure. As part of this effort, it is important to build new roads and bridges, maintain existing road pavement and non-marked road surface conditions, and improve bridge and other transportation infrastructure. Therefore, the purpose of this study is to review previous oil location prediction models and propose a novel geospatial model to predict drilling locations which have a significant impact on local roads, to verify and provide a better prediction model. Then, this study proposes a SIR (susceptible–infected–recovered) epidemic model to predict oil drilling locations which are traffic generators. The simulation has been done on the historical data from 1980 to 2015. The study found that the best fit parameters of β (contact rate) and μ (recovery rate) were estimated by using a dataset of historical oil wells. The study found that the SIR epidemic model can be applied to predict the locations of oil wells. The proposed model can be used to predict other drilling locations and can assist with traffic, road conditions, and other related issues, which is a much needed predictive model that is key in transportation planning and pavement design and maintenance.

Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure-including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19)

OBJECTIVE

Contact tracing is a central public health response to infectious disease outbreaks, especially in the early stages of an outbreak when specific treatments are limited. Importation of novel coronavirus (COVID-19) from China and elsewhere into the UK highlights the need to understand the impact of contact tracing as a control measure.

DESIGN

Detailed survey information on social encounters from over 5800 respondents is coupled to predictive models of contact tracing and control. This is used to investigate the likely efficacy of contact tracing and the distribution of secondary cases that may go untraced.

RESULTS

Taking recent estimates for COVID-19 transmission we predict that under effective contact tracing less than 1 in 6 cases will generate any subsequent untraced infections, although this comes at a high logistical burden with an average of 36 individuals traced per case. Changes to the definition of a close contact can reduce this burden, but with increased risk of untraced cases; we find that tracing using a contact definition requiring more than 4 hours of contact is unlikely to control spread.

CONCLUSIONS

The current contact tracing strategy within the UK is likely to identify a sufficient proportion of infected individuals such that subsequent spread could be prevented, although the ultimate success will depend on the rapid detection of cases and isolation of contacts. Given the burden of tracing a large number of contacts to find new cases, there is the potential the system could be overwhelmed if imports of infection occur at a rapid rate.

A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections

BACKGROUND

Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published.

METHODS

We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible.

RESULTS

In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies.

CONCLUSIONS

We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China

Non-pharmaceutical interventions to control COVID-19 spread have been implemented in several countries with different intensity, timing, and impact on transmission. As a result, post-lockdown COVID-19 dynamics are heterogenous and difficult to interpret. Here we describe a set of contact surveys performed in four Chinese cities (Wuhan, Shanghai, Shenzhen, and Changsha) during the pre-pandemic, lockdown, and post-lockdown period to quantify the transmission impact of relaxing interventions via changes in age-specific contact patterns. We estimate that the mean number of contacts increased 5%-17% since the end of the lockdown but are still 3-7 times lower than their pre-pandemic levels. We find that post-lockdown contact patterns in China are still sufficiently low to keep SARS-CoV-2 transmission under control. We also find that the impact of school interventions depends non-linearly on the share of other activities being resumed. When most community activities are halted, school closure leads to a 77% decrease in the reproductive number; in contrast, when social mixing outside of schools is at pre-pandemic level, school closure leads to a 5% reduction in transmission. Moving forward, to control COVID-19 spread without resorting to a lockdown, it will be key to dose relaxation in social mixing in the community and strengthen targeted interventions.

Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices

The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.

Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices

The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.

Ethical guidelines for COVID-19 tracing apps

Contact tracing is a central public health response to infectious disease outbreaks, especially in the early stages of an outbreak when specific treatments are limited. Importation of novel Coronavirus (COVID-19) from China and elsewhere into the United Kingdom highlights the need to understand the impact of contact tracing as a control measure. Using detailed survey information on social encounters coupled to predictive models, we investigate the likely efficacy of the current UK definition of a close contact (within 2 meters for 15 minutes or more) and the distribution of secondary cases that may go untraced. Taking recent estimates for COVID-19 transmission, we show that less than 1 in 5 cases will generate any subsequent untraced cases, although this comes at a high logistical burden with an average of 36.1 individuals (95th percentiles 0-182) traced per case. Changes to the definition of a close contact can reduce this burden, but with increased risk of untraced cases; we estimate that any definition where close contact requires more than 4 hours of contact is likely to lead to uncontrolled spread.

Spatiotemporal heterogeneity of social contact patterns related to infectious diseases in the Guangdong Province, China

The social contact patterns associated with the infectious disease transmitted by airborne droplets or close contact follow specific rules. Understanding these processes can improve the accuracy of disease transmission models, permitting their integration into model simulations. In this study, we performed a large-scale population-based survey to collect social contact patterns in three cities on the Pearl River Delta of China in winter and summer. A total of 5,818 participants were face-to-face interviewed and 35,542 contacts were recorded. The average number of contacts per person each day was 16.7 considering supplementary professional contacts (SPCs). Contacts that occurred on a daily basis, lasted more than 4 hours, and took place in households were more likely to involve physical contact. The seasonal characteristics of social contact were heterogeneous, such that contact in the winter was more likely to involve physical contact compared to summer months. The spatial characteristics of the contacts were similar. Social mixing patterns differed according to age, but all ages maintained regular contact with their peers. Taken together, these findings describe the spatiotemporal distribution of social contact patterns relevant to infections in the Guangdong Province of China. This information provides important parameters for mathematical models of infectious diseases.

A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread.

Patterns of human social contact and contact with animals in Shanghai, China

East Asia is as a principal hotspot for emerging zoonotic infections. Understanding the likely pathways for their emergence and spread requires knowledge on human-human and human-animal contacts, but such studies are rare. We used self-completed and interviewer-completed contact diaries to quantify patterns of these contacts for 965 individuals in 2017/2018 in a high-income densely-populated area of China, Shanghai City. Interviewer-completed diaries recorded more social contacts (19.3 vs. 18.0) and longer social contact duration (35.0 vs. 29.1 hours) than self-reporting. Strong age-assortativity was observed in all age groups especially among young participants (aged 7-20) and middle aged participants (25-55 years). 17.7% of participants reported touching animals (15.3% (pets), 0.0% (poultry) and 0.1% (livestock)). Human-human contact was very frequent but contact with animals (especially poultry) was rare although associated with frequent human-human contact. Hence, this densely populated area is more likely to act as an accelerator for human-human spread but less likely to be at the source of a zoonosis outbreak. We also propose that telephone interview at the end of reporting day is a potential improvement of the design of future contact surveys.

Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission.

Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week.

Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

Networks of face-to-face social contacts in Niakhar, Senegal

We present the first analysis of face-to-face contact network data from Niakhar, Senegal. Participants in a cluster-randomized influenza vaccine trial were interviewed about their contact patterns when they reported symptoms during their weekly household surveillance visit. We employ a negative binomial model to estimate effects of covariates on contact degree. We estimate the mean contact degree for asymptomatic Niakhar residents to be 16.5 (95% C.I. 14.3, 18.7) in the morning and 14.8 in the afternoon (95% C.I. 12.7, 16.9). We estimate that symptomatic people make 10% fewer contacts than asymptomatic people (95% C.I. 5%, 16%; p = 0.006), and those aged 0-5 make 33% fewer contacts than adults (95% C.I. 29%, 37%; p < 0.001). By explicitly modelling the partial rounding pattern observed in our data, we make inference for both the underlying (true) distribution of contacts as well as for the reported distribution. We created an estimator for homophily by compound (household) membership and estimate that 48% of contacts by symptomatic people are made to their own compound members in the morning (95% CI, 45%, 52%) and 60% in the afternoon/evening (95% CI, 56%, 64%). We did not find a significant effect of symptom status on compound homophily. We compare our findings to those from other countries and make design recommendations for future surveys.

Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal

Many animals socialize in two or more major ecological contexts. In nature, these contexts often involve one situation in which space is more constrained (e.g. shared refuges, sleeping cliffs, nests, dens or burrows) and another situation in which animal movements are relatively free (e.g. in open spaces lacking architectural constraints). Although it is widely recognized that an individual's characteristics may shape its social life, the extent to which architecture constrains social decisions within and between habitats remains poorly understood. Here we developed a novel, automated-monitoring system to study the effects of personality, life-history stage and sex on the social network structure of a facultatively social mammal, the California ground squirrel (Otospermophilus beecheyi) in two distinct contexts: aboveground where space is relatively open and belowground where it is relatively constrained by burrow architecture. Aboveground networks reflected affiliative social interactions whereas belowground networks reflected burrow associations. Network structure in one context (belowground), along with preferential juvenile-adult associations, predicted structure in a second context (aboveground). Network positions of individuals were generally consistent across years (within contexts) and between ecological contexts (within years), suggesting that individual personalities and behavioural syndromes, respectively, contribute to the social network structure of these free-living mammals. Direct ties (strength) tended to be stronger in belowground networks whereas more indirect paths (betweenness centrality) flowed through individuals in aboveground networks. Belowground, females fostered significantly more indirect paths than did males. Our findings have important potential implications for disease and information transmission, offering new insights into the multiple factors contributing to social structures across ecological contexts.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

Quantifying heterogeneous contact patterns in Japan: a social contact survey

BACKGROUND

Social contact surveys can greatly help in quantifying the heterogeneous patterns of infectious disease transmission. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza.

METHODS

An internet-based questionnaire survey was conducted, covering all 47 prefectures in Japan and including a total of 1476 households. The social contact matrix was quantified assuming reciprocity and using the maximum likelihood method. By imposing several parametric assumptions for the next-generation matrix, the empirical seroepidemiological data of influenza A (H1N1) 2009 was analysed and we estimated the basic reproduction number, R0.

RESULTS

In total, the reported number of contacts on weekdays was 10,682 whereas that on weekend days was 8867. Strong age-dependent assortativity was identified. Forty percent of weekday contacts took place at schools or workplaces, but that declined to 14% on weekends. Accounting for the age-dependent heterogeneity with the known social contact matrix, the minimum value of the Akaike information criterion was obtained and R0 was estimated at 1.45 (95% confidence interval: 1.42, 1.49).

CONCLUSIONS

Survey datasets will be useful for parameterizing the heterogeneous transmission model of various directly transmitted infectious diseases in Japan. Age-dependent assortativity, especially among children, along with numerous contacts in school settings on weekdays implies the potential effectiveness of school closure.

Capturing sexual contact patterns in modelling the spread of sexually transmitted infections: Evidence using Natsal-3

BACKGROUND

Understanding the spread of sexually transmitted infections (STIs) in a population is of great importance to the planning and delivery of health services globally. The worldwide rise of HIV since the 1980's, and the recent increase in common STIs (including HPV and Chlamydia) in many countries, means that there is an urgent need to understand transmission dynamics in order to better predict the spread of such infections in the population. Unlike many other infections which can be captured by assumptions of random mixing, STI transmission is intimately linked to the number and pattern of sexual contacts. In fact, it is the huge variation in the number of new sexual partners that gives rise to the extremes of risk within populations which need to be captured in predictive models of STI transmission. Such models are vital in providing the necessary scientific evidence to determine whether a range of controls (from education to screening to vaccination) are cost-effective.

METHOD AND RESULTS

We use probability sample survey data from Britain's third National Survey of Sexual Attitudes and Lifestyles (Natsal-3) to determine robust distributions for the rate of new partnerships that involve condomless sex and can therefore facilitate the spread of STIs. Different distributions are defined depending on four individual-level characteristics: age, sex, sexual orientation, and previous sexual experience. As individual behaviour patterns can change (e.g. by remaining in a monogamous relationship for a long period) we allow risk-percentiles to be randomly redrawn, to capture longer term behaviour as measured by Natsal-3. We demonstrate how this model formulation interacts with the transmission of infection by constructing an individual-based SIS-P (Susceptible-Infected-Susceptible-Protected) transmission model for the spread of a generic STI, and observing overall population demographics when varying the transmission probability within a partnership, recovery rate and the level of population protection (e.g. from vaccination where applicable).

Infectious illness prevention and control methods and their effectiveness in non-health workplaces: an integrated literature review

BACKGROUND

Infectious illness in the workplace places a substantial cost burden on employers due to productivity losses from employee absenteeism and presenteeism.

AIM

Given the clear impacts of infectious illness on workplaces, this review aimed to investigate the international literature on the effectiveness and cost-benefit of the strategies non-healthcare workplaces use to prevent and control infectious illnesses in these workplaces.

METHODS

MEDLINE, CINAHL Plus with Fulltext and Business Source Complete were searched concurrently using EBSCO Host 1995-2016.

FINDINGS

Infection prevention and control strategies to reduce workplace infectious illness and absenteeism evaluated in the literature include influenza vaccination programs, use of alcohol-based hand sanitiser and paid sick days. While the reported studies have various methodological flaws, there is good evidence of the effectiveness of influenza vaccination in preventing workplace infectious illness and absences and moderate evidence to support hand hygiene programs.

DISCUSSION

Some studies used more than one intervention concurrently, making it difficult to determine the relative benefit of each individual strategy. Workplace strategies to prevent and control infectious illness transmission may reduce costs and productivity losses experienced by businesses and organisations related to infectious illness absenteeism and presenteeism.

Quantifying potentially infectious sharing patterns among people who inject drugs in Baltimore, USA

Mixing matrices quantify how people with similar or different characteristics make contact with each other, creating potential for disease transmission. Little empirical data on mixing patterns among persons who inject drugs (PWID) are available to inform models of blood-borne disease such as HIV and hepatitis C virus. Egocentric drug network data provided by PWID in Baltimore, Maryland between 2005 and 2007 were used to characterise drug equipment-sharing patterns according to age, race and gender. Black PWID and PWID who were single (i.e. no stable sexual partner) self-reported larger equipment-sharing networks than their white and non-single counterparts. We also found evidence of assortative mixing according to age, gender and race, though to a slightly lesser degree in the case of gender. Highly assortative mixing according to race and gender highlights the existence of demographically isolated clusters, for whom generalised treatment interventions may have limited benefits unless targeted directly. These findings provide novel insights into mixing patterns of PWID for which little empirical data are available. The age-specific assortativity we observed is also significant in light of its role as a key driver of transmission for other pathogens such as influenza and tuberculosis.

Relaxation dynamics of maximally clustered networks

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics-the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

Temporal variation of human encounters and the number of locations in which they occur: a longitudinal study of Hong Kong residents

Patterns of social contact between individuals are important for the transmission of many pathogens and shaping patterns of immunity at the population scale. To refine our understanding of how human social behaviour may change over time, we conducted a longitudinal study of Hong Kong residents. We recorded the social contact patterns for 1450 individuals, up to four times each between May 2012 and September 2013. We found individuals made contact with an average of 12.5 people within 2.9 geographical locations, and spent an average estimated total duration of 9.1 h in contact with others during a day. Distributions of the number of contacts and locations in which contacts were made were not significantly different between study waves. Encounters were assortative by age, and the age mixing pattern was broadly consistent across study waves. Fitting regression models, we examined the association of contact rates (number of contacts, total duration of contact, number of locations) with covariates and calculated the inter- and intra-participant variation in contact rates. Participant age was significantly associated with the number of contacts made, the total duration of contact and the number of locations in which contact occurred, with children and parental-age adults having the highest rates of contact. The number of contacts and contact duration increased with the number of contact locations. Intra-individual variation in contact rate was consistently greater than inter-individual variation. Despite substantial individual-level variation, remarkable consistency was observed in contact mixing at the population scale. This suggests that aggregate measures of mixing behaviour derived from cross-sectional information may be appropriate for population-scale modelling purposes, and that if more detailed models of social interactions are required for improved public health modelling, further studies are needed to understand the social processes driving intra-individual variation.

Emergence of encounter networks due to human mobility

There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times. This generates a temporal network that is characterized by global quantities. We compare this dynamics with real data for two cities: New York City and Tokyo. We use data from the location-based social network Foursquare and obtain the emergent temporal encounter network, for these two cities, that we compare with our model. We found long-range (Lévy-like) distributions for traveled distances and time intervals that characterize the emergent social network due to human mobility. Studying this connection is important for several fields like epidemics, social influence, voting, contagion models, behavioral adoption and diffusion of ideas.

Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.

Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.