The relative importance of frequency of contacts and duration of exposure for the spread of directly transmitted infections

A novel fractal fractional mathematical model for HIV/AIDS transmission stability and sensitivity with numerical analysis

Understanding the complex dynamics of HIV/AIDS transmission requires models that capture real-world progression and intervention impacts. This study introduces an innovative mathematical framework using fractal-fractional calculus to analyze HIV/AIDS dynamics, emphasizing memory effects and nonlocal interactions critical to disease spread. By dividing populations into four distinct compartments-susceptible individuals, infected individuals, those undergoing treatment, and individuals in advanced AIDS stages-the model reflects key phases of infection and therapeutic interventions. Unlike conventional approaches, the proposed nonlinear transmission function, [Formula: see text], accounts for varying infectivity levels across stages (where [Formula: see text] is the total population and ∇ denotes the effective contact rate), offering a nuanced view of how treatment efficacy ([Formula: see text]) and progression to AIDS ([Formula: see text]) shape transmission. The analytical framework combines rigorous mathematical exploration with practical insights. We derive the basic reproduction number [Formula: see text] to assess outbreak potential and employ Lyapunov theory to establish global stability conditions. Using the Schauder fixed-point theorem, we prove the existence and uniqueness of solutions, while bifurcation analysis via center manifold theory reveals critical thresholds for disease persistence or elimination. We use a computational scheme that combines the Adams-Bashforth method with an interpolation-based correction technique to ensure numerical precision and confirm theoretical results. Sensitivity analysis highlights medication accessibility and delaying the spread of AIDS as a vital control strategy by identifying ([Formula: see text]) and ([Formula: see text]) as critical parameters. The numerical simulations illustrate the predictive ability of the model, which shows how fractal-fractional order affects outbreak trajectories and long-term disease burden. The framework outperforms conventional integer order models and produces more accurate epidemiological predictions by integrating memory-dependent transmission with fractional order flexibility. These findings demonstrate the model's value in developing targeted public health initiatives, particularly in environments with limited resources where disease monitoring and balancing treatment allocation is essential. In the end, our work provides a tool to better predict and manage the evolving challenges of HIV/AIDS by bridging the gap between theoretical mathematics and actual disease control.

Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands

BACKGROUND

Measles outbreaks are still routine, even in countries where vaccination coverage exceeds the guideline of 95%. Therefore, achieving ambitions for measles eradication will require understanding of how unvaccinated children interact with others who are unvaccinated. It is well established that schools and homes are key settings for both clustering of unvaccinated children and for transmission of infection. In this study, we evaluate the potential for contacts between unvaccinated children in these contexts to facilitate measles outbreaks with a focus on the Netherlands, where large outbreaks have been observed periodically since the introduction of mumps, measles and rubella (MMR).

METHODS AND FINDINGS

We created a network of all primary and secondary schools in the Netherlands based on the total number of household pairs between each school. A household pair are siblings from the same household who attend a different school. We parameterised the network with individual level administrative school and household data provided by the Dutch Ministry for Education and estimates of school level uptake of the MMR vaccine. We analysed the network to establish the relative strength of contact between schools and found that schools associated with low vaccine uptake are highly connected, aided by a differentiated school system in the Netherlands (Coleman homophily index (CHI) = 0.63). We simulated measles outbreaks on the network and evaluated the model against empirical measles data per postcode area from a large outbreak in 2013 (2,766 cases). We found that the network-based model could reproduce the observed size and spatial distribution of the historic outbreak much more clearly than the alternative models, with a case weighted receiver operating characteristic (ROC) sensitivity of 0.94, compared to 0.17 and 0.26 for models that do not account for specific network structure or school-level vaccine uptake, respectively. The key limitation of our framework is that it neglects transmission routes outside of school and household contexts.

CONCLUSIONS

Our framework indicates that clustering of unvaccinated children in primary schools connected by unvaccinated children in related secondary schools lead to large, connected clusters of unvaccinated children. Using our approach, we could explain historical outbreaks on a spatial level. Our framework could be further developed to aid future outbreak response.

Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study

OBJECTIVES

To quantify contact patterns of UK home delivery drivers and identify protective measures adopted during the pandemic.

METHODS

We conducted a cross-sectional online survey to measure the interactions of 170 UK delivery drivers during a working shift between 7 December 2020 and 31 March 2021.

RESULTS

Delivery drivers had a mean number of 71.6 (95% CI 61.0 to 84.1) customer contacts per shift and 15.0 (95% CI 11.2 to 19.2) depot contacts per shift. Maintaining physical distancing with customers was more common than at delivery depots. Prolonged contact (more than 5 min) with customers was reported by 5.4% of drivers on their last shift. We found 3.0% of drivers had tested positive for SARS-CoV-2 since the start of the pandemic and 16.8% of drivers had self-isolated due to a suspected or confirmed case of COVID-19. In addition, 5.3% (95% CI 2.3% to 10.2%) of participants reported having worked while ill with COVID-19 symptoms, or with a member of their household having a suspected or confirmed case of COVID-19.

CONCLUSION

Delivery drivers had a large number of face-to-face customer and depot contacts per shift compared with other working adults during this time. However, transmission risk may be curtailed as contact with customers was of short duration. Most drivers were unable to maintain physical distance with customers and at depots at all times. Usage of protective items such as face masks and hand sanitiser was widespread.

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

Quantifying variation of individual infectiousness is critical to inform disease control. Previous studies reported substantial heterogeneity in transmission of many infectious diseases including SARS-CoV-2. However, those results are difficult to interpret since the number of contacts is rarely considered in such approaches. Here, we analyze data from 17 SARS-CoV-2 household transmission studies conducted in periods dominated by ancestral strains, in which the number of contacts was known. By fitting individual-based household transmission models to these data, accounting for number of contacts and baseline transmission probabilities, the pooled estimate suggests that the 20% most infectious cases have 3.1-fold (95% confidence interval: 2.2- to 4.2-fold) higher infectiousness than average cases, which is consistent with the observed heterogeneity in viral shedding. Household data can inform the estimation of transmission heterogeneity, which is important for epidemic management.

Influence of indoor environmental conditions on airborne transmission and lifetime of sneeze droplets in a confined space: a way to reduce COVID-19 spread

Effects of indoor temperature (T∞) and relative humidity (RH∞) on the airborne transmission of sneeze droplets in a confined space were studied over the T∞ range of 15-30 °C and RH∞ of 22-62%. In addition, a theoretical evaporation model was used to estimate the droplet lifetime based on experimental data. The results showed that the body mass index (BMI) of the participants played an important role in the sneezing jet velocity, while the impact of the BMI and gender of participants was insignificant on the size distribution of droplets. At a critical relative humidity RH∞,crit of 46%, the sneezing jet velocity and droplet lifetime were roughly independent of T∞. At RH∞ < RH∞,crit, the sneezing jet velocity decreased by increasing T∞ from 15 to 30 °C, while its trend was reversed at RH∞ > RH∞,crit. The maximum spreading distance of aerosols increased by decreasing the RH∞ and increasing T∞, while the droplet lifetime increased by decreasing T∞ at RH∞ > RH∞,crit. The mean diameter of aerosolized droplets was less affected by T∞ than the large droplets at RH∞ < RH∞,crit, while the mean diameter and number fraction of aerosols were more influenced by RH∞ than the T∞ in the range of 46% ≤ RH∞ ≤ 62%. In summary, this study suggests suitable indoor environmental conditions by considering the transmission rate and lifetime of respiratory droplets to reduce the spread of COVID-19.

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.

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.

Using time-use diaries to track changing behavior across successive stages of COVID-19 social restrictions

How did people change their behavior over the different phases of the UK COVID-19 restrictions, and how did these changes affect their risk of being exposed to infection? Time-use diary surveys are unique in providing a complete chronicle of daily behavior: 24-h continuous records of the populations' activities, their social context, and their location. We present results from four such surveys, collected in real time from representative UK samples, both before and at three points over the course of the current pandemic. Comparing across the four waves, we find evidence of substantial changes in the UK population's behavior relating to activities, locations, and social context. We assign different levels of risk to combinations of activities, locations, and copresence to compare risk-related behavior across successive "lockdowns." We find evidence that during the second lockdown (November 2020), there was an increase in high-risk behaviors relative to the first (starting March 2020). This increase is shown to be associated with more paid work time in the workplace. At a time when capacity is still limited both in respect of immunization and track-trace technology, governments must continue to rely on changes in people's daily behaviors to contain the spread of COVID-19 and similar viruses. Time-use diary information of this type, collected in real time across the course of the COVID-19 pandemic, can provide policy makers with information to assess and quantify changes in daily behaviors and the impact they are likely to have on overall behavioral-associated risks.

Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread

Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Patterns of patients’ transfer between different hospitals contribute crucially to the risk of hospital-acquired infections (HAIs) in the health care system. To quantify this risk, network models can be applied. The estimated risk can be inaccurate in the case of incomplete data on hospital admissions, which can be a consequence of the multiplicity of insurance companies as it is the case in Germany. To develop a better understanding of how incompleteness of data affects network measures and the simulated spread of HAI, we compared those measures derived from sampled, scale-up and original data, based on hospitalization data from three AOK insurance companies. We found that common network measures were affected by incompleteness, but the simulated prevalence as a measure of epidemic spread in the network was robust over a large range of incompleteness proportions. Epidemics and the transition of the infectious diseases may be modelled on hospital data with a coverage as low as 10% of the local population, whilst maintaining accuracy to within 10% of the true population prevalence.

A new perspective from time use research on the effects of social restrictions on COVID-19 behavioral infection risk

We present findings from three waves of a population-representative, UK time-use diary survey conducted both pre- and in real time during full ‘lockdown’, and again following the easing of social restrictions. We used an innovative online diary instrument that has proved both reliable and quick-to-field. Combining diary information on activity, location, and co-presence to estimate infection risks associated with daily behavior, we show clear changes in risk-associated behavior between the pre, full-lockdown and post full-lockdown periods. We document a shift from more to less risky daily behavior patterns (combinations of activity/location/co-presence categories) between the pre-pandemic pattern and full lockdown in May/June 2020, followed by a reversion (although not a complete reversal) of those patterns in August 2020 following the end of the first lockdown. Because, in general, a populations’ time use changes relatively slowly, the behavioral changes revealed may be interpreted as a consequence of the UK COVID-19 lockdown social restrictions and their subsequent relaxation.

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.

Estimating density limits for walking pedestrians keeping a safe interpersonal distancing

With people trying to keep a safe distance from others due to the COVID-19 outbreak, the way in which pedestrians walk has completely changed since the pandemic broke out1,2. In this work, laboratory experiments demonstrate the effect of several variables-such as the pedestrian density, the walking speed and the prescribed safety distance-on the interpersonal distance established when people move within relatively dense crowds. Notably, we observe that the density should not be higher than 0.16 pedestrians per square meter (around 6 m2 per pedestrian) in order to guarantee an interpersonal distance of 1 m. Although the extrapolation of our findings to other more realistic scenarios is not straightforward, they can be used as a first approach to establish density restrictions in urban and architectonic spaces based on scientific evidence.

Inferring high-resolution human mixing patterns for disease modeling

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors

We provide research findings on the physics of aerosol and droplet dispersion relevant to the hypothesized aerosol transmission of SARS-CoV-2 during the current pandemic. We utilize physics-based modeling at different levels of complexity, along with previous literature on coronaviruses, to investigate the possibility of airborne transmission. The previous literature, our 0D-3D simulations by various physics-based models, and theoretical calculations, indicate that the typical size range of speech and cough originated droplets ( d ⩽ 20 μ m ) allows lingering in the air for O ( 1 h ) so that they could be inhaled. Consistent with the previous literature, numerical evidence on the rapid drying process of even large droplets, up to sizes O ( 100 μ m ) , into droplet nuclei/aerosols is provided. Based on the literature and the public media sources, we provide evidence that the individuals, who have been tested positive on COVID-19, could have been exposed to aerosols/droplet nuclei by inhaling them in significant numbers e.g. O ( 100 ) . By 3D scale-resolving computational fluid dynamics (CFD) simulations, we give various examples on the transport and dilution of aerosols ( d ⩽ 20 μ m ) over distances O ( 10 m ) in generic environments. We study susceptible and infected individuals in generic public places by Monte-Carlo modelling. The developed model takes into account the locally varying aerosol concentration levels which the susceptible accumulate via inhalation. The introduced concept, 'exposure time' to virus containing aerosols is proposed to complement the traditional 'safety distance' thinking. We show that the exposure time to inhale O ( 100 ) aerosols could range from O ( 1 s ) to O ( 1 min ) or even to O ( 1 h ) depending on the situation. The Monte-Carlo simulations, along with the theory, provide clear quantitative insight to the exposure time in different public indoor environments.

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.

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.

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.

Estimating age-mixing patterns relevant for the transmission of airborne infections

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Airborne infection transmission can occur between anybody sharing indoor space.

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We demonstrate a method for calculating age-mixing patterns for these contacts.

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It only requires data that can be easily collected during social contact surveys.

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Age-mixing patterns for these contacts may vary from those typically used in models.

Introduction

Age-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody ‘sharing air’ indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not ‘close’) is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data.

Methods

We estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group.

Results

Patterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses.

Conclusions

Patterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts.

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).

Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

Background

Nosocomial, or healthcare-associated infections (HAI), exact a high medical and financial toll on patients, healthcare workers, caretakers, and the health system. Interpersonal contact patterns play a large role in infectious disease spread, but little is known about the relationship between health care workers’ (HCW) movements and contact patterns within a heath care facility and HAI. Quantitatively capturing these patterns will aid in understanding the dynamics of HAI and may lead to more targeted and effective control strategies in the hospital setting.

Methods

Staff at 3 urban university-based tertiary care hospitals in Canada completed a detailed questionnaire on demographics, interpersonal contacts, in-hospital movement, and infection prevention and control practices. Staff were divided into categories of administrative/support, nurses, physicians, and “Other HCWs” - a fourth distinct category, which excludes physicians and nurses. Using quantitative network modeling tools, we constructed the resulting HCW “co-location network” to illustrate contacts among different occupations and with locations in hospital settings.

Results

Among 3048 respondents (response rate 38%) an average of 3.79, 3.69 and 3.88 floors were visited by each HCW each week in the 3 hospitals, with a standard deviation of 2.63, 1.74 and 2.08, respectively. Physicians reported the highest rate of direct patient contacts (> 20 patients/day) but the lowest rate of contacts with other HCWs; nurses had the most extended (> 20 min) periods of direct patient contact. “Other HCWs” had the most direct daily contact with all other HCWs. Physicians also reported significantly more locations visited per week than nurses, other HCW, or administrators; nurses visited the fewest. Public spaces such as the cafeteria had the most staff visits per week, but the least mean hours spent per visit. Inpatient settings had significantly more HCW interactions per week than outpatient settings.

Conclusions

HCW contact patterns and spatial movement demonstrate significant heterogeneity by occupation. Control strategies that address this diversity among health care workers may be more effective than “one-strategy-fits-all” HAI prevention and control programs.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3093-x) contains supplementary material, which is available to authorized users.

Infectious disease transmission: survey of contacts between hospital-based healthcare workers and working adults from the general population

BACKGROUND

Healthcare workers (HCWs) may be the inadvertent interface between the healthcare setting and the community for infectious diseases transmission.

AIM

To investigate HCWs' contacts during a work day and compare these against working adults from the general population.

METHODS

Prospective survey of contacts through 24 h self-reported diary in three public sector tertiary care hospitals and community-based working adults in Singapore. Participants were HCWs and working adults from the community.

FINDINGS

In all, 211 HCWs and 1028 working adults reported a total of 4066 and 9206 contacts. HCWs reported more work-related contacts than community-based working adults (median of 13 versus 4), and more contacts that were neither household nor work-related (1 versus 0) but fewer household contacts (2 versus 3). HCWs reported more work-related contacts involving physical contacts, and more new contacts particularly with short duration (≤15 min) compared to community-based working adults. Among different HCW types, doctors reported the highest whereas ward-based nurses reported the lowest total work-related contacts. Around half of ward-based and clinic-based nurses' contacts involved physical touch. Work-related contacts reported by clinic-based nurses, doctors, and assorted HCWs were shorter than in ward-based nurses, with a substantial number effectively occurring with new contacts. Institutional effects significant on univariate analyses were much reduced and non-significant after adjusting for confounding by HCW type.

CONCLUSION

HCWs' contacts differ substantially from those of community-based working adults. HCWs may thus be at higher risk of acquiring and spreading contact-transmissible and respiratory infections due to the nature of their work. Whereas total number of contacts was fairly similar between HCW types, the characteristics of their contacts differed substantively.

Contacts of healthcare workers, patients and visitors in general wards in Singapore

To characterize contacts in general wards, a prospective survey of healthcare workers (HCWs), patients and visitors was conducted using self-reported diary, direct observation and telephone interviews. Nurses, doctors and assorted HCWs reported a median of 14, 18 and 15 contact persons over one work shift, respectively. Within 1 h, we observed 3·5 episodes with 25·6 min of cumulative contact time for nurses, 2·9 episodes and 22·1 min for doctors and 5·0 episodes with 44·3 min for assorted-HCWs. In interactions with patients, nurses had multiple brief episodes of contact; doctors had fewer episodes and less cumulative contact time; assorted-HCWs had fewer contact episodes of longer durations (than for nurses and doctors). Assortative mixing occurred amongst HCWs: those of the same HCW type were the next most frequent class of contact after patients. Over 24-h, patients contacted 14 persons with 23 episodes and 314·5 min of contact time. Patient-to-patient contact episodes were rare, but a maximum of five were documented from one patient participant. 22·9% of visitors reported contact with patients other than the one they visited. Our study revealed differences in the characteristics of contacts among different HCW types and potential transmission routes from patients to others within the ward environment.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Social Contact Structures and Time Use Patterns in the Manicaland Province of Zimbabwe

BACKGROUND

Patterns of person-to-person contacts relevant for infectious diseases transmission are still poorly quantified in Sub-Saharan Africa (SSA), where socio-demographic structures and behavioral attitudes are expected to be different from those of more developed countries.

METHODS AND FINDINGS

We conducted a diary-based survey on daily contacts and time-use of individuals of different ages in one rural and one peri-urban site of Manicaland, Zimbabwe. A total of 2,490 diaries were collected and used to derive age-structured contact matrices, to analyze time spent by individuals in different settings, and to identify the key determinants of individuals' mixing patterns. Overall 10.8 contacts per person/day were reported, with a significant difference between the peri-urban and the rural site (11.6 versus 10.2). A strong age-assortativeness characterized contacts of school-aged children, whereas the high proportion of extended families and the young population age-structure led to a significant intergenerational mixing at older ages. Individuals spent on average 67% of daytime at home, 2% at work, and 9% at school. Active participation in school and work resulted the key drivers of the number of contacts and, similarly, household size, class size, and time spent at work influenced the number of home, school, and work contacts, respectively. We found that the heterogeneous nature of home contacts is critical for an epidemic transmission chain. In particular, our results suggest that, during the initial phase of an epidemic, about 50% of infections are expected to occur among individuals younger than 12 years and less than 20% among individuals older than 35 years.

CONCLUSIONS

With the current work, we have gathered data and information on the ways through which individuals in SSA interact, and on the factors that mostly facilitate this interaction. Monitoring these processes is critical to realistically predict the effects of interventions on infectious diseases dynamics.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes

BACKGROUND

Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability.

METHODS

We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability.

RESULTS

There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors.

CONCLUSION

Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes

Background

Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement – paper diaries vs. wearable proximity sensors – that were applied concurrently to the same population, and we measured acceptability.

Methods

We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability.

Results

There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants’ aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors.

Conclusion

Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1676-y) contains supplementary material, which is available to authorized users.

Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.

Tracking social contact networks with online respondent-driven detection: who recruits whom?

Background

Transmission of respiratory pathogens in a population depends on the contact network patterns of individuals. To accurately understand and explain epidemic behaviour information on contact networks is required, but only limited empirical data is available. Online respondent-driven detection can provide relevant epidemiological data on numbers of contact persons and dynamics of contacts between pairs of individuals. We aimed to analyse contact networks with respect to sociodemographic and geographical characteristics, vaccine-induced immunity and self-reported symptoms.

Methods

In 2014, volunteers from two large participatory surveillance panels in the Netherlands and Belgium were invited for a survey. Participants were asked to record numbers of contacts at different locations and self-reported influenza-like-illness symptoms, and to invite 4 individuals they had met face to face in the preceding 2 weeks. We calculated correlations between linked individuals to investigate mixing patterns.

Results

In total 1560 individuals completed the survey who reported in total 30591 contact persons; 488 recruiter-recruit pairs were analysed. Recruitment was assortative by age, education, household size, influenza vaccination status and sentiments, indicating that participants tended to recruit contact persons similar to themselves. We also found assortative recruitment by symptoms, reaffirming our objective of sampling contact persons whom a participant may infect or by whom a participant may get infected in case of an outbreak. Recruitment was random by sex and numbers of contact persons. Relationships between pairs were influenced by the spatial distribution of peer recruitment.

Conclusions

Although complex mechanisms influence online peer recruitment, the observed statistical relationships reflected the observed contact network patterns in the general population relevant for the transmission of respiratory pathogens. This provides useful and innovative input for predictive epidemic models relying on network information.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1250-z) contains supplementary material, which is available to authorized users.

The French Connection: The First Large Population-Based Contact Survey in France Relevant for the Spread of Infectious Diseases

Background

Empirical social contact patterns are essential to understand the spread of infectious diseases. To date, no such data existed for France. Although infectious diseases are frequently seasonal, the temporal variation of contact patterns has not been documented hitherto.

Methods

COMES-F is the first French large-scale population survey, carried out over 3 different periods (February-March, April, April-May) with some participants common to the first and the last period. Participants described their contacts for 2 consecutive days, and reported separately on professional contacts when typically over 20 per day.

Results

2033 participants reported 38 881 contacts (weighted median [first quartile-third quartile]: 8[5–14] per day), and 54 378 contacts with supplementary professional contacts (9[5–17]). Contrary to age, gender, household size, holidays, weekend and occupation, period of the year had little influence on the number of contacts or the mixing patterns. Contact patterns were highly assortative with age, irrespective of the location of the contact, and gender, with women having 8% more contacts than men. Although most contacts occurred at home and at school, the inclusion of professional contacts modified the structure of the mixing patterns. Holidays and weekends reduced dramatically the number of contacts, and as proxies for school closure, reduced R₀ by 33% and 28%, respectively. Thus, school closures could have an important impact on the spread of close contact infections in France.

Conclusions

Despite no clear evidence for temporal variation, trends suggest that more studies are needed. Age and gender were found important determinants of the mixing patterns. Gender differences in mixing patterns might help explain gender differences in the epidemiology of infectious diseases.

Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and The Netherlands using respondent-driven sampling

Understanding infection dynamics of respiratory diseases requires the identification and quantification of behavioural, social and environmental factors that permit the transmission of these infections between humans. Little empirical information is available about contact patterns within real-world social networks, let alone on differences in these contact networks between populations that differ considerably on a socio-cultural level. Here we compared contact network data that were collected in The Netherlands and Thailand using a similar online respondent-driven method. By asking participants to recruit contact persons we studied network links relevant for the transmission of respiratory infections. We studied correlations between recruiter and recruited contacts to investigate mixing patterns in the observed social network components. In both countries, mixing patterns were assortative by demographic variables and random by total numbers of contacts. However, in Thailand participants reported overall more contacts which resulted in higher effective contact rates. Our findings provide new insights on numbers of contacts and mixing patterns in two different populations. These data could be used to improve parameterisation of mathematical models used to design control strategies. Although the spread of infections through populations depends on more factors, found similarities suggest that spread may be similar in The Netherlands and Thailand.

Causal analysis of H1N1pdm09 influenza infection risk in a household cohort

Background

Obtaining a comprehensive quantitative figure of the determinants of influenza infection will help identify priority targets for future influenza mitigation interventions. We developed an original causal model integrating highly diverse factors and their dependencies, to identify the most critical determinants of pandemic influenza infection (H1N1pdm09) during the 2010–2011 influenza season.

Methods

We used data from 601 households (1450 participants) included in a dedicated cohort. Structural equations were used to model direct and indirect relationships between infection and risk perception, compliance with preventive behaviours, social contacts, indoor and outdoor environment, sociodemographic factors and pre-epidemic host susceptibility. Standardised estimates (β_std) were used to assess the strength of associations (ranging from −1 for a completely negative association to 1 for a completely positive association).

Results

Host susceptibility to H1N1pdm09 and compliance with preventive behaviours were the only two factors directly associated with the infection risk (β_std=0.31 and β_std=−0.21). Compliance with preventive behaviours was influenced by risk perception and preventive measures perception (β_std=0.14 and β_std=0.27). The number and duration of social contacts were not associated with H1N1pdm09 infection.

Conclusions

Our findings suggest that influenza vaccination in addition to public health communication campaigns focusing on personal preventive measures should be prioritised as potentially efficient interventions to mitigate influenza epidemics.