Using time-use data to parameterize models for the spread of close-contact infectious diseases

Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey

Background

Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across time, spatial scales, and demographic and social classifications, and what social behavior looks like at baseline in the absence of an ongoing pandemic.

Methods

We analyze spatiotemporal non-household contact patterns across 10.7 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of non-pandemic US contact data, we employ a regression approach to estimate baseline contact and address this gap.

Findings

Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of White individuals and contacts at work or social events change the most under increased national incidence.

Interpretation

We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing future disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory infectious disease transmission in the US.

Funding

Research reported in this publication was supported by the National Institutes of Health under award number R01GM123007 and R35GM153478 (SB).

Transmission matrices used in epidemiologic modelling

Mixing matrices are included in infectious disease models to reflect transmission opportunities between population strata. These matrices were originally constructed on the basis of theoretical considerations and most of the early work in this area originates from research on sexually transferred diseases in the 80s, in response to AIDS. Later work in the 90s populated these matrices on the basis of survey data gathered to capture transmission risks for respiratory diseases. We provide an overview of developments in the construction of matrices for capturing transmission opportunities in populations. Such transmission matrices are useful for epidemiologic modelling to capture within and between stratum transmission and can be informed from theoretical mixing assumptions, informed by empirical evidence gathered through investigation as well as generated on the basis of data. Links to summary measures and threshold conditions are also provided.

A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

Quantifying social contact patterns in Minnesota during stay-at-home social distancing order

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

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.

Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

The Epidemiological and Economic Impact of a Cell-Based Quadrivalent Influenza Vaccine in Adults in the US: A Dynamic Modeling Approach

Mutations of the H3N2 vaccine strain during the egg-based vaccine manufacturing process partly explain the suboptimal effectiveness of traditional seasonal influenza vaccines. Cell-based influenza vaccines improve antigenic match and vaccine effectiveness by avoiding such egg-adaptation. This study evaluated the public health and economic impact of a cell-based quadrivalent influenza vaccine (QIVc) in adults (18–64 years) compared to the standard egg-based quadrivalent influenza vaccine (QIVe) in the US. The impact of QIVc over QIVe in public health and cost outcomes was estimated using a dynamic age-structured SEIR transmission model, which accounted for four circulating influenza strains [A/H1N1pdm9, A/H3N2, B(Victoria), and B(Yamagata)] and was calibrated on the 2013–2018 influenza seasons. The robustness of the results was assessed in univariate and probabilistic sensitivity analyses. Switching from QIVe to QIVc in 18- to 64-year-olds may prevent 5.7 million symptomatic cases, 1.8 million outpatient visits, 50,000 hospitalizations, and 5453 deaths annually. The switch could save 128,000 Quality-Adjusted Life Years (QALYs) and US $ 845 M in direct costs, resulting in cost-savings in a three-year time horizon analysis. Probabilistic sensitivity analyses confirmed the robustness of the cost-saving result. The analysis shows that QIVc is expected to prevent hospitalizations and deaths, and result in substantial savings in healthcare costs.

Analyzing the demographic, spatial, and temporal factors influencing social contact patterns in U.S. and implications for infectious disease spread

Background

Diseases such as COVID-19 are spread through social contact. Reducing social contacts is required to stop disease spread in pandemics for which vaccines have not yet been developed. However, existing data on social contact patterns in the United States (U.S.) is limited.

Method

We use American Time Use Survey data from 2003–2018 to describe and quantify the age-pattern of disease-relevant social contacts. For within-household contacts, we construct age-structured contact duration matrices (who spends time with whom, by age). For both within-household and non-household contacts, we also estimate the mean number and duration of contact by location. We estimate and test for differences in the age-pattern of social contacts based on demographic, temporal, and spatial characteristics.

Results

The mean number and duration of social contacts vary by age. The biggest gender differences in the age-pattern of social contacts are at home and at work; the former appears to be driven by caretaking responsibilities.

Non-Hispanic Blacks have a shorter duration of contact and fewer social contacts than non-Hispanic Whites. This difference is largely driven by fewer and shorter contacts at home. Pre-pandemic, non-Hispanic Blacks have shorter durations of work contacts. Their jobs are more likely to require close physical proximity, so their contacts are riskier than those of non-Hispanic Whites. Hispanics have the highest number of household contacts and are also more likely to work in jobs requiring close physical proximity than non-Hispanic Whites.

With the exceptions of work and school contacts, the duration of social contact is higher on weekends than on weekdays. Seasonal differences in the total duration of social contacts are driven by school-aged respondents who have significantly shorter contacts during the summer months. Contact patterns did not differ by metro status. Age patterns of social contacts were similar across regions.

Conclusion

Social contact patterns differ by age, race and ethnicity, and gender. Other factors besides contact patterns may be driving seasonal variation in disease incidence if school-aged individuals are not an important source of transmission. Pre-pandemic, there were no spatial differences in social contacts, but this finding has likely changed during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06610-w.

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.

Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures

Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic reasoning bias that stems from intuitive misunderstandings of accumulation. Previous work in system dynamics shows that even highly educated individuals with training in science and math misunderstand basic concepts of accumulation. In the context of COVID-19, relying on the single cue of daily new cases can lead to relaxed attitudes about the risk of COVID-19 when daily new cases begin to decline. This situation is at the very point when risk is highest because even though daily new cases have declined, the active number of cases are highest because they have been accumulating over time. In an experiment with young adults from the USA and Canada (N = 551), we confirm that individuals fail to understand accumulation regarding COVID-19, have less concern regarding COVID-19, and decrease endorsement for public health measures as new cases decline but when active cases are at the highest point. Moreover, we experimentally manipulate different dynamic data visualizations and show that presenting data highlighting active cases and minimizing new cases led to increased concern and increased endorsement for COVID-19 health measures compared to a control condition highlighting daily cases. These results hold regardless of country, political affiliation, and individual differences in decision making. This study has implications for communicating the risks of contracting COVID-19 and future public health issues.

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals' voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.

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.

Quantifying population contact patterns in the United States during the COVID-19 pandemic

SARS-CoV-2 is transmitted primarily through close, person-to-person interactions. Physical distancing policies can control the spread of SARS-CoV-2 by reducing the amount of these interactions in a population. Here, we report results from four waves of contact surveys designed to quantify the impact of these policies during the COVID-19 pandemic in the United States. We surveyed 9,743 respondents between March 22 and September 26, 2020. We find that interpersonal contact has been dramatically reduced in the US, with an 82% (95%CI: 80%-83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. However, we find increases in contact rates over the subsequent waves. We also find that certain demographic groups, including people under 45 and males, have significantly higher contact rates than the rest of the population. Tracking these changes can provide rapid assessments of the impact of physical distancing policies and help to identify at-risk populations.

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.

Dynamic interplay between social distancing duration and intensity in reducing COVID-19 US hospitalizations: A "law of diminishing returns"

We uncover and highlight the importance of social distancing duration and intensity in lowering hospitalization demand-to-supply during the coronavirus disease 2019 (COVID-19) epidemic in the USA. We have developed an epidemic progression model involving the susceptible-exposed-infected-recovered dynamics, the age-stratified disease transmissibility, and the possible large-scale undocumented (i.e., asymptomatic and/or untested) transmission of COVID-19 taking place in the USA. Our analysis utilizes COVID-19 observational data in the USA between March 19 and 28, corresponding to the early stage of the epidemic when the impacts of social distancing on disease progression were yet to manifest. Calibrating our model using epidemiological data from this time period enabled us to unbiasedly address the question "How long and with what intensity does the USA need to implement social distancing intervention during the COVID-19 pandemic?" For a short (i.e., up to two weeks) duration, we find a near-linear decrease in hospital beds demand with increasing intensity (φ) of social distancing. For a duration longer than two weeks, our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. Long-term implementation of strict social distancing with φ>50% could lead to the emergence of a second wave of infections due to a large residual susceptible population which highlights the need for contact tracing and isolation before re-opening of the economy. Finally, we investigate the scenario of intermittent social distancing and find an optimal social-to-no-distancing duration ratio of 5:1 corresponding to a sustainable reduction in medical demands.

Close contact behavior in indoor environment and transmission of respiratory infection

Close contact was first identified as the primary route of transmission for most respiratory infections in the early 20th century. In this review, we synthesize the existing understanding of the mechanisms of close contact transmission. We focus on two issues: the mechanism of transmission in close contact, namely the transmission of the expired particles between two people, and the physical parameters of close contact that affect the exposure of particles from one individual to another, or how the nature of close contact plays a role in transmission. We propose the existence of three sub-routes of transmission: short-range airborne, large droplets, and immediate body-surface contact. We also distinguish a "body contact," which is defined with an interpersonal distance of zero, from a close contact. We demonstrate herein that the short-range airborne sub-route may be most common. The timescales over which data should be collected to assess the transmission risk during close contact events are much shorter than those required for the distant airborne or fomite routes. The current paucity of high-resolution data over short distances and timescales makes it very difficult to assess the risk of infection in these circumstances.

Evaluating vaccination strategies for tuberculosis in endemic and non-endemic settings

According to the World Health Organization, tuberculosis (TB) is the leading cause of death from infectious disease worldwide (WHO, 2017). While there is no effective vaccine against adult pulmonary TB, more than a dozen vaccine candidates are in the clinical trial pipeline. These include both pre-exposure vaccines to prevent initial infections and post-exposure vaccines to prevent reactivation of latent disease. Many epidemiological models have been used to study TB, but most have not included a continuous age structure and the possibility of both pre- and post-exposure vaccination. Incorporating age-dependent death rates, disease properties, and social contact data allows for more realistic modeling of disease spread. We propose a continuous age-structured model for the epidemiology of tuberculosis with pre- and post-exposure vaccination. We use uncertainty and sensitivity analysis to make predictions about the efficacy of different vaccination strategies in a non-endemic setting (United States) and an endemic setting (Cambodia). In particular, we determine optimal age groups to target for pre-exposure and post-exposure vaccination in both settings. We find that the optimal age groups tend to be younger for Cambodia than for the US, and that post-exposure vaccination has a significantly larger effect than pre-exposure vaccination in the US.

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.

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.

Close encounters between infants and household members measured through wearable proximity sensors

Describing and understanding close proximity interactions between infant and family members can provide key information on transmission opportunities of respiratory infections within households. Among respiratory infections, pertussis represents a public health priority. Pertussis infection can be particularly harmful to young, unvaccinated infants and for these patients, family members represent the main sources of transmission. Here, we report on the use of wearable proximity sensors based on RFID technology to measure face-to-face proximity between family members within 16 households with infants younger than 6 months for 2-5 consecutive days of data collection. The sensors were deployed over the course of approximately 1 year, in the context of a national research project aimed at the improvement of infant pertussis prevention strategies. We investigated differences in close-range interactions between family members and we assessed whether demographic variables or feeding practices affect contact patterns between parents and infants. A total of 5,958 contact events were recorded between 55 individuals: 16 infants, 4 siblings, 31 parents and 4 grandparents. The aggregated contact networks, obtained for each household, showed a heterogeneous distribution of the cumulative time spent in proximity with the infant by family members. Contact matrices defined by age and by family role showed that most of the contacts occurred between the infant and other family members (70%), while 30% of contacts was among family members (infants excluded). Many contacts were observed between infants and adults, in particular between infant and mother, followed by father, siblings and grandparents. A larger number of contacts and longer contact durations between infant and other family members were observed in families adopting exclusive breastfeeding, compared to families in which the infant receives artificial or mixed feeding. Our results demonstrate how a high-resolution measurement of contact matrices within infants' households is feasible using wearable proximity sensing devices. Moreover, our findings suggest the mother is responsible for the large majority of the infant's contact pattern, thus being the main potential source of infection for a transmissible disease. As the contribution to the infants' contact pattern by other family members is very variable, vaccination against pertussis during pregnancy is probably the best strategy to protect young, unvaccinated infants.

Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual's activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

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.

Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission

This study aims to develop a typology of generic meeting places based on social contact and mixing of relevance for infectious disease transmission. Data were collected by means of a contact diary survey conducted on a representative sample of the Swedish population. The typology is derived from a cluster analysis accounting for four dimensions associated with transmission risk: visit propensity and its characteristics in terms of duration, number of other persons present and likelihood of physical contact. In the analysis, we also study demographic, socio-economic and geographical differences in the propensity of visiting meeting places. The typology identifies the family venue, the fixed activity site, the family vehicle, the trading plaza and the social network hub as generic meeting places. The meeting place typology represents a spatially explicit account of social contact and mixing relevant to infectious disease modelling, where the social context of the outbreak can be highlighted in light of the actual infectious disease.

Modelling the transmission and control strategies of varicella among school children in Shenzhen, China

Objectives

Varicella (chickenpox) is a highly transmissible childhood disease. Between 2010 and 2015, it displayed two epidemic waves annually among school populations in Shenzhen, China. However, their transmission dynamics remain unclear and there is no school-based vaccination programme in Shenzhen to-date. In this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention) scenario.

Methods

Data on varicella reported cases were downloaded from the Infectious Disease Reporting Information Management System. We obtained the population size, age structure of children aged 15 or under, the class and school distribution from Shenzhen Education Bureau. We developed an Agent-Based Susceptible-Exposed-Infectious-Recovered (ABM-SEIR) Model that considered within-class, class-to-class and out-of-school transmission modes. The intervention scenario was that school-wide vaccination intervention occurred when an outbreak threshold was reached within a school. We varied this threshold level from five to ten cases. We compared the reduction of disease outbreak size and estimated the key epidemiological parameters under the intervention strategy.

Results

Our ABM-SEIR model provided a good model fit to the two annual varicella epidemic waves from 2013 to 2015. The transmission dynamics displayed strong seasonality. Our results suggested that a school-based vaccination strategy could effectively prevent large outbreaks at different thresholds.

Conclusions

There was a considerable increase in reported varicella cases from 2013 to 2015 in Shenzhen. Our modelling study provided important theoretical support for disease control decision making during school outbreaks and the development of a school-based vaccination programme.

Varicella seroepidemiology in United States air force recruits: A retrospective cohort study comparing immunogenicity of varicella vaccination and natural infection

BACKGROUND/OBJECTIVES

Infection with varicella zoster virus (VZV) produces lifelong immunity, but duration of post-vaccination immunity has not been established. The purpose of this study is to determine if a difference exists in the long-term seropositivity of anti-VZV antibodies in a cohort of young adults who were vaccinated against varicella as compared to a similar cohort with a history of chickenpox disease, and to determine which variables best predict waning seropositivity following varicella vaccination.

METHODS

This retrospective cohort study captures immunization and serology data from approximately 10,000 recruits who entered basic military training between January 1, 2008, and December 31, 2015, and who have childhood immunization records in the Air Force Aeromedical Services Information Management System. Varicella vaccine immunogenicity was determined relative to the immunogenicity of chickenpox disease, as measured by multiplex flow immunoassay. Among vaccine recipients, waning seroimmunity was modeled and adjusted for several important covariates.

RESULTS

Basic military trainees who received varicella vaccine in childhood were 24% less likely to be seropositive to VZV than trainees who were exempt from vaccine due to a history of chickenpox disease. There was no significant difference in seropositivity between male and female trainees. The odds of a vaccinated trainee being seropositive to VZV decreased by 8% with each year elapsed since vaccination. Seroprevalence declined below estimated herd immunity thresholds in vaccinated trainees born after 1994, and in the cohort as a whole for trainees born after 1995.

CONCLUSION

Despite prior vaccination, seroimmunity in a large cohort of young adults unexposed to wild-type VZV failed to meet the estimated threshold for herd immunity. If vaccination in accordance with the current US VZV vaccination schedule is inadequate to maintain herd immunity, young adults not previously exposed to wild-type VZV may be at increased risk for varicella outbreaks.

Estimating contact patterns relevant to the spread of infectious diseases in Russia

Understanding human mixing patterns is the key to provide public health decision makers with model-based evaluation of strategies for the control of infectious diseases. Here we conducted a population-based survey in Tomsk, Russia, asking participants to record all their contacts in physical person during the day. We estimated 9.8 contacts per person per day on average, 15.2 when including additional estimated professional contacts. We found that contacts were highly assortative by age, especially for school-age individuals, and the number of contacts negatively correlated with the age of the participant. The network of contacts was quite clustered, with the majority of contacts (about 72%) occurring between family members, students of the same school/university, and work colleagues. School represents the location where the largest number of contacts was recorded - students contacted about 7 individuals per day at school. Our modeling analysis based on the recorded contact patterns supports the importance of modeling age-mixing patterns - we show that, in the case of an epidemic caused by a novel influenza virus, school-age individuals would be the most affected age group, followed by adults aged 35-44 years. In conclusion, this study reveals an age-mixing pattern in general agreement with that estimated for European countries, although with several quantitative differences. The observed differences can be attributable to sociodemographic and cultural differences between countries. The age- and setting-specific contact matrices provided in this study could be instrumental for the design of control measures for airborne infections, specifically targeted on the characteristics of the Russian population.

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.

Potential impact of a ventilation intervention for influenza in the context of a dense indoor contact network in Hong Kong

Emerging diseases may spread rapidly through dense and large urban contact networks. We constructed a simple but novel dual-contact network model to account for both airborne contact and close contact of individuals in the densely populated city of Hong Kong. The model was then integrated with an existing epidemiological susceptible-exposed-infectious-recovered (SEIR) model, and we used a revised Wells-Riley model to estimate infection risks by the airborne route and an exponential dose-response model for risks by the contact and droplet routes. A potential outbreak of partially airborne influenza was examined, assuming different proportions of transmission through the airborne route. Our results show that building ventilation can have significant effects in airborne transmission-dominated conditions. Moreover, even when the airborne route only contributes 20% to the total infection risk, increasing the ventilation rate has a strong influence on transmission dynamics, and it also can achieve control effects similar to those of wearing masks for patients, isolation and vaccination.

Cost-effectiveness of seasonal quadrivalent versus trivalent influenza vaccination in the United States: A dynamic transmission modeling approach

Trivalent inactivated influenza vaccines (IIV3s) protect against 2 A strains and one B lineage; quadrivalent versions (IIV4s) protect against an additional B lineage. The objective was to assess projected health and economic outcomes associated with IIV4 versus IIV3 for preventing seasonal influenza in the US. A cost-effectiveness model was developed to interact with a dynamic transmission model. The transmission model tracked vaccination, influenza cases, infection-spreading interactions, and recovery over 10 y (2012–2022). The cost-effectiveness model estimated influenza-related complications, direct and indirect costs (2013–2014 US$), health outcomes, and cost-effectiveness. Inputs were taken from published/public sources or estimated using regression or calibration. Outcomes were discounted at 3% per year. Scenario analyses tested the reliability of the results. Seasonal vaccination with IIV4 versus IIV3 is predicted to reduce annual influenza cases by 1,973,849 (discounted; 2,325,644 undiscounted), resulting in 12–13% fewer cases and influenza-related complications and deaths. These reductions are predicted to translate into 18,485 more quality-adjusted life years (QALYs) accrued annually for IIV4 versus IIV3. Increased vaccine-related costs ($599 million; 5.7%) are predicted to be more than offset by reduced influenza treatment costs ($699 million; 12.2%), resulting in direct medical cost saving annually ($100 million; 0.6%). Including indirect costs, savings with IIV4 are predicted to be $7.1 billion (5.6%). Scenario analyses predict IIV4 to be cost-saving in all scenarios tested apart from low infectivity, where IIV4 is predicted to be cost-effective. In summary, seasonal influenza vaccination in the US with IIV4 versus IIV3 is predicted to improve health outcomes and reduce costs.

Evaluating targeted interventions via meta-population models with multi-level mixing

Among the several means by which heterogeneity can be modeled, Levins' (1969) meta-population approach preserves the most analytical tractability, a virtue to the extent that generality is desirable. When model populations are stratified, contacts among their respective sub-populations must be described. Using a simple meta-population model, Feng et al. (2015) showed that mixing among sub-populations, as well as heterogeneity in characteristics affecting sub-population reproduction numbers, must be considered when evaluating public health interventions to prevent or control infectious disease outbreaks. They employed the convex combination of preferential within- and proportional among-group contacts first described by Nold (1980) and subsequently generalized by Jacquez et al. (1988). As the utility of meta-population modeling depends on more realistic mixing functions, the authors added preferential contacts between parents and children and among co-workers (Glasser et al., 2012). Here they further generalize this function by including preferential contacts between grandparents and grandchildren, but omit workplace contacts. They also describe a general multi-level mixing scheme, provide three two-level examples, and apply two of them. In their first application, the authors describe age- and gender-specific patterns in face-to-face conversations (Mossong et al., 2008), proxies for contacts by which respiratory pathogens might be transmitted, that are consistent with everyday experience. This suggests that meta-population models with inter-generational mixing could be employed to evaluate prolonged school-closures, a proposed pandemic mitigation measure that could expose grandparents, and other elderly surrogate caregivers for working parents, to infectious children. In their second application, the authors use a meta-population SEIR model stratified by 7 age groups and 50 states plus the District of Columbia, to compare actual with optimal vaccination during the 2009-2010 influenza pandemic in the United States. They also show that vaccination efforts could have been adjusted month-to-month during the fall of 2009 to ensure maximum impact. Such applications inspire confidence in the reliability of meta-population modeling in support of public health policymaking.

Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases. We simulate 10 years of epidemics (2003–2012) based on mixing patterns derived from individual time-use data to show that the mixing patterns in 2009 yield the lowest number of total infections relative to if the epidemic had occurred in any of the other nine years. The World Health Organization and other public health bodies have emphasized an important role for ‘distancing’ or non-pharmaceutical interventions. Our empirical results suggest that neglect for voluntary avoidance behaviour in epidemic models may overestimate the public health benefits of public social distancing policies.

Cost-Effectiveness of Pertussis Vaccination During Pregnancy in the United States

Vaccination against pertussis has reduced the disease burden dramatically, but the most severe cases and almost all fatalities occur in infants too young to be vaccinated. Recent epidemiologic evidence suggests that targeted vaccination of mothers during pregnancy can reduce pertussis incidence in their infants. To evaluate the cost-effectiveness of antepartum maternal vaccination in the United States, we created an age-stratified transmission model, incorporating empirical data on US contact patterns and explicitly modeling parent-infant exposure. Antepartum maternal vaccination incurs costs of $114,000 (95% prediction interval: 82,000, 183,000) per quality-adjusted life-year, in comparison with the strategy of no adult vaccination, and is cost-effective in the United States according to World Health Organization criteria. By contrast, vaccinating a second parent is not cost-effective, and vaccination of either parent postpartum is strongly dominated by antepartum maternal vaccination. Nonetheless, postpartum vaccination of mothers who were not vaccinated antepartum improves upon the current recommendation of untargeted adult vaccination. Additionally, the temporary direct protection of the infant due to maternal antibody transfer has efficacy for infants comparable to that conferred to toddlers by the full primary vaccination series. Efficient protection against pertussis for infants begins before birth. We highly recommend antepartum vaccination for as many US mothers as possible.

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.

Retrospective public health impact of a quadrivalent influenza vaccine in the United States

INTRODUCTION

Vaccination is an effective preventive strategy against influenza. However, current trivalent influenza vaccines (TIVs) contain only one of the two influenza B lineages that circulate each year. Vaccine mismatches are frequent because predicting which one will predominate is difficult. Recently licensed quadrivalent influenza vaccines (QIVs) containing the two B lineages should address this issue. Our study estimates their impact by assessing what would have been the US public health benefit of routinely vaccinating with QIV in 2000-2013.

METHODS

We developed a dynamic compartmental model that accounts for interactions between influenza B lineages (natural or vaccine-induced) and simulates the multiyear influenza dynamics for 2000-2013. Age-structured population dynamics, vaccine efficacy (VE) per strain, and weekly ramp-up of vaccination coverage are modeled. Sensitivity analyses were performed on VE, duration of immunity, and levels of vaccine-induced cross-protection between B lineages.

RESULTS

Assuming a cross-protection of 70% of the VE of the matched vaccine, the model predicts 16% more B lineage cases prevented by QIV. Elderly (≥65 years) and young seniors (50-64 years) benefit most from QIV, with 21% and 18% reductions in B lineage cases. Reducing cross-protection to 50%, 30%, and 0% of the VE of the matched vaccine improves the relative benefit of QIV to 25%, 30%, and 34% less B lineage cases.

CONCLUSION

Using a dynamic retrospective framework with real-life vaccine mismatch, our analysis shows that QIV routine vaccination in the United States has the potential to substantially reduce the number of influenza infections, even with relatively high estimates of TIV-induced cross-protection.

Cost-effectiveness of next-generation vaccines: The case of pertussis

Despite steady vaccination coverage rates, pertussis incidence in the United States has continued to rise. This public health challenge has motivated calls for the development of a new vaccine with greater efficacy and duration of protection. Any next-generation vaccine would likely come at a higher cost, and must provide sufficient health benefits beyond those provided by the current vaccine in order to be deemed cost-effective. Using an age-structured transmission model of pertussis, we quantified the health and economic benefits of a next-generation vaccine that would enhance either the efficacy or duration of protection of the childhood series, the duration of the adult booster, or a combination. We developed a metric, the maximum cost-effective price increase (MCPI), to compare the potential value of such improvements. The MCPI estimates the per-dose price increase that would maintain the cost-effectiveness of pertussis vaccination. We evaluated the MCPI across a range of potential single and combined improvements to the pertussis vaccine. As an upper bound, we found that a next-generation vaccine which could achieve perfect efficacy for the childhood series would permit an MCPI of $18 per dose (95% CI: $12-$31). Pertussis vaccine improvements that extend the duration of protection to an average of 75 years would allow for an MCPI of $22 per dose for the childhood series (CI: $10-$33) or $12 for the adult booster (CI: $4-$18). Despite the short duration of the adult booster, improvements to the childhood series could be more valuable than improvements to the adult booster. Combining improvements in both efficacy and duration, a childhood series with perfect efficacy and average duration of 75 years would permit an MCPI of $39 per dose, the highest of any scenario evaluated. Our results highlight the utility of the MCPI metric in evaluating potential vaccines or other interventions when prices are unknown.

Influenza in workplaces: transmission, workers' adherence to sick leave advice and European sick leave recommendations

Background: Knowledge about influenza transmission in the workplace and whether staying home from work when experiencing influenza-like illness can reduce the spread of influenza is crucial for the design of efficient public health initiatives. Aim: This review synthesizes current literature on sickness presenteeism and influenza transmission in the workplace and provides an overview of sick leave recommendations in Europe for influenza. Methods: A search was performed on Medline, Embase, PsychINFO, Cinahl, Web of Science, Scopus and SweMed to identify studies related to workplace contacts, -transmission, -interventions and compliance with recommendations to take sick leave. A web-based survey on national recommendations and policies for sick leave during influenza was issued to 31 European countries. Results: Twenty-two articles (9 surveys; 13 modelling articles) were eligible for this review. Results from social mixing studies suggest that 20–25% of weekly contacts are made in the workplace, while modelling studies suggest that on average 16% (range 9–33%) of influenza transmission occurs in the workplace. The effectiveness of interventions to reduce workplace presenteeism is largely unknown. Finally, estimates from studies reporting expected compliance with sick leave recommendations ranged from 71 to 95%. Overall, 18 countries participated in the survey of which nine (50%) had issued recommendations encouraging sick employees to stay at home during the 2009 A(H1N1) pandemic, while only one country had official recommendations for seasonal influenza. Conclusions: During the 2009 A(H1N1) pandemic, many European countries recommended ill employees to take sick leave. Further research is warranted to quantify the effect of reduced presenteeism during influenza illness.

Cost-effectiveness evaluation of quadrivalent influenza vaccines for seasonal influenza prevention: a dynamic modeling study of Canada and the United Kingdom

Background

The adoption of quadrivalent influenza vaccine (QIV) to replace trivalent influenza vaccine (TIV) in immunization programs is growing worldwide, thus helping to address the problem of influenza B lineage mismatch. However, the price per dose of QIV is higher than that of TIV. In such circumstances, cost-effectiveness analyses provide important and relevant information to inform national health recommendations and implementation decisions. This analysis assessed potential vaccine impacts and cost-effectiveness of a country-wide switch from TIV to QIV, in Canada and the UK, from a third-party payer perspective.

Methods

An age-stratified, dynamic four-strain transmission model which incorporates strain interaction, transmission-rate seasonality and age-specific mixing in the population was used. Model input data were obtained from published literature and online databases. In Canada, we evaluated a switch from TIV to QIV in the entire population. For the UK, we considered two strategies: Children aged 2–17 years who receive the live-attenuated influenza vaccine (LAIV) switch to the quadrivalent formulation (QLAIV), while individuals aged > 18 years switch from TIV to QIV. Two different vaccination uptake scenarios in children (UK1 and UK2, which differ in the vaccine uptake level) were considered. Health and cost outcomes for both vaccination strategies, and the cost-effectiveness of switching from TIV/LAIV to QIV/QLAIV, were estimated from the payer perspective. For Canada and the UK, cost and outcomes were discounted using 5 % and 3.5 % per year, respectively.

Results

Overall, in an average influenza season, our model predicts that a nationwide switch from TIV to QIV would prevent 4.6 % influenza cases, 4.9 % general practitioner (GP) visits, 5.7 % each of emergency room (ER) visits and hospitalizations, and 6.8 % deaths in Canada. In the UK (UK1/UK2), implementing QIV would prevent 1.4 %/1.8 % of influenza cases, 1.6 %/2.0 % each of GP and ER visits, 1.5 %/1.9 % of hospitalizations and 4.3 %/4.9 % of deaths. Discounted incremental cost-utility ratios of $7,961 and £7,989/£7,234 per quality-adjusted life-year (QALY) gained are estimated for Canada and the UK (UK1/UK2), both of which are well within their respective cost-effectiveness threshold values.

Conclusions

Switching from TIV to QIV is expected to be a cost-effective strategy to further reduce the burden of influenza in both countries.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1193-4) contains supplementary material, which is available to authorized users.

Social contacts, vaccination decisions and influenza in Japan

Background

Contact patterns and vaccination decisions are fundamental to transmission dynamics of infectious diseases. We report on age-specific contact patterns in Japan and their effect on influenza vaccination behaviour.

Methods

Japanese adults (N=3146) were surveyed in Spring 2011 to assess the number of their social contacts within a 24 h period, defined as face-to-face conversations within 2 m, and gain insight into their influenza-related behaviour. We analysed the duration and location of contacts according to age. Additionally, we analysed the probability of vaccination and influenza infection in relation to the number of contacts controlling for individual's characteristics.

Results

The mean and median reported numbers of daily contacts were 15.3 and 12.0, respectively. School-aged children and young adults reported the greatest number of daily contacts, and individuals had the most contacts with those in the same age group. The age-specific contact patterns were different between men and women, and differed between weekdays and weekends. Children had fewer contacts between the same age groups during weekends than during weekdays, due to reduced contacts at school. The probability of vaccination increased with the number of contacts, controlling for age and household size. Influenza infection among unvaccinated individuals was higher than for those vaccinated, and increased with the number of contacts.

Conclusions

Contact patterns in Japan are age and gender specific. These contact patterns, as well as their interplay with vaccination decisions and infection risks, can help inform the parameterisation of mathematical models of disease transmission and the design of public health policies, to control disease transmission.

An elaboration of theory about preventing outbreaks in homogeneous populations to include heterogeneity or preferential mixing

The goal of many vaccination programs is to attain the population immunity above which pathogens introduced by infectious people (e.g., travelers from endemic areas) will not cause outbreaks. Using a simple meta-population model, we demonstrate that, if sub-populations either differ in characteristics affecting their basic reproduction numbers or if their members mix preferentially, weighted average sub-population immunities cannot be compared with the proportionally-mixing homogeneous population-immunity threshold, as public health practitioners are wont to do. Then we review the effect of heterogeneity in average per capita contact rates on the basic meta-population reproduction number. To the extent that population density affects contacts, for example, rates might differ in urban and rural sub-populations. Other differences among sub-populations in characteristics affecting their basic reproduction numbers would contribute similarly. In agreement with more recent results, we show that heterogeneous preferential mixing among sub-populations increases the basic meta-population reproduction number more than homogeneous preferential mixing does. Next we refine earlier results on the effects of heterogeneity in sub-population immunities and preferential mixing on the effective meta-population reproduction number. Finally, we propose the vector of partial derivatives of this reproduction number with respect to the sub-population immunities as a fundamentally new tool for targeting vaccination efforts.

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.

Mitigation of infectious disease at school: targeted class closure vs school closure

Background

School environments are thought to play an important role in the community spread of infectious diseases such as influenza because of the high mixing rates of school children. The closure of schools has therefore been proposed as an efficient mitigation strategy. Such measures come however with high associated social and economic costs, making alternative, less disruptive interventions highly desirable. The recent availability of high-resolution contact network data from school environments provides an opportunity to design models of micro-interventions and compare the outcomes of alternative mitigation measures.

Methods and results

We model mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the specific case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics, and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time – equal to the sum of the average infectious and latent durations – whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and significantly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school.

Conclusions

Our model of targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, shows that these strategies might be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-014-0695-9) contains supplementary material, which is available to authorized users.

Merging economics and epidemiology to improve the prediction and management of infectious disease

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as "economic epidemiology" or "epidemiological economics," the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

The role of different social contexts in shaping influenza transmission during the 2009 pandemic

Evaluating the relative importance of different social contexts in which infection transmission occurs is critical for identifying optimal intervention strategies. Nonetheless, an overall picture of influenza transmission in different social contexts has yet to emerge. Here we provide estimates of the fraction of infections generated in different social contexts during the 2009 H1N1 pandemic in Italy by making use of a highly detailed individual-based model accounting for time use data and parametrized on the basis of observed age-specific seroprevalence. We found that 41.6% (95%CI: 39-43.7%) of infections occurred in households, 26.7% (95%CI: 21-33.2) in schools, 3.3% (95%CI: 1.7-5%) in workplaces, and 28.4% (95%CI: 24.6-31.9%) in the general community. The above estimates strongly depend on the lower susceptibility to infection of individuals 19+ years old compared to younger ones, estimated to be 0.2 (95%CI 0.12-0.28). We also found that school closure over the weekends contributed to decrease the effective reproduction number of about 8% and significantly affected the pattern of transmission. These results highlight the pivotal role played by schools in the transmission of the 2009 H1N1 influenza. They may be relevant in the evaluation of intervention options and, hence, for informing policy decisions.

Contact patterns among high school students

Face-to-face contacts between individuals contribute to shape social networks and play an important role in determining how infectious diseases can spread within a population. It is thus important to obtain accurate and reliable descriptions of human contact patterns occurring in various day-to-day life contexts. Recent technological advances and the development of wearable sensors able to sense proximity patterns have made it possible to gather data giving access to time-varying contact networks of individuals in specific environments. Here we present and analyze two such data sets describing with high temporal resolution the contact patterns of students in a high school. We define contact matrices describing the contact patterns between students of different classes and show the importance of the class structure. We take advantage of the fact that the two data sets were collected in the same setting during several days in two successive years to perform a longitudinal analysis on two very different timescales. We show the high stability of the contact patterns across days and across years: the statistical distributions of numbers and durations of contacts are the same in different periods, and we observe a very high similarity of the contact matrices measured in different days or different years. The rate of change of the contacts of each individual from one day to the next is also similar in different years. We discuss the interest of the present analysis and data sets for various fields, including in social sciences in order to better understand and model human behavior and interactions in different contexts, and in epidemiology in order to inform models describing the spread of infectious diseases and design targeted containment strategies.