Estimating infectious disease parameters from data on social contacts and serological status

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.

On the use of historical estimates

The use of historical, i.e., already existing, estimates in current studies is common in a wide variety of application areas. Nevertheless, despite their routine use, the uncertainty associated with historical estimates is rarely properly accounted for in the analysis. In this communication, we review common practices and then provide a mathematical formulation and a principled frequentist methodology for addressing the problem of drawing inferences in the presence of historical estimates. Three distinct variants are investigated in detail; the corresponding limiting distributions are found and compared. The design of future studies, given historical data, is also explored and relations with a variety of other well-studied statistical problems discussed.

Differential impact of physical distancing strategies on social contacts relevant for the spread of SARS-CoV-2: evidence from a cross-national online survey, March-April 2020

OBJECTIVES

We investigate changes in social contact patterns following the gradual introduction of non-pharmaceutical interventions and their implications for infection transmission in the early phase of the pandemic.

DESIGN, SETTING AND PARTICIPANTS

We conducted an online survey based on targeted Facebook advertising campaigns across eight countries (Belgium, France, Germany, Italy, the Netherlands, Spain, UK and USA), achieving a sample of 51 233 questionnaires in the period 13 March-12 April 2020. Poststratification weights based on census information were produced to correct for selection bias.

OUTCOME MEASURES

Participants provided data on social contact numbers, adoption of protective behaviours and perceived level of threat. These data were combined to derive a weekly index of infection transmission, the net reproduction number [Formula: see text] .

RESULTS

Evidence from the USA and UK showed that the number of daily contacts mainly decreased after governments issued the first physical distancing guidelines. In mid-April, daily social contact numbers had decreased between 61% in Germany and 87% in Italy with respect to pre-COVID-19 levels, mostly due to a contraction in contacts outside the home. Such reductions, which were uniform across age groups, were compatible with an [Formula: see text] equal or smaller than one in all countries, except Germany. This indicates lower levels of infection transmission, especially in a period of gradual increase in the adoption rate of the face mask outside the home.

CONCLUSIONS

We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week and other demographic factors, which could be leveraged by the scientific community for developing more realistic epidemic models of COVID-19.

Estimating the basic reproductive number of varicella in South Korea incorporating social contact patterns and seroprevalence

Varicella, which is caused by the varicella zoster virus (VZV), is a common infectious disease affecting children. Varicella vaccines have been used for decades; however, vaccination policies vary across countries because of differences in VZV epidemiology. The basic reproductive number R0 _a transmissibility measure parameter, also differs from country to country. In this study R0 for varicella was estimated in South Korea using the contact rate matrix derived from averaged POLYMOD contact data, the Korean population, and proportionality factor fitted to the Korean VZV seroprevalence R0 for varicella in South Korea was estimated to be 5.67 (95% CI: 5.33, 6.33). Therefore, to reach the herd immunity threshold, the critical vaccine coverage should be greater than 82.4% with a perfect vaccine, or the primary vaccine failure proportion should be less than 17.6% with 100% coverage. Because of the relatively low seroconversion rate and rapidly waning immunity after one-dose vaccination in South Korea, the herd immunity threshold is difficult to attain with only a one-dose vaccine. Two doses of vaccination may be necessary to effectively interrupt varicella transmission and maintain herd immunity in South Korea. The study results can help guide the decision-making on an effective varicella vaccination policy in South Korea.

Close contact infection dynamics over time: insights from a second large-scale social contact survey in Flanders, Belgium, in 2010-2011

Background

In 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period.

Methods

Different data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a generalized additive models for location, scale and shape (GAMLSS) model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R₀) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in sex, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data.

Results

All estimated contact patterns featured strong homophily in age and sex, especially for small children and adolescents. A 30% (95% BCI [17%; 37%]) and 29% (95% BCI [14%; 40%]) reduction in R₀ was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study.

Conclusions

The second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12879-021-05949-4).

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

SOCRATES: an online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

Objective

Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.

Results

We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{0}$$\end{document}0. We incorporated location-specific physical distancing measures (e.g. school closure or at work) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that physical distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

SOCRATES: an online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

OBJECTIVE

Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.

RESULTS

We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R[Formula: see text]. We incorporated location-specific physical distancing measures (e.g. school closure or at work) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that physical distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

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

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

Household members do not contact each other at random: implications for infectious disease modelling

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling

Background

Our work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey.

Methods

Statistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2001–2003 where testing was done, amongst others, for the presence of Immunoglobulin G antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium.

Results

The optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections.

Conclusions

When estimating epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0692-1) contains supplementary material, which is available to authorized users.

Models for optimally controlling varicella and herpes zoster by varicella vaccination: a comparative study

The introduction of mass vaccination against Varicella-Zoster-Virus (VZV) is being delayed in many European countries mainly because of the "fear" of a subsequent boom in natural herpes zoster (HZ) incidence in the first decades after the initiation of vaccination, caused by the expected decline in the protective effect of natural immunity boosting due to reduced virus circulation. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal disease intervention strategies. In this paper, we describe how a reduced 'toy' model can extract the essentials of the dynamics of the VZV transmission and reactivation in case of the study of optimal paths of varicella immunization programs. Results obtained using different optimization approaches are compared with the ones of a more realistic age-structured model. The reduced model shows some unreliable predictions in regards of model time scales about herpes zoster dynamic; nevertheless, it is able to reproduce the main qualitative dynamic of the more realistic model to the different optimization problems, while requiring a minimal number of parameters to be identified. Graphical abstract ᅟ.

Correlated gamma frailty models for bivariate survival time data

Frailty models have been developed to quantify both heterogeneity as well as association in multivariate time-to-event data. In recent years, numerous shared and correlated frailty models have been proposed in the survival literature allowing for different association structures and frailty distributions. A bivariate correlated gamma frailty model with an additive decomposition of the frailty variables into a sum of independent gamma components was introduced before. Although this model has a very convenient closed-form representation for the bivariate survival function, the correlation among event- or subject-specific frailties is bounded above which becomes a severe limitation when the values of the two frailty variances differ substantially. In this article, we review existing correlated gamma frailty models and propose novel ones based on bivariate gamma frailty distributions. Such models are found to be useful for the analysis of bivariate survival time data regardless of the censoring type involved. The frailty methodology was applied to right-censored and left-truncated Danish twins mortality data and serological survey current status data on varicella zoster virus and parvovirus B19 infections in Belgium. From our analyses, it has been shown that fitting more flexible correlated gamma frailty models in terms of the imposed association and correlation structure outperforms existing frailty models including the one with an additive decomposition.

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

BACKGROUND

School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified.

METHODS

We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.

RESULTS

Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic.

CONCLUSION

Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

Infectious reactivation of cytomegalovirus explaining age- and sex-specific patterns of seroprevalence

Human cytomegalovirus (CMV) is a herpes virus with poorly understood transmission dynamics. Person-to-person transmission is thought to occur primarily through transfer of saliva or urine, but no quantitative estimates are available for the contribution of different infection routes. Using data from a large population-based serological study (n = 5,179), we provide quantitative estimates of key epidemiological parameters, including the transmissibility of primary infection, reactivation, and re-infection. Mixture models are fitted to age- and sex-specific antibody response data from the Netherlands, showing that the data can be described by a model with three distributions of antibody measurements, i.e. uninfected, infected, and infected with increased antibody concentration. Estimates of seroprevalence increase gradually with age, such that at 80 years 73% (95%CrI: 64%-78%) of females and 62% (95%CrI: 55%-68%) of males are infected, while 57% (95%CrI: 47%-67%) of females and 37% (95%CrI: 28%-46%) of males have increased antibody concentration. Merging the statistical analyses with transmission models, we find that models with infectious reactivation (i.e. reactivation that can lead to the virus being transmitted to a novel host) fit the data significantly better than models without infectious reactivation. Estimated reactivation rates increase from low values in children to 2%-4% per year in women older than 50 years. The results advance a hypothesis in which transmission from adults after infectious reactivation is a key driver of transmission. We discuss the implications for control strategies aimed at reducing CMV infection in vulnerable groups.

Human cytomegalovirus (CMV) is a herpes virus causing lifelong infection. In high-income countries, the probability of infection increases gradually with age such that at old age up to 100% of the population is infected. CMV is thought to be transmitted mainly by transfer of saliva or urine, but little quantitative evidence is available about the transmission dynamics. We analyze serological data to estimate age- and sex-specific rates of infection, re-infection, and reactivation. The analyses show that infectious reactivation (i.e. reactivation of the virus in an infected person that is sufficient for it to be transmitted to another person) is essential to explain the data. We propose that infectious reactivation in adults is an important driver of transmission of CMV.

Incorporating social contact data in spatio-temporal models for infectious disease spread

Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases-possibly stratified by region and/or age group. We investigate how an age-structured social contact matrix can be incorporated into a spatio-temporal endemic-epidemic model for infectious disease counts. To illustrate the approach, we analyze the spread of norovirus gastroenteritis over six age groups within the 12 districts of Berlin, 2011-2015, using contact data from the POLYMOD study. The proposed age-structured model outperforms alternative scenarios with homogeneous or no mixing between age groups. An extended contact model suggests a power transformation of the survey-based contact matrix toward more within-group transmission.

Risk stratification in compartmental epidemic models: Where to draw the line?

Economic evaluations of infectious disease control interventions frequently use dynamic compartmental epidemic models. Such models capture heterogeneity in risk of infection by stratifying the population into discrete risk groups, thus approximating what is typically continuous variation in risk. An important open question is whether and how different risk stratification choices influence model predictions of intervention effects. We develop equivalent Susceptible-Infected-Susceptible (SIS) dynamic transmission models: an unstratified model, a model stratified into a high-risk and low-risk group, and a model with an arbitrary number of risk groups. Absent intervention, the models produce the same overall prevalence of infected individuals in steady state. We consider an intervention that either reduces the contact rate or increases the disease clearance rate. We develop analytical and numerical results characterizing the models and the effects of the intervention. We find that there exist multiple feasible choices of risk stratification, contact distribution, and within- and between-group contact rates for models that stratify risk. We show analytically and empirically that these choices can generate different estimates of intervention effectiveness, and that these differences can be significant enough to alter conclusions from cost-effectiveness analyses and change policy recommendations. We conclude that the choice of how to discretize risk in compartmental epidemic models can influence predicted effectiveness of interventions. Therefore, analysts should examine multiple alternatives and report the range of results.

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

BACKGROUND

To reduce the burden of severe influenza, most industrialized countries target specific risk-groups with influenza vaccines, e.g. the elderly or individuals with comorbidities. Since children are the main spreaders, some countries have recently implemented childhood vaccination programs to reduce overall virus transmission and thereby influenza disease in the whole population. The introduction of childhood vaccination programs was often supported by modelling studies that predicted substantial incidence reductions. We developed a mathematical transmission model to examine the potential impact of childhood influenza vaccination in Germany, while also challenging established modelling assumptions.

METHODS

We developed an age-stratified SEIR-type transmission model to reproduce the epidemic influenza seasons between 2003/04 and 2013/14. The model was built upon German population counts, contact patterns, and vaccination history and was fitted to seasonal data on influenza-attributable medically attended acute respiratory infections (I-MAARI) and strain distribution using Bayesian methods. As novelties we (i) implemented a stratified model structure enabling seasonal variability and (ii) deviated from the commonly assumed mass-action-principle by employing a phenomenological transmission rate.

RESULTS

According to the model, by vaccinating primarily the elderly over ten seasons 4 million (95% prediction interval: 3.84 - 4.19) I-MAARI were prevented which corresponds to an 8.6% (8.3% - 8.9%) reduction compared to a no-vaccination scenario and a number-needed-to-vaccinate (NNV) to prevent one I-MAARI of 37.1 (35.5 - 38.7). Additional vaccination of 2-10 year-old children at 40% coverage would have led to an overall I-MAARI reduction of 17.8% (17.1 - 18.7%) mostly due to indirect effects with a NNV of 20.7 (19.6 - 21.6). When employing the traditional mass-action-principle, the model predicted a more than 3-fold higher I-MAARI reduction (55.6%) due to childhood vaccination.

CONCLUSION

In Germany, the introduction of routine childhood influenza vaccination could considerably reduce I-MAARI among all age-groups and improve the NNV. However, the predicted impact is much lower compared to previous studies, which is primarily caused by our phenomenological approach to modelling influenza virus transmission.

Social mixing in Fiji: Who-eats-with-whom contact patterns and the implications of age and ethnic heterogeneity for disease dynamics in the Pacific Islands

Empirical data on contact patterns can inform dynamic models of infectious disease transmission. Such information has not been widely reported from Pacific islands, nor strongly multi-ethnic settings, and few attempts have been made to quantify contact patterns relevant for the spread of gastrointestinal infections. As part of enteric fever investigations, we conducted a cross-sectional survey of the general public in Fiji, finding that within the 9,650 mealtime contacts reported by 1,814 participants, there was strong like-with-like mixing by age and ethnicity, with higher contact rates amongst iTaukei than non-iTaukei Fijians. Extra-domiciliary lunchtime contacts follow these mixing patterns, indicating the overall data do not simply reflect household structures. Inter-ethnic mixing was most common amongst school-age children. Serological responses indicative of recent Salmonella Typhi infection were found to be associated, after adjusting for age, with increased contact rates between meal-sharing iTaukei, with no association observed for other contact groups. Animal ownership and travel within the geographical division were common. These are novel data that identify ethnicity as an important social mixing variable, and use retrospective mealtime contacts as a socially acceptable metric of relevance to enteric, contact and respiratory diseases that can be collected in a single visit to participants. Application of these data to other island settings will enable communicable disease models to incorporate locally relevant mixing patterns in parameterisation.

Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability

Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only.

Exploring the population-level impact of MenB vaccination via modeling: Potential for serogroup replacement

Various meningococcal conjugate vaccines exist against serogroups A, C, W and Y. A new protein-based vaccine targeting serogroup B (MenB) is also now available. The potential of such vaccines to drive serogroup replacement is considered a possible public health concern when implementing nationwide routine immunization programmes. The aim of this work was to investigate if and how serogroup replacement may occur following widespread vaccination with a MenB vaccine that may protect against carriage. To that end, we built a dynamic transmission model with age and serogroup stratification, focusing on European settings where most invasive meningococcal disease (IMD) cases are caused by serogroups B and C. For illustration purposes, the model was employed in 2 such settings: UK (England and Wales) and Czech Republic. Preliminary model-based projections suggest that, under strong serogroup competition for colonization, vaccine-induced serogroup replacement may occur even with a relatively low vaccine efficacy against serogroup B carriage (e.g., 20%), with potential subsequent increase in serogroup C IMD. The magnitude and speed of the model-projected serogroup C IMD increase depend on the MenB vaccination strategy, vaccine efficacy against carriage and the extent of any potential cross-protection against other serogroups. These analyses are neither exhaustive nor definitive, and focused on simulating potential population-level trends in IMD post-vaccination, under certain assumptions. Due to present inherent limitations and uncertainties, this study has limited quantitative value and is best regarded as an explorative qualitative modeling approach, to complement and challenge the current status quo, and suggest areas where collecting additional data may be essential.

Inferring rubella outbreak risk from seroprevalence data in Belgium

Rubella is usually a mild disease for which infections often pass by unnoticed. In approximately 50% of the cases, there are no or only few clinical symptoms. However, rubella contracted during early pregnancy could lead to spontaneous abortion, to central nervous system defects, or to one of a range of other serious and debilitating conditions in a newborn such as the congenital rubella syndrome. Before the introduction of mass vaccination, rubella was a common childhood infection occurring all over the world. However, since the introduction of rubella antigen-containing vaccines, the incidence of rubella has declined dramatically in high-income countries. Recent large-scale mumps outbreaks, one of the components in the combined measles-mumps-rubella vaccine, occurring in countries throughout Europe with high vaccination coverage, provide evidence of pathogen-specific waning of vaccine-induced immunity and primary vaccine failure. In addition, recent measles outbreaks affecting populations with suboptimal vaccination coverages stress the importance of maintaining high vaccination coverages. In this paper, we focus on the assessment of rubella outbreak risk using a previously developed method to identify geographic regions of high outbreak potential. The methodology relies on 2006 rubella seroprevalence data and vaccination coverage data from Belgium and information on primary and secondary vaccine failure obtained from extensive literature reviews. We estimated the rubella outbreak risk in Belgium to be low, however maintaining high levels of immunisation and surveillance are of utmost importance to avoid future outbreaks.

Estimating age-specific reproductive numbers-A comparison of methods

Large outbreaks, such as those caused by influenza, put a strain on resources necessary for their control. In particular, children have been shown to play a key role in influenza transmission during recent outbreaks, and targeted interventions, such as school closures, could positively impact the course of emerging epidemics. As an outbreak is unfolding, it is important to be able to estimate reproductive numbers that incorporate this heterogeneity and to use surveillance data that is routinely collected to more effectively target interventions and obtain an accurate understanding of transmission dynamics. There are a growing number of methods that estimate age-group specific reproductive numbers with limited data that build on methods assuming a homogenously mixing population. In this article, we introduce a new approach that is flexible and improves on many aspects of existing methods. We apply this method to influenza data from two outbreaks, the 2009 H1N1 outbreaks in South Africa and Japan, to estimate age-group specific reproductive numbers and compare it to three other methods that also use existing data from social mixing surveys to quantify contact rates among different age groups. In this exercise, all estimates of the reproductive numbers for children exceeded the critical threshold of one and in most cases exceeded those of adults. We introduce a flexible new method to estimate reproductive numbers that describe heterogeneity in the population.

The most efficient critical vaccination coverage and its equivalence with maximizing the herd effect

'Critical vaccination coverages' are vaccination allocations that result in an effective reproduction ratio of one. In a population with interacting subpopulations there are many different critical vaccination coverages. To find the most efficient critical vaccination coverage, we define the following optimization problem: minimize the required amount of vaccines to obtain an effective reproduction ratio of exactly one. We prove that this optimization problem is equivalent to the problem of maximizing the proportion of susceptibles that escape infection during an epidemic (i.e., maximizing the herd effect). We propose an efficient general approach to solve these optimization problems based on Perron-Frobenius theory. We study two special cases that provide further insight into these optimization problems. First, we derive an efficient algorithm for the case of multiple populations that interact according to separable mixing. In this algorithm the subpopulations are ordered by their ratio of population size to reproduction ratio. Allocating vaccines based on this priority order results in an optimal allocation. Second, we derive an explicit analytic solution for the case of two interacting populations. We apply our solutions in a case study for pre-pandemic vaccination in the initial phase of an influenza pandemic where the entire population is susceptible to the new influenza virus. The results show that for the optimal allocation the critical vaccination coverage is achieved for a much smaller amount of vaccines as compared to allocations proposed previously.

Clustering of contacts relevant to the spread of infectious disease

OBJECTIVE

Infectious disease spread depends on contact rates between infectious and susceptible individuals. Transmission models are commonly informed using empirically collected contact data, but the relevance of different contact types to transmission is still not well understood. Some studies select contacts based on a single characteristic such as proximity (physical/non-physical), location, duration or frequency. This study aimed to explore whether clusters of contacts similar to each other across multiple characteristics could better explain disease transmission.

METHODS

Individual contact data from the POLYMOD survey in Poland, Great Britain, Belgium, Finland and Italy were grouped into clusters by the k medoids clustering algorithm with a Manhattan distance metric to stratify contacts using all four characteristics. Contact clusters were then used to fit a transmission model to sero-epidemiological data for varicella-zoster virus (VZV) in each country.

RESULTS AND DISCUSSION

Across the five countries, 9-15 clusters were found to optimise both quality of clustering (measured using average silhouette width) and quality of fit (measured using several information criteria). Of these, 2-3 clusters were most relevant to VZV transmission, characterised by (i) 1-2 clusters of age-assortative contacts in schools, (ii) a cluster of less age-assortative contacts in non-school settings. Quality of fit was similar to using contacts stratified by a single characteristic, providing validation that single stratifications are appropriate. However, using clustering to stratify contacts using multiple characteristics provided insight into the structures underlying infection transmission, particularly the role of age-assortative contacts, involving school age children, for VZV transmission between households.

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.

How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?

Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

Schools, offices, hospitals play an important role in the spreading of epidemics. Information about interactions between individuals in such contexts can help understand the patterns of transmission and design ad hoc immunization strategies. Data about contacts can be collected through various techniques such as diaries or proximity sensors. Here, we first ask if the corresponding datasets give similar predictions of the epidemic risk when they are used to build a network of contacts among individuals. Not surprisingly, the answer is negative: indeed, if we consider data from sensors as the ground truth, diaries are affected by low participation rate, underreporting and overestimation of durations. Is it however possible, despite these biases, to use data from contact diaries to obtain sensible epidemic risk predictions? We show here that, thanks to the structural similarities between the two networks, it is possible to use the contact diaries to build surrogate versions of the contact network obtained from the sensor data, such that both yield the same epidemic risk estimation. We show that the construction of such surrogate networks can be performed using solely the information contained in the contact diaries, complemented by publicly available data on the heterogeneity of cumulative contact durations between individuals.

A dynamic transmission model with age-dependent infectiousness and reactivation for cytomegalovirus in the United States: Potential impact of vaccination strategies on congenital infection

We present an age-structured dynamic transmission model for cytomegalovirus (CMV) in the United States, based on natural history and available data, primarily aiming to combine the available qualitative and quantitative knowledge toward more complex modeling frameworks to better reflect the underlying biology and epidemiology of the CMV infection. The model structure explicitly accounts for primary infections, reactivations and re-infections. Duration of infectiousness and likelihood of reactivation were both assumed to be age-dependent, and natural reduction in the re-infection risk following primary infection was included. We used an empirical social contact matrix (POLYMOD-based) as support for CMV transmission between different age groups. The baseline model reproduced well the age-stratified seroprevalence data (National Health and Nutrition Examination Survey III) used for calibration. The model was further used to explore the potential impact of hypothetical vaccination on reducing congenital CMV infection under various vaccine profiles and vaccination scenarios. Our preliminary model-based simulations suggested that while infant vaccination may represent an attractive way to reduce congenital CMV infection over time, adolescent female vaccination with an adequate routine booster platform may, under certain conditions, provide an alternative. However, for such tools to be considered toward actual decision-making, enhanced validations based on additional studies and data would be further necessary. The modeling framework presented in this paper was designed to be sufficiently general and flexible, such that it can allow for further adaptations to reflect new knowledge or data that may become available in the future.

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.

Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0-9 years) and adults (25-54 years) have the highest potential to cause a major zoonotic outbreak.

Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence

Dynamic transmission models are essential to design and evaluate control strategies for airborne infections. Our objective was to develop a dynamic transmission model for seasonal influenza allowing to evaluate the impact of vaccinating specific age groups on the incidence of infection, disease and mortality. Projections based on such models heavily rely on assumed 'input' parameter values. In previous seasonal influenza models, these parameter values were commonly chosen ad hoc, ignoring between-season variability and without formal model validation or sensitivity analyses. We propose to directly estimate the parameters by fitting the model to age-specific influenza-like illness (ILI) incidence data over multiple influenza seasons. We used a weighted least squares (WLS) criterion to assess model fit and applied our method to Belgian ILI data over six influenza seasons. After exploring parameter importance using symbolic regression, we evaluated a set of candidate models of differing complexity according to the number of season-specific parameters. The transmission parameters (average R0, seasonal amplitude and timing of the seasonal peak), waning rates and the scale factor used for WLS optimization, influenced the fit to the observed ILI incidence the most. Our results demonstrate the importance of between-season variability in influenza transmission and our estimates are in line with the classification of influenza seasons according to intensity and vaccine matching.

A household-based study of contact networks relevant for the spread of infectious diseases in the highlands of Peru

BACKGROUND

Few studies have quantified social mixing in remote rural areas of developing countries, where the burden of infectious diseases is usually the highest. Understanding social mixing patterns in those settings is crucial to inform the implementation of strategies for disease prevention and control. We characterized contact and social mixing patterns in rural communities of the Peruvian highlands.

METHODS AND FINDINGS

This cross-sectional study was nested in a large prospective household-based study of respiratory infections conducted in the province of San Marcos, Cajamarca-Peru. Members of study households were interviewed using a structured questionnaire of social contacts (conversation or physical interaction) experienced during the last 24 hours. We identified 9015 reported contacts from 588 study household members. The median age of respondents was 17 years (interquartile range [IQR] 4-34 years). The median number of reported contacts was 12 (IQR 8-20) whereas the median number of physical (i.e. skin-to-skin) contacts was 8.5 (IQR 5-14). Study participants had contacts mostly with people of similar age, and with their offspring or parents. The number of reported contacts was mainly determined by the participants' age, household size and occupation. School-aged children had more contacts than other age groups. Within-household reciprocity of contacts reporting declined with household size (range 70%-100%). Ninety percent of household contact networks were complete, and furthermore, household members' contacts with non-household members showed significant overlap (range 33%-86%), indicating a high degree of contact clustering. A two-level mixing epidemic model was simulated to compare within-household mixing based on observed contact networks and within-household random mixing. No differences in the size or duration of the simulated epidemics were revealed.

CONCLUSION

This study of rural low-density communities in the highlands of Peru suggests contact patterns are highly assortative. Study findings support the use of within-household homogenous mixing assumptions for epidemic modeling in this setting.

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.

The contribution of social behaviour to the transmission of influenza A in a human population

Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35-50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.

Social mixing patterns in rural and urban areas of southern China

A dense population, global connectivity and frequent human-animal interaction give southern China an important role in the spread and emergence of infectious disease. However, patterns of person-to-person contact relevant to the spread of directly transmitted infections such as influenza remain poorly quantified in the region. We conducted a household-based survey of travel and contact patterns among urban and rural populations of Guangdong, China. We measured the character and distance from home of social encounters made by 1821 individuals. Most individuals reported 5-10 h of contact with around 10 individuals each day; however, both distributions have long tails. The distribution of distance from home at which contacts were made is similar: most were within a kilometre of the participant's home, while some occurred further than 500 km away. Compared with younger individuals, older individuals made fewer contacts which tended to be closer to home. There was strong assortativity in age-based contact rates. We found no difference between the total number or duration of contacts between urban and rural participants, but urban participants tended to make contacts closer to home. These results can improve mathematical models of infectious disease emergence, spread and control in southern China and throughout the region.

Assessing mumps outbreak risk in highly vaccinated populations using spatial seroprevalence data

Mumps is a potentially severe viral infection. The incidence of mumps has declined dramatically in high-income countries since the introduction of mumps antigen-containing vaccines. However, recent large outbreaks of mumps in highly vaccinated populations suggest waning of vaccine-induced immunity and primary vaccine failure. In this paper we present a simple method for identifying geographic regions with high outbreak potential, demonstrated using 2006 mumps seroprevalence data from Belgium and Belgian vaccination coverage data. Predictions of the outbreak potential in terms of the effective reproduction number in future years signal an increased risk of new mumps outbreaks. Literature reviews on serological information for both primary vaccine failure and waning immunity provide essential information for our predictions. Tailor-made additional vaccination campaigns would be valuable for decreasing local pockets of susceptibility, thereby reducing the risk of future large-scale mumps outbreaks.

Changing social contact patterns under tropical weather conditions relevant for the spread of infectious diseases

Weather conditions and social contact patterns provide some clues to understanding year-round influenza epidemics in the tropics. Recent studies suggest that contact patterns may direct influenza transmission in the tropics as critically as the aerosol channel in temperate regions. To examine this argument, we analysed a representative nationwide survey dataset of contact diaries with comprehensive weather data in Taiwan. Methods we used included model-free estimated relative changes in reproduction number, R 0; relative changes in the number of contacts; and model-based estimated relative changes in mean contacts using zero-inflated negative binomial regression models. Overall, social contact patterns clearly differ by demographics (such as age groups), personal idiosyncrasies (such as personality and happiness), and social institutions (such as the division of weekdays and weekend days). Further, weather conditions also turn out to be closely linked to contact patterns under various circumstances. Fleeting contacts, for example, tend to diminish when it rains hard on weekdays, while physical contacts also decrease during weekend days with heavy rain. Frequent social contacts on weekdays and under good weather conditions, including high temperature and low absolute humidity, all might facilitate the transmission of infectious diseases in tropical regions.

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

The recent availability of survey data on social contact patterns has made possible important advances in the understanding of the social determinants of the spread of close-contact infections, and of the importance of long-lasting contacts for effective transmission to occur. Still, little is known about the relationship between two of the most critical identified factors (frequency of contacts and duration of exposure) and how this relationship applies to different types of infections. By integrating data from two independently collected social surveys (Polymod and time use), we propose a model that combines these two transmission determinants into a new epidemiologically relevant measure of contacts: the number of "suitable" contacts, which is the number of contacts that involve a sufficiently long exposure time to allow for transmission. The validity of this new epidemiological measure is tested against Italian serological data for varicella and parvovirus-B19, with uncertainty evaluated using the Bayesian melding technique. The model performs quite well, indicating that the interplay between time of exposure and contacts is critical for varicella transmission, while for B19 it is the duration of exposure that matters for transmission.

How should social mixing be measured: comparing web-based survey and sensor-based methods

Background

Contact surveys and diaries have conventionally been used to measure contact networks in different settings for elucidating infectious disease transmission dynamics of respiratory infections. More recently, technological advances have permitted the use of wireless sensor devices, which can be worn by individuals interacting in a particular social context to record high resolution mixing patterns. To date, a direct comparison of these two different methods for collecting contact data has not been performed.

Methods

We studied the contact network at a United States high school in the spring of 2012. All school members (i.e., students, teachers, and other staff) were invited to wear wireless sensor devices for a single school day, and asked to remember and report the name and duration of all of their close proximity conversational contacts for that day in an online contact survey. We compared the two methods in terms of the resulting network densities, nodal degrees, and degree distributions. We also assessed the correspondence between the methods at the dyadic and individual levels.

Results

We found limited congruence in recorded contact data between the online contact survey and wireless sensors. In particular, there was only negligible correlation between the two methods for nodal degree, and the degree distribution differed substantially between both methods. We found that survey underreporting was a significant source of the difference between the two methods, and that this difference could be improved by excluding individuals who reported only a few contact partners. Additionally, survey reporting was more accurate for contacts of longer duration, and very inaccurate for contacts of shorter duration. Finally, female participants tended to report more accurately than male participants.

Conclusions

Online contact surveys and wireless sensor devices collected incongruent network data from an identical setting. This finding suggests that these two methods cannot be used interchangeably for informing models of infectious disease dynamics.

Positive network assortativity of influenza vaccination at a high school: implications for outbreak risk and herd immunity

Schools are known to play a significant role in the spread of influenza. High vaccination coverage can reduce infectious disease spread within schools and the wider community through vaccine-induced immunity in vaccinated individuals and through the indirect effects afforded by herd immunity. In general, herd immunity is greatest when vaccination coverage is highest, but clusters of unvaccinated individuals can reduce herd immunity. Here, we empirically assess the extent of such clustering by measuring whether vaccinated individuals are randomly distributed or demonstrate positive assortativity across a United States high school contact network. Using computational models based on these empirical measurements, we further assess the impact of assortativity on influenza disease dynamics. We found that the contact network was positively assortative with respect to influenza vaccination: unvaccinated individuals tended to be in contact more often with other unvaccinated individuals than with vaccinated individuals, and these effects were most pronounced when we analyzed contact data collected over multiple days. Of note, unvaccinated males contributed substantially more than unvaccinated females towards the measured positive vaccination assortativity. Influenza simulation models using a positively assortative network resulted in larger average outbreak size, and outbreaks were more likely, compared to an otherwise identical network where vaccinated individuals were not clustered. These findings highlight the importance of understanding and addressing heterogeneities in seasonal influenza vaccine uptake for prevention of large, protracted school-based outbreaks of influenza, in addition to continued efforts to increase overall vaccine coverage.