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

Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases

Thanks to recent technological advances, measuring real-world interactions by the use of mobile devices and wearable sensors has become possible, allowing researchers to gather data on human social interactions in a variety of contexts with high spatial and temporal resolution. Empirical data describing contact networks have thus acquired a high level of detail that may yield new insights into the dynamics of infection transmission between individuals. At the same time, such data bring forth new challenges related to their statistical description and analysis, and to their use in mathematical models. In particular, the integration of highly detailed empirical data in computational frameworks designed to model the spread of infectious diseases raises the issue of assessing which representations of the raw data work best to inform the models. There is an emerging need to strike a balance between simplicity and detail in order to ensure both generalizability and accuracy of predictions. Here, we review recent work on the collection and analysis of highly detailed data on temporal networks of face-to-face human proximity, carried out in the context of the SocioPatterns collaboration. We discuss the various levels of coarse-graining that can be used to represent the data in order to inform models of infectious disease transmission. We also discuss several limitations of the data and future avenues for data collection and modelling efforts in the field of infectious diseases.

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.

Association between population prevalence of smoking and incidence of meningococcal disease in Norway, Sweden, Denmark and the Netherlands between 1975 and 2009: a population-based time series analysis

OBJECTIVE

To investigate the relationship between the prevalence of smoking in the population and incidence of invasive meningococcal disease (IMD) among children under 5 years of age.

DESIGN

Retrospective, longitudinal, observational study. Poisson regression controlled for confounding factors.

SETTING

Norway, Sweden, Denmark and the Netherlands between 1975 and 2009.

POPULATION

Total population of approximately 35 million people in these four countries.

DATA SOURCES

Data were collected from the Ministries of Health, National Statistics Bureaus and other relevant national institutes.

RESULTS

In Norway, there was a significant positive relationship between the annual prevalence of daily smokers among individuals aged 25-49 years and the incidence of IMD in children under 5 years of age, unadjusted (RR=1.04-1.06, 95% CI 1.02 to 1.07, p<0.001) and after adjustment for time of year (quarter), incidence of influenza-like illness and household crowding (RR=1.05-1.07, 95% CI 1.03 to 1.09, p<0.001). Depending on age group, the risk of IMD increased by 5.2-6.9% per 1% increase in smoking prevalence among individuals aged 25-49 years in adjusted analyses. Using limited datasets from the three other countries, unadjusted analysis showed positive associations between IMD in children related to older smokers in Sweden and the Netherlands and negative associations related to younger smokers in Sweden. However, there were no demonstrable associations between incidence of IMD and prevalence of smoking, after adjustment for the same confounding variables.

CONCLUSIONS

The reduced incidence of IMD in Norway between 1975 and 2009 may partly be explained by the reduced prevalence of smoking during this period. High-quality surveillance data are required to confirm this in other countries. Strong efforts to reduce smoking in the whole population including targeted campaigns to reduce smoking among adults may have a role to play in the prevention of IMD in children.

Cost-effectiveness of Tdap vaccination of adults aged ≥65 years in the prevention of pertussis in the US: a dynamic model of disease transmission

OBJECTIVES

In February 2012, the Advisory Committee on Immunization Practices (ACIP) advised that all adults aged ≥65 years receive a single dose of reduced-antigen-content tetanus, diphtheria, and acellular pertussis (Tdap), expanding on a 2010 recommendation for adults >65 that was limited to those with close contact with infants. We evaluated clinical and economic outcomes of adding Tdap booster of adults aged ≥65 to "baseline" practice [full-strength DTaP administered from 2 months to 4-6 years, and one dose of Tdap at 11-64 years replacing decennial Td booster], using a dynamic model.

METHODS

We constructed a population-level disease transmission model to evaluate the cost-effectiveness of supplementing baseline practice by vaccinating 10% of eligible adults aged ≥65 with Tdap replacing the decennial Td booster. US population effects, including indirect benefits accrued by unvaccinated persons, were estimated during a 1-year period after disease incidence reached a new steady state, with consequences of deaths and long-term pertussis sequelae projected over remaining lifetimes. Model outputs include: cases by severity, encephalopathy, deaths, costs (of vaccination and pertussis care) and quality-adjusted life-years (QALYs) associated with each strategy. Results in terms of incremental cost/QALY gained are presented from payer and societal perspectives. Sensitivity analyses vary key parameters within plausible ranges.

RESULTS

For the US population, the intervention is expected to prevent >97,000 cases (>4,000 severe and >5,000 among infants) of pertussis annually at steady state. Additional vaccination costs are $4.7 million. Net cost savings, including vaccination costs, are $47.7 million (societal perspective) and $44.8 million (payer perspective). From both perspectives, the intervention strategy is dominant (less costly, and more effective by >3,000 QALYs) versus baseline. Results are robust to sensitivity analyses and alternative scenarios.

CONCLUSIONS

Immunization of eligible adults aged ≥65, consistent with the current ACIP recommendation, is cost saving from both payer and societal perspectives.

The social life of infants in the context of infectious disease transmission; social contacts and mixing patterns of the very young

Insight into how humans interact helps further understanding of the transmission of infectious diseases. For diseases such as pertussis, infants are at particular risk for severe outcomes. To understand the contact pattern of infants, especially those too young to be vaccinated, we sent contact diaries to a representative sample of 1000 mothers in the United Kingdom. We received 115 responses with a total of 758 recorded contacts. The average number of daily contacts for an infant was 6.68 overall and 5.7 for those aged ≤10 weeks. Of the latter, 2.1 (37%) contacts were with non-household members and were >15 minutes duration, suggesting that a cocooning programme may miss a substantial proportion of exposures leading to disease transmission. The least contact was between adolescents and infants. Thus the impact of adolescent (pertussis) vaccination on infants would likely be limited, unless it reduces transmission to other age groups whose contact with infants is greater.

Estimating potential infection transmission routes in hospital wards using wearable proximity sensors

Background

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures.

Methods and Findings

We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders.

Conclusions

Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.

A lattice model for influenza spreading

We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the [Formula: see text] season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.

Hope-Simpson's progressive immunity hypothesis as a possible explanation for herpes zoster incidence data

Varicella-zoster virus (VZV) is the causative agent of both varicella (chickenpox) and herpes zoster (HZ) (shingles). After varicella infection, the virus remains dormant in the host's dorsal ganglia and can reactivate due to waning cell-mediated immunity, causing HZ. Exposure of varicella-immune persons to VZV may boost the host's immune response, resulting in a protective effect against HZ. In this study, we used mathematical models of VZV transmission and HZ development to test the biological hypothesis of "progressive immunity," originally proposed by Hope-Simpson (Proc R Soc Med. 1965;58:9-20), that cell-mediated protection against HZ increases after each episode of exposure to VZV. Predictions from a model incorporating such a hypothesis were compared with those of other concurrent models proposed for explaining HZ epidemiology. The progressive immunity model fits significantly better the age profile of HZ incidence for Finland (years 2000-2006), Italy (2003-2005), Spain (1997-2004), and the United Kingdom (1991-1992), suggesting that this mechanism may be critical in shaping HZ patterns. The model thus validated is an alternative to VZV models currently used to evaluate the impact of mass immunization programs for varicella and therefore extends the range of tools available to assist policy-makers with the present decision paralysis on the introduction of vaccination.

An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices

BACKGROUND

The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail.

METHODS

We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations.

RESULTS

We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.

CONCLUSIONS

Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.

Perspectives on the impact of varicella immunization on herpes zoster. A model-based evaluation from three European countries

The introduction of mass vaccination against Varicella-Zoster-Virus (VZV) is being delayed in many European countries because of, among other factors, the possibility of a large increase in Herpes Zoster (HZ) incidence in the first decades after the initiation of vaccination, due to the expected decline of the boosting of Cell Mediated Immunity caused by the reduced varicella circulation. A multi-country model of VZV transmission and reactivation, is used to evaluate the possible impact of varicella vaccination on HZ epidemiology in Italy, Finland and the UK. Despite the large uncertainty surrounding HZ and vaccine-related parameters, surprisingly robust medium-term predictions are provided, indicating that an increase in HZ incidence is likely to occur in countries where the incidence rate is lower in absence of immunization, possibly due to a higher force of boosting (e.g. Finland), whereas increases in HZ incidence might be minor where the force of boosting is milder (e.g. the UK). Moreover, a convergence of HZ post vaccination incidence levels in the examined countries is predicted despite different initial degrees of success of immunization policies. Unlike previous model-based evaluations, our investigation shows that after varicella immunization an increase of HZ incidence is not a certain fact, rather depends on the presence or absence of factors promoting a strong boosting intensity and which might or not be heavily affected by changes in varicella circulation due to mass immunization. These findings might explain the opposed empirical evidences observed about the increases of HZ in sites where mass varicella vaccination is ongoing.

A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks

BACKGROUND

Infectious disease outbreaks in communities can be controlled by early detection and effective prevention measures. Assessing the relative importance of each individual community member with respect to these two processes requires detailed knowledge about the underlying social contact network on which the disease can spread. However, mapping social contact networks is typically too resource-intensive to be a practical possibility for most communities and institutions.

METHODS

Here, we describe a simple, low-cost method - called collocation ranking - to assess individual importance for early detection and targeted intervention strategies that are easily implementable in practice. The method is based on knowledge about individual collocation which is readily available in many community settings such as schools, offices, hospitals, and so on. We computationally validate our method in a school setting by comparing the outcome of the method against computational predictions based on outbreak simulations on an empirical high-resolution contact network. We compare collocation ranking to other methods for assessing the epidemiological importance of the members of a population. To this end, we select subpopulations of the school population by applying these assessment methods to the population and adding individuals to the subpopulation according to their individual rank. Then, we assess how suited these subpopulations are for early detection and targeted intervention strategies.

RESULTS

Likelihood and timing of infection during an outbreak are important features for early detection and targeted intervention strategies. Subpopulations selected by the collocation ranking method show a substantially higher average infection probability and an earlier onset of symptoms than randomly selected subpopulations. Furthermore, these subpopulations selected by the collocation ranking method were close to the optimum.

CONCLUSIONS

Our results indicate that collocation ranking is a highly effective method to assess individual importance, providing critical low-cost information for the development of sentinel surveillance systems and prevention strategies.

Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-5

The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this paper.

Behavioral Epidemiology of Infectious Diseases: An Overview

The focus of the growing discipline of behavioral epidemiology (BE) of infectious diseases is on individual behavior as a key determinant of infection trajectories. This overview departs from the central, but static, role of human behavior in traditional mathematical models of infection to motivate the importance of including behavior into epidemiological models. Our aim is threefold. First, we attempt to motivate the historical and cultural background underpinning the BE revolution, focusing on the issue of rational opposition to vaccines as a natural endpoint of the changed relation between man and disease in modern industrialized countries. Second, we review those contributions, from both mathematical epidemiology and economics, that forerun the current “epidemic” of studies on BE. Last, we offer a more detailed overview of the current epidemic phase of BE studies and, still motivated by the issue of immunization choices, introduce some baseline ideas and models.

A nice day for an infection? Weather conditions and social contact patterns relevant to influenza transmission

Although there is no doubt that significant morbidity and mortality occur during annual influenza epidemics, the role of contextual circumstances, which catalyze seasonal influenza transmission, remains unclear. Weather conditions are believed to affect virus survival, efficiency of transmission and host immunity, but seasonality may also be driven by a tendency of people to congregate indoors during periods of bad weather. To test this hypothesis, we combined data from a social contact survey in Belgium with local weather data. In the absence of a previous in-depth weather impact analysis of social contact patterns, we explored the possibilities and identified pitfalls. We found general dominance of day-type (weekend, holiday, working day) over weather conditions, but nonetheless observed an increase in long duration contacts ([Formula: see text]1 hour) on regular workdays with low temperatures, almost no precipitation and low absolute humidity of the air. Interestingly, these conditions are often assumed to be beneficial for virus survival and transmission. Further research is needed to establish the impact of the weather on social contacts. We recommend that future studies sample over a broad spectrum of weather conditions and day types and include a sufficiently large proportion of holiday periods and weekends.

Representative contact diaries for modeling the spread of infectious diseases in Taiwan

Recent studies of infectious diseases have attempted to construct more realistic parameters of interpersonal contact patterns from diary-approach surveys. To ensure that such diary-based contact patterns provide accurate baseline data for policy implementation in densely populated Taiwan, we collected contact diaries from a national sample, using 3-stage systematic probability sampling and rigorous in-person interviews. A representative sample of 1,943 contact diaries recorded a total of 24,265 wide-range, face-to-face interpersonal contacts during a 24-hour period. Nearly 70% of the contacts occurred outside of respondents' households. The most active age group was schoolchildren (ages 5–14), who averaged around 16–18 daily contacts, about 2–3 times as many as the least active age groups. We show how such parameters of contact patterns help modify a sophisticated national simulation system that has been used for years to model the spread of pandemic diseases in Taiwan. Based on such actual and representative data that enable researchers to infer findings to the whole population, our analyses aim to facilitate implementing more appropriate and effective strategies for controlling an emerging or pandemic disease infection.

Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread

Social contact patterns among individuals encode the transmission route of infectious diseases and are a key ingredient in the realistic characterization and modeling of epidemics. Unfortunately, the gathering of high quality experimental data on contact patterns in human populations is a very difficult task even at the coarse level of mixing patterns among age groups. Here we propose an alternative route to the estimation of mixing patterns that relies on the construction of virtual populations parametrized with highly detailed census and demographic data. We present the modeling of the population of 26 European countries and the generation of the corresponding synthetic contact matrices among the population age groups. The method is validated by a detailed comparison with the matrices obtained in six European countries by the most extensive survey study on mixing patterns. The methodology presented here allows a large scale comparison of mixing patterns in Europe, highlighting general common features as well as country-specific differences. We find clear relations between epidemiologically relevant quantities (reproduction number and attack rate) and socio-demographic characteristics of the populations, such as the average age of the population and the duration of primary school cycle. This study provides a numerical approach for the generation of human mixing patterns that can be used to improve the accuracy of mathematical models in the absence of specific experimental data.

The dynamics of infectious diseases caused by pathogens transmissible from human to human strongly depends on contact patterns between individuals. High quality observational data on contact patterns, usually presented in the form of age-specific contact matrices, are difficult to gather and are currently available only for few countries worldwide. Here we propose a computational approach, based on the simulation of a virtual society of agents, allowing the estimation of contact patterns by age for 26 European countries. We validate the estimated contact matrices against those obtained by the most extensive field study on contact patterns, with data collected in eight European countries. We show that our contact matrices share some common features, e.g. individuals tend to mix preferentially with individuals their own age, and country-specific differences, which can be partly explained by differences in population structures due to different demographic trajectories followed after WWII. Our analysis highlights well defined correlations between epidemiological parameters and socio-demographic features of the populations. This study provides the first estimates of contact matrices for many European countries where specific experimental data are still not available.

Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--5

The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this article.

Close encounters of the infectious kind: methods to measure social mixing behaviour

A central tenet of close-contact or respiratory infection epidemiology is that infection patterns within human populations are related to underlying patterns of social interaction. Until recently, few researchers had attempted to quantify potentially infectious encounters made between people. Now, however, several studies have quantified social mixing behaviour, using a variety of methods. Here, we review the methodologies employed, suggest other appropriate methods and technologies, and outline future research challenges for this rapidly advancing field of research.

Leveraging social networks for understanding the evolution of epidemics

BACKGROUND

To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them time-dependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics.

RESULTS

We implement a scalable, fully distributed simulator and validate the epidemic model by comparing the simulation results against the data in the 2004-2005 New York State Department of Health Report (NYSDOH), with similar temporal distribution results for the number of infected individuals. We analyze the impact of different types of connection models on the virus propagation. Lastly, we analyze and compare the effects of adopting several different vaccination policies, some of them based on individual characteristics -such as age- while others targeting the super-connectors in the social model.

CONCLUSIONS

This paper presents an approach to modeling the propagation of the influenza virus via a realistic social model based on actual individual interactions extracted from online social networks. We implemented a scalable, fully distributed simulator and we analyzed both the dissemination of the infection and the effect of different vaccination policies on the progress of the epidemics. The epidemic values predicted by our simulator match real data from NYSDOH. Our results show that our simulator can be a useful tool in understanding the differences in the evolution of an epidemic within populations with different characteristics and can provide guidance with regard to which, and how many, individuals should be vaccinated to slow down the virus propagation and reduce the number of infections.

Mixing in age-structured population models of infectious diseases

Infectious diseases are controlled by reducing pathogen replication within or transmission between hosts. Models can reliably evaluate alternative strategies for curtailing transmission, but only if interpersonal mixing is represented realistically. Compartmental modelers commonly use convex combinations of contacts within and among groups of similarly aged individuals, respectively termed preferential and proportionate mixing. Recently published face-to-face conversation and time-use studies suggest that parents and children and co-workers also mix preferentially. As indirect effects arise from the off-diagonal elements of mixing matrices, these observations are exceedingly important. Accordingly, we refined the formula published by Jacquez et al. [19] to account for these newly-observed patterns and estimated age-specific fractions of contacts with each preferred group. As the ages of contemporaries need not be identical nor those of parents and children to differ by exactly the generation time, we also estimated the variances of the Gaussian distributions with which we replaced the Kronecker delta commonly used in theoretical studies. Our formulae reproduce observed patterns and can be used, given contacts, to estimate probabilities of infection on contact, infection rates, and reproduction numbers. As examples, we illustrate these calculations for influenza based on "attack rates" from a prospective household study during the 1957 pandemic and for varicella based on cumulative incidence estimated from a cross-sectional serological survey conducted from 1988-94, together with contact rates from the several face-to-face conversation and time-use studies. Susceptibility to infection on contact generally declines with age, but may be elevated among adolescents and adults with young children.

High-resolution measurements of face-to-face contact patterns in a primary school

BACKGROUND

Little quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6-12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices.

METHODS AND FINDINGS

Data on face-to-face interactions were collected on Thursday, October 1(st) and Friday, October 2(nd) 2009. We recorded 77,602 contact events between 242 individuals (232 children and 10 teachers). In this setting, each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis.

CONCLUSIONS

We report on important properties of the contact patterns between school children that are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics.

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance

PURPOSE OF REVIEW

Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies.

RECENT FINDINGS

Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health.

SUMMARY

Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

BACKGROUND

The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population.

METHODS

We considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects.

RESULTS

We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic.

CONCLUSIONS

These results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics. Please see related article BMC Medicine, 2011, 9:88.

Modelling the effects of population structure on childhood disease: the case of varicella

Realistic, individual-based models based on detailed census data are increasingly used to study disease transmission. Whether the rich structure of such models improves predictions is debated. This is studied here for the spread of varicella, a childhood disease, in a realistic population of children where infection occurs in the household, at school, or in the community at large. A methodology is first presented for simulating households with births and aging. Transmission probabilities were fitted for schools and community, which reproduced the overall cumulative incidence of varicella over the age range of 0-11 years old.Moreover, the individual-based model structure allowed us to reproduce several observed features of VZV epidemiology which were not included as hypotheses in the model: the age at varicella in first-born children was older than in other children, in accordance with observation; the same was true for children residing in rural areas. Model predicted incidence was comparable to observed incidence over time. These results show that models based on detailed census data on a small scale provide valid small scale prediction. By simulating several scenarios, we evaluate how varicella epidemiology is shaped by policies, such as age at first school enrolment, and school eviction. This supports the use of such models for investigating outcomes of public health measures.

What types of contacts are important for the spread of infections?: using contact survey data to explore European mixing patterns

Knowledge of the determinants of infectious disease transmission is a public health priority as it allows the design of optimal control strategies for endemic or emerging infections. We analyse a detailed dataset on contact patterns across five European countries and use available serological profiles for varicella and parvovirus B19 infections to identify the types of contact that may be most relevant for transmission. We show that models informed by contact data fit well the observed serological profiles of both infections. We find that intimate types of contacts explain the pattern of acquisition of serological markers by age better than other types of social contacts. We observe similar patterns in each of the countries analysed, suggesting that there are consistent biological mechanisms at work.

Modelling the epidemiology of infectious diseases for decision analysis: a primer

The number of economic evaluations related to infectious disease topics has increased over the last 2 decades. However, many such evaluations rely on models that do not take into account unique features of infectious diseases that can affect the estimated value of interventions against them. These include their transmissibility from infected to susceptible individuals, the possibility of acquiring natural immunity following recovery from infection and the uncertainties that arise as a result of their complex natural history and epidemiology. Modellers conducting economic evaluations of infectious disease interventions need to know the main features of different types of infectious disease models, the situations in which they should be applied and the effects of model choices on the cost effectiveness of interventions.

Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors

Background

Nosocomial infections place a substantial burden on health care systems and represent one of the major issues in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals.

Methods and Findings

We used wearable active Radio-Frequency Identification Devices (RFID) to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants, with 51 health care workers, 37 patients, and 31 caregivers. Nearly 16,000 contacts were recorded during the study period, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections.

Conclusions

Our study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The obtained results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.

Serological evidence of subclinical transmission of the 2009 pandemic H1N1 influenza virus outside of Mexico

Background

Relying on surveillance of clinical cases limits the ability to understand the full impact and severity of an epidemic, especially when subclinical cases are more likely to be present in the early stages. Little is known of the infection and transmissibility of the 2009 H1N1 pandemic influenza (pH1N1) virus outside of Mexico prior to clinical cases being reported, and of the knowledge pertaining to immunity and incidence of infection during April–June, which is essential for understanding the nature of viral transmissibility as well as for planning surveillance and intervention of future pandemics.

Methodology/Principal Findings

Starting in the fall of 2008, 306 persons from households with schoolchildren in central Taiwan were followed sequentially and serum samples were taken in three sampling periods for haemagglutination inhibition (HI) assay. Age-specific incidence rates were calculated based on seroconversion of antibodies to the pH1N1 virus with an HI titre of 1∶40 or more during two periods: April–June and September–October in 2009. The earliest time period with HI titer greater than 40, as well as a four-fold increase of the neutralization titer, was during April 26–May 3. The incidence rates during the pre-epidemic phase (April–June) and the first wave (July–October) of the pandemic were 14.1% and 29.7%, respectively. The transmissibility of the pH1N1 virus during the early phase of the epidemic, as measured by the effective reproductive number R₀, was 1.16 (95% confidence interval (CI): 0.98–1.34).

Conclusions

Approximately one in every ten persons was infected with the 2009 pH1N1 virus during the pre-epidemic phase in April–June. The lack of age-pattern in seropositivity is unexpected, perhaps highlighting the importance of children as asymptomatic transmitters of influenza in households. Although without virological confirmation, our data raise the question of whether there was substantial pH1N1 transmission in Taiwan before June, when clinical cases were first detected by the surveillance network.

Household size is critical to varicella-zoster virus transmission in the tropics despite lower viral infectivity

The epidemiology and severity of infections can vary dramatically in different geographical regions. Varicella zoster virus (VZV) is a particularly tractable model for investigating such global differences, since infections can be unambiguously identified. VZV is spread by aerosol to cause chickenpox, which, in temperate countries, is a relatively benign childhood infection; yet in tropical countries it tends to occur at later age, a trend associated with markedly increased severity including complications, hospitalization, and overall burden of care.

To investigate global differences in the epidemiology of chickenpox we studied a population in Guinea Bissau, which in contrast to other tropical countries has an unexpectedly early age of infection with VZV, comparable to temperate latitudes. In this study we used detailed records from over 3000 houses during an outbreak of chickenpox, combined with viral genetic information on routes of infection, to obtain precise estimates of disease transmission within and between houses. This community contains many large households in which different families live under a single roof, in living quarters divided by partitions. Our data show that household infectivity in tropical Guinea Bissau is reduced four-fold compared with temperate climates (14.8% versus 61–85%), with an intermediate rate between members of the same family who are in more intimate contact (23.5%). All else being equal, these lower infection rates would be expected to lead to a later age of infection as is commonly seen in other tropical countries. The young age of infection, which had drawn our attention to the Guinea Bissau population, can however be explained by the exceptionally large household sizes (mean 14.5 people).

We have combined genetic and demographic data to show that the epidemiology of chickenpox in tropical Guinea Bissau is dependent on the interaction of the social and physical environments. The distinctive clinical presentation of VZV and its ubiquitous distribution make it an attractive model for estimating the variables that contribute to global differences in the transmission of airborne viruses.

Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data

Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to reliably reconstruct such data. In this paper, we compute a variety of synthetic age-specific contact matrices through simulation of a simple individual-based model (IBM). The model is informed by Italian Time Use data and routine socio-demographic data (e.g., school and workplace attendance, household structure, etc.). The model is named “Little Italy” because each artificial agent is a clone of a real person. In other words, each agent's daily diary is the one observed in a corresponding real individual sampled in the Italian Time Use Survey. We also generated contact matrices from the socio-demographic model underlying the Italian IBM for pandemic prediction. These synthetic matrices are then validated against recently collected Italian serological data for Varicella (VZV) and ParvoVirus (B19). Their performance in fitting sero-profiles are compared with other matrices available for Italy, such as the Polymod matrix. Synthetic matrices show the same qualitative features of the ones estimated from sample surveys: for example, strong assortativeness and the presence of super- and sub-diagonal stripes related to contacts between parents and children. Once validated against serological data, Little Italy matrices fit worse than the Polymod one for VZV, but better than concurrent matrices for B19. This is the first occasion where synthetic contact matrices are systematically compared with real ones, and validated against epidemiological data. The results suggest that simple, carefully designed, synthetic matrices can provide a fruitful complementary approach to questionnaire-based matrices. The paper also supports the idea that, depending on the transmissibility level of the infection, either the number of different contacts, or repeated exposure, may be the key factor for transmission.

Data on social contact patterns are fundamental to design adequate control policies for directly transmissible infectious diseases, ranging from a flu pandemic to tuberculosis, to recurrent epidemics of childhood diseases. Most countries in the world do not dispose of such data. We propose an approach to generate synthetic contact data by simulating an artificial society that integrates routinely available socio-demographic data, such as data on household composition or on school participation, with Time Use data, which are increasingly available. We then validate the ensuing simulated contact data against real epidemiological data for varicella and parvo-virus. The results suggest that the approach is potentially a very fruitful one, and provide some insights on the biology of transmission of close-contact infectious diseases.

Dynamic model of rotavirus transmission and the impact of rotavirus vaccination in Kyrgyzstan

New rotavirus vaccines show promise to reduce the burden of severe diarrhea among children in developing countries. We present an age-specific dynamic rotavirus model to assess the effect of rotavirus vaccination in Kyrgyzstan, a country in Central Asia that is eligible for funds from the GAVI Alliance. A routine rotavirus vaccination program at 95% coverage and 54% effectiveness against severe infection is estimated to lead to a 56% reduction in rotavirus-associated deaths and a 50% reduction in hospital admissions, while outpatient visits and homecare episodes would decrease by 52% compared to baseline levels after 5 years of intervention. A 10% reduction in vaccine efficacy due to incomplete 3-dose regimen is estimated to increase the numbers of severe cases by 6-8%. Herd immunity was found to account for 1% or less of averted cases of severe gastroenteritis, while an extra 7-8% of all rotavirus infections would be avoided due to reduced transmission.

CONCLUSION

Rotavirus vaccines would reduce the burden of rotavirus disease substantially, but the results are sensitive to delay in age-appropriate vaccination.

Evaluation of targeted influenza vaccination strategies via population modeling

BACKGROUND

Because they can generate comparable predictions, mathematical models are ideal tools for evaluating alternative drug or vaccine allocation strategies. To remain credible, however, results must be consistent. Authors of a recent assessment of possible influenza vaccination strategies conclude that older children, adolescents, and young adults are the optimal targets, no matter the objective, and argue for vaccinating them. Authors of two earlier studies concluded, respectively, that optimal targets depend on objectives and cautioned against changing policy. Which should we believe?

METHODS AND FINDINGS

In matrices whose elements are contacts between persons by age, the main diagonal always predominates, reflecting contacts between contemporaries. Indirect effects (e.g., impacts of vaccinating one group on morbidity or mortality in others) result from off-diagonal elements. Mixing matrices based on periods in proximity with others have greater sub- and super-diagonals, reflecting contacts between parents and children, and other off-diagonal elements (reflecting, e.g., age-independent contacts among co-workers), than those based on face-to-face conversations. To assess the impact of targeted vaccination, we used a time-usage study's mixing matrix and allowed vaccine efficacy to vary with age. And we derived mortality rates either by dividing observed deaths attributed to pneumonia and influenza by average annual cases from a demographically-realistic SEIRS model or by multiplying those rates by ratios of (versus adding to them differences between) pandemic and pre-pandemic mortalities.

CONCLUSIONS

In our simulations, vaccinating older children, adolescents, and young adults averts the most cases, but vaccinating either younger children and older adults or young adults averts the most deaths, depending on the age distribution of mortality. These results are consistent with those of the earlier studies.

Development of an individual-based model for polioviruses: implications of the selection of network type and outcome metrics

We developed an individual-based (IB) model to explore the stochastic attributes of state transitions, the heterogeneity of the individual interactions, and the impact of different network structure choices on the poliovirus transmission process in the context of understanding the dynamics of outbreaks. We used a previously published differential equation-based model to develop the IB model and inputs. To explore the impact of different types of networks, we implemented a total of 26 variations of six different network structures in the IB model. We found that the choice of network structure plays a critical role in the model estimates of cases and the dynamics of outbreaks. This study provides insights about the potential use of an IB model to support policy analyses related to managing the risks of polioviruses and shows the importance of assumptions about network structure.

Humoral immunity following chickenpox is influenced by geography and ethnicity

OBJECTIVES

To investigate the contribution of ethnicity and geographical location to varicella-zoster virus (VZV) serostatus and antibody concentrations.

METHODS

The presence and concentrations of antibodies to VZV were measured in 639 Bangladeshi women born in Bangladesh (BBB), 94 Bangladeshi women born in the UK (BUK) and 262 White women born in the UK (WUK). The results were analysed in relation to demographic and social data.

RESULTS

BBB women were significantly less likely to be VZV seropositive at all ages than both BUK and WUK women. However, the odds of a Bangladeshi-born woman being seropositive increased by 1.04 for each year under the age of 15 spent in the UK. In contrast, antibody concentrations were significantly lower in ethnic Bangladeshi women, irrespective of country of birth. White, but not Bangladeshi women, showed evidence of antibody boosting over time despite the latter having more exposure to children.

CONCLUSION

Geographical location during childhood is the major influence on age of primary infection with VZV while the level of antibody is related to ethnicity. Since the risk of re-infection with VZV following both natural infection and vaccination is increased as antibody concentrations fall, these results have implications for VZV vaccination programmes particularly in non-White populations.

Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks

Background

Mathematical models of infection that consider targeted interventions are exquisitely dependent on the assumed mixing patterns of the population. We report on a pilot study designed to assess three different methods (one retrospective, two prospective) for obtaining contact data relevant to the determination of these mixing patterns.

Methods

65 adults were asked to record their social encounters in each location visited during 6 study days using a novel method whereby a change in physical location of the study participant triggered data entry. Using a cross-over design, all participants recorded encounters on 3 days in a paper diary and 3 days using an electronic recording device (PDA). Participants were randomised to first prospective recording method.

Results

Both methods captured more contacts than a pre-study questionnaire, but ascertainment using the paper diary was superior to the PDA (mean difference: 4.52 (95% CI 0.28, 8.77). Paper diaries were found more acceptable to the participants compared with the PDA. Statistical analysis confirms that our results are broadly consistent with those reported from large-scale European based surveys. An association between household size (trend 0.14, 95% CI (0.06, 0.22), P < 0.001) and composition (presence of child 0.37, 95% CI (0.17, 0.56), P < 0.001) and the total number of reported contacts was observed, highlighting the importance of sampling study populations based on household characteristics as well as age. New contacts were still being recorded on the third study day, but compliance had declined, indicating that the optimal number of sample days represents a trade-off between completeness and quality of data for an individual.

Conclusions

The study's location-based reporting design allows greater scope compared to other methods for examining differences in the characteristics of encounters over a range of environments. Improved parameterisation of dynamic transmission models gained from work of this type will aid in the development of more robust decision support tools to assist health policy makers and planners.

Modeling the impact of one- and two-dose varicella vaccination on the epidemiology of varicella and zoster

In many countries, policymakers are being asked to make recommendations regarding the introduction of a 2-dose varicella vaccination program. The objective of this study was to examine the potential impact of 1-dose versus 2-dose varicella vaccination programs on varicella and zoster incidence, using Canada as an example. We developed a deterministic realistic age-structured model that fits 1- and 2-dose vaccine efficacy, varicella force of infection and zoster incidence. Assuming 90% coverage, the base case model (range: min; max) predicts that 1-dose vaccination will reduce varicella and zoster cases by 64% (14%; 96%) and 5% (-2%; 22%), respectively, over 80-years. Adding a second dose is predicted to reduce varicella and zoster by an additional 22% (0%; 82%) and 6% (0%; 14%), respectively. Most varicella cases prevented by the second dose are breakthrough infections. Although the incremental effectiveness of adding the second dose is highly sensitive to vaccine efficacy and mixing, predictions of the overall benefit of a 2-dose program is relatively robust to model assumptions. Adding a 2-dose program may help guarantee high population-level effectiveness against varicella. However, the incremental benefit of a second dose is highly dependant on the effectiveness of the first dose and its impact on zoster.

Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries

Background

Mathematical modelling of infectious disease is increasingly used to help guide public health policy. As directly transmitted infections, such as influenza and tuberculosis, require contact between individuals, knowledge about contact patterns is a necessary pre-requisite of accurate model predictions. Of particular interest is the potential impact of school closure as a means of controlling pandemic influenza (and potentially other pathogens).

Methods

This paper uses a population-based prospective survey of mixing patterns in eight European countries to study the relative change in the basic reproduction number (R₀ - the average number of secondary cases from a typical primary case in a fully susceptible population) on weekdays versus weekends and during regular versus holiday periods. The relative change in R₀ during holiday periods and weekends gives an indication of the impact collective school closures (and prophylactic absenteeism) may have during a pandemic.

Results

Social contact patterns differ substantially when comparing weekdays to the weekend and regular to holiday periods mainly due to the reduction in work and/or school contacts. For most countries the basic reproduction number decreases from the week to weekends and regular to holiday periods by about 21% and 17%, respectively. However for other countries no significant decrease was observed.

Conclusion

We use a large-scale social contact survey in eight different European countries to gain insights in the relative change in the basic reproduction number on weekdays versus weekends and during regular versus holiday periods. The resulting estimates indicate that school closure can have a substantial impact on the spread of a newly emerging infectious disease that is transmitted via close (non sexual) contacts.

Measures of disassortativeness and their application to directly transmitted infections

We propose a measure of disassortativeness to summarize contact patterns relevant to the transmission of directly transmitted infections. We discuss the properties of this measure, describe standardization relative to homogeneous mixing, and generalize it to multivariate contact structures. We explore some of its properties and apply our methods to serological surveys of close contact infections and surveys of self-reported social contacts obtained in several European countries.