Centre de Physique Théorique de Marseille, CNRS UMR 6207, Marseille, France

Hospices Civils de Lyon, Hôpital Edouard Herriot, Service d'Hygiène, Epidémiologie et Prévention, Lyon, France

Université de Lyon; université Lyon 1; CNRS UMR 5558, laboratoire de Biométrie et de Biologie Evolutive, Equipe Epidémiologie et Santé Publique, Lyon, France

Data Science Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy

INSERM, U707, Paris F-75012, France

UPMC Université Paris 06, Faculté de Médecine Pierre et Marie Curie, UMR S 707, Paris F75012, France

Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy

Laboratoire de Physique de l'Ecole Normale Supérieure de Lyon, CNRS UMR 5672, Lyon, France

Abstract

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

Background

The pattern of contacts between individuals is a crucial determinant for the spread of infectious diseases in a population

The starting point of most modeling approaches is the assumption of homogeneous mixing, which assumes that every individual has an equal probability of contacting other individuals in the population

New technologies are now available that allow the tracking of proximity to and interactions between individuals

Finally, little is known about the level of detail that should be incorporated in the modeling effort to perform in practice realistic simulations of epidemics spreading in a population. Very coarse descriptions of human behavior, such as the homogeneous mixing hypothesis, leave out crucial elements. Conversely, extremely detailed information may yield a lack of transparency in the models, making it difficult to discriminate the effect of any particular modeling assumption or component.

The aim of this study was to assess the role of the temporal aspects, heterogeneities and constraints of dynamic contact patterns in shaping the dynamics of an infectious disease in a population using data collected during a 2-day medical conference. In this study, we capitalized on the recent development of a data-collection infrastructure that allows the tracking of face-to-face proximity of individuals at a high temporal resolution

Methods

The ethics committee of Lyon University Hospital approved this study, and all participants gave signed, written informed consent. The data were collected anonymously.

Data collection platform

Contact network measurements are based on the SocioPatterns RFID platform (

All communication (from tag to tag, from tags to receivers, and from receivers to the data storage system) is encrypted. Contact data are stored in encrypted form, and all data management is completely anonymous. Other details on the data-collection infrastructure can be found elsewhere

Data collection in this study

Participants attending the 2009 Annual French Conference on Nosocomial Infections (

Empirical contact networks

To assess the role of the dynamic nature of the network of contacts in the dynamics of disease spread, we considered a network built on the explicit representation of the dynamic interactions between individuals (referred to as the dynamic network; DYN) at the shortest available temporal resolution (20 seconds) against two benchmark networks that are built on progressively lower amounts of information available on the interactions, referred to as the heterogeneous (HET) and homogenous (HOM) networks, respectively.

Firstly, taking advantage of the full spatial and temporal resolution, DYN considered the empirical sequence of successive contact events collected during the congress. Each contact was identified by the RFID identification numbers of the two individuals involved, and by its starting and ending times. The resulting network was a dynamic object encoding the actual chronology and duration of contacts, therefore preserving heterogeneity in the duration of contacts and the causality constraints between events. The latter is particularly important for disease spread, as it may prevent propagation along certain sequences of interactions that would otherwise be allowed in an aggregated static representation of the contact patterns. For example, if a susceptible individual A interacts first with an infectious individual B and then with a susceptible individual C, disease transmission can occur from B to A and then from A to C. If instead, A meets first C and later B, A can become infected from B, but the propagation from B to A and then to C is no longer possible.

The benchmark networks correspond to coarse-graining of the data on a daily scale. The first one, HET, was produced for each conference day by connecting individuals who came in contact during this conference day, thus aggregating all daily dynamic information in a single snapshot, and weighting each link by the total time the two individuals spent in face-to-face presence during the considered day. Therefore, HET included information on the actual contacts between individuals (who has met whom) and on the total duration of these contacts (how long A was in contact with B during the whole day), but disregarded information about the temporal order of contacts. In the previous example, the transmission from A to C could take place in both situations, representing the different sequences of the events. HET was therefore a daily aggregated network in which contacts were aggregated over a day, but the whole neighborhood structure between individuals was kept. As the conference lasted 2 days, the aggregation procedure produced two such networks, one for each day.

By contrast, the HOM network was constructed for each day by connecting individuals who were in face-to-face contact during the conference day, again aggregating all daily dynamic information in a single snapshot, but weighting each link with equal weight, corresponding to the mean duration of contacts between two individuals who have met each other on the same day in the HET network. The HOM construction may correspond to networks constructed by asking each participant to report with whom they have been in contact during the conference day, and then estimating for how long on average this contact lasted. For each conference day, HET and HOM have exactly the same structure of interactions from a topological point of view, but they differ by the assignments of weights on the links.

Generation of contact networks on longer timescales

Because we simulated the spread of a realistic infectious disease, which would be characterized by longer timescales than the data collection period, we introduced three different procedures to longitudinally extend the data-driven network, by preserving some of its features. The simplest procedure consisted of repeating the 2-day recordings. This repetition procedure, denoted as REP, was performed both for the dynamic sequence of contacts (DYN) and consistently for the set of daily HET and HOM networks. In this simple procedure, the same contacts were repeated for each attendee for each simulated sequence of 2 days; that is, the assumption was made that the same attendee always met the same set of other attendees, in the same order, and for the same duration. Although this procedure yields a realistic contact pattern for each single day, it uses only empirical data, and thus such a 'deterministic' repetition is rather unrealistic as time goes on. We therefore considered two additional procedures that might improve this limitation.

The first one, random shuffling (RAND-SH), consisted of producing 2-day sequences by randomly reshuffling the participants' identities, as given by their tag IDs. The overall sequence of contacts was preserved, but each contact was set as occurring between different attendees from one 2-day sequence to the next. DYN networks were then constructed as before, taking into account the 20- second temporal resolution, and the HET and HOM networks were obtained by aggregating the data for each day, as explained above. This method results in more realistic contact patterns being obtained, and avoids the unrealistic repetition of interactions between individuals. However, the RAND-SH procedure completely erases any correlations between the contact patterns of an attendee in successive 2-day sequences, which is also unrealistic. Analysis of the empirical contact networks shows that in fact a correlation did exist between the number of contacts of an attendee in the first and second conference days, and also that a fraction of contacts were repeated from one day to the next.

Therefore, we designed a third procedure (constrained shuffling; CONSTR-SH) for the generation of synthetic contact patterns starting from the 2-day sequence, which constrained the reshuffling to preserve the correlations between the attendees' social activity and the same fraction of repeated contacts during successive days (see Additional file

**Supporting text**. Description of the data-extension procedure CONSTR-SH (constrained shuffling_.

Click here for file

It is important to note that in all cases we preserved the time frame during which data were collected, because no collection occurred outside the conference premises. For this reason, each individual was considered as isolated during the 'night' periods in the DYN network. We therefore also introduced such 'nights' in the HET and HOM networks by 'switching off' the links (that is, considering individuals as isolated) during these periods, thus resembling the circadian pattern encoded by the empirical data.

Epidemiological model

We considered a simple SEIR epidemic model for the simulation of the infectious-disease spread in the population under study, in which no births, deaths or introduction of new individuals occurred. Individuals were each assigned to one of the following disease states: Susceptible (S), Exposed (E), Infectious (I) or Recovered (R).

The model is individual-based and stochastic. Susceptible individuals may contract the disease with a given rate when in contact with an infectious individual, and enter the exposed disease state when they become infected but are not yet infectious themselves. These exposed individuals become infectious at a rate σ, with σ^{-1 }representing the mean latent period of the disease. Infectious individuals can transmit the disease during their infectious period, whose mean duration is equal to ^{-1}. After this period, they enter the recovered phase, acquiring permanent immunity to the disease.

To compare simulation results obtained from the three different networks, we needed to adequately define the rate of infection for a given infectious-susceptible pair, depending on the definition of the networks themselves. β was defined as the constant rate of infection from an infected individual to one of their susceptible contacts on the unitary time step _{AB}
_{AB }

We considered two different disease scenarios for the simulations of disease spread on all networks under study. In particular, the following values were assumed for the duration of the mean latency period (σ^{-1}), mean infectious period (^{-1}) and transmission rate (β): (i) σ^{-1 }= 1 days, ^{-1 }= 2 days and β = 3.10^{-4}/s (very short incubation and infectious periods); and (ii) σ^{-1}= 2 days, ^{-1 }= 4 days and β = 15.10^{-5}/s (short incubation and infectious periods). These sets of parameter values were chosen to maintain the same value of β/

Analysis of the empirical contact networks and of the simulation results

To describe the empirical contact networks, we calculated the number of contacts, the mean duration of contacts, the mean degree of a node (defined as the number of distinct individuals encountered by the individual under scrutiny), the mean clustering coefficient (which describes the local cohesiveness), the mean shortest path (defined as the mean number of links to cross to go from one node to another, and the correlation between the properties of the nodes in the aggregated networks of the first and second conference day). For this analysis, we measured the Pearson correlation coefficients between the degree of an individual in the first and second day, and between the time spent in interaction in the first and second day.

The comparison of the epidemic outbreaks in the three networks under study was performed by analyzing several parameters, namely the final size of the epidemic, the number of infectious individuals during the epidemic peak, the time of the peak, and the duration of the epidemic.

Since we aim at assessing the impact on spreading phenomena of the contact patterns, of their dynamic nature, and of the available amount of details on their dynamics we also estimated the reproductive number _{0}, defined as the expected number of secondary infections from an initial infected individual in a completely susceptible host population _{0 }
_{0 }as the mean, over different realizations, of the number of secondary cases from the single initial randomly chosen infectious individual. Mean _{0 }values and variances were then compared for the three networks (DYN, HET and HOM) and the three data-extension procedures (REP, RAND-SH and CONSTR-SH) under study.

Results

In total, 28,540 face-to-face contacts between 405 attendees at a 2-day conference were recorded, and the probability distribution of the duration of these contacts was plotted (Figure

Distribution of the contact duration between any two people on a log-log scale

**Distribution of the contact duration between any two people on a log-log scale**. The mean duration was 49 seconds, with SD 112 seconds.

In the daily contact networks, the mean degree of a node was close to 30, with a distribution decaying exponentially for large numbers. The mean clustering coefficient was 0.28, much larger than the mean value of 0.07 obtained for a random network of the same size and mean degree. The network was also a small world, with a mean shortest path of 2.2 (snapshots of the network of the first conference day are shown; see Additional File

**Supplementary figures 1-3**. Snapshots of the contact graph between the 405 attendees for the first conference day.

Click here for file

The link weights, by contrast, had a broad distribution, with a mean cumulated duration of the interaction between two attendees of 2 minutes. The total duration spent in contact by any attendee also had a broad distribution, with a mean of 75 minutes. The Pearson correlation coefficient between the degree of an individual in the first and second day was 0.37, and that between the total time spent in interaction in the first and second day was 0.52. The fraction of repeated contacts in the second day with respect to the first was 12%, and was independent of the degree.

The distributions of _{0 }for the three networks using the REP procedure were also plotted (Figure _{0}, depending on the scenarios and the network type are shown: Figure _{0}, together with larger variances, were observed in the HOM network compared with the HET and DYN networks.

Distribution of R_{0 }for the homogenous (HOM), heterogenous (HET#) and dynamic (DYN) networks with the parameters σ^{-1 }= 2 days, ^{-1 }= 4 days and β = 15.10^{-5}/s, in the repetition (REP) procedure

**Distribution of R _{0 }for the homogenous (HOM), heterogenous (HET) and dynamic (DYN) networks with the parameters σ^{-1 }= 2 days, **.

Boxplots showing the distributions of R_{0 }according to the different scenarios and network types

**Boxplots showing the distributions of R _{0 }according to the different scenarios and network types**. The bottom and top of the rectangular boxes correspond to the 25th and 75th quantile of the distribution, the horizontal lines to the median, and the ends of the whiskers give the 5th and 95th percentiles. Very short latency, very short infectiousness scenario: σ

**Supplementary table 1**. Mean values, variances and 90% CI of R_{0 }according to the different scenarios and network types.

Click here for file

The distribution of the final number of cases for the three networks and the REP data-extension procedure are also shown (Figure

Distribution of the final number of cases for the three networks with the parameters σ^{-1 }= 2 days, ^{-1 }= 4 days and β = 15.10^{-5}/s (short latency, short infectiousness), in the repetition (REP) procedure

**Distribution of the final number of cases for the three networks with the parameters σ ^{-1 }= 2 days, v^{-1 }= 4 days and β = 15.10^{-5}/s (short latency, short infectiousness), in the repetition (REP) procedure**.

The distribution of the final number of cases for the three networks was analyzed for the various parameters of the SEIR model and for the various extrapolation scenarios (Table

Distribution of the final number of cases for the three network types according to the four scenarios (5000 runs, dynamic contact network of 405 participating attendees)

**1 to 10 final cases (AR* ≤ 2.5%)**

**11 to 40 final cases (2.5% < AR ≤ 10%)**

**> 40 final cases (AR > 10%)**

**Scenarios**

**Parameters**

**Network ^{a}**

**Runs, n**

**% of run with no secondary cases**

**% run**

**Mean cases, n**

**90% CI**

**% run**

**Mean cases, n**

**90% CI**

**% run**

**Mean cases, n**

**90% CI**

REP^{b}

Very short latency

σ^{-1 }= 1 days

DYN

5000

47.3

18.2

2.3

1 to 6

0.7

15.9

11 to 22

33.8

208

169 to 242

Very short infectiousness

^{-1 }= 2 days

HET

5000

46.4

17.7

2.4

1 to 7

0.8

17.9

11 to 32

35.2

210

171 to 243

Transmission rate

β = 3.10^{-4}/s

HOM

5000

41.7

11.7

2.2

1 to 6

0.2

16.6

11 to 30

46.3

285

257 to 310

Short latency

σ^{-1 }= 2 days

DYN

5000

45.3

17.0

2.2

1 to 7

0.4

18.3

11 to 38

37.3

214

178 to 246

Short infectiousness

^{-1 }= 4 days

HET

5000

44.4

16.4

2.2

1 to 6

0.6

16.8

11 to 27

38.6

216

178 to 248

Transmission rate

β = 15.10^{-5}/s

HOM

5000

38.7

13;2

2.1

1 to 6

0.1

13.2

11 to 15

48.1

288

262 to 310

RAND-SH^{c}

Very short latency

σ^{-1 }= 1 days

DYN

5000

44.8

19.4

2.8

1 to 8

2.2

17.9

11 to 31

33.6

278

223 to 319

Very short infectiousness

^{-1 }= 2 days

HET

5000

45.4

18.5

2.6

1 to 7

1.6

17.6

11 to 30

34.5

284

241 to 322

Transmission rate

β = 3.10^{-4}/s

HOM

5000

39.9

14.3

2.6

1 to 7

0.8

15.7

11 to 28

45.0

324

291 to 350

Short latency

σ^{-1 }= 2 days

DYN

5000

40.6

18.6

2.7

1 to 8

1.4

19.2

11 to 31

39.4

297

254 to 331

Short infectiousness

^{-1 }= 4 days

HET

5000

39.5

18.0

2.7

1 to 8

1.3

16.7

11 to 30

41.2

300

259 to 333

Transmission rate

β = 15.10^{-5}/s

HOM

5000

35.9

15.7

2.5

1 to 7

0.9

17.0

11 to 31

47.5

325

293 to 352

CONSTR-SH^{d}

Very short latency

σ^{-1 }= 1 days

DYN

5000

45.4

17.7

2.4

1 to 7

1.0

17.0

11 to 28

35.8

240

194 to 278

Very short infectiousness

^{-1 }= 2 days

HET

5000

46.8

16.5

2.4

1 to 7

0.8

19.0

11 to 33

35.9

245

202 to 282

Transmission rate

β = 3.10^{-4}/s

HOM

5000

39.8

13.3

2.3

1 to 6

0.7

15.4

11 to 21

46.2

308

278 to 334

Short latency

σ^{-1 }= 2 days

DYN

5000

40.9

18.2

2.3

1 to 6

0.8

16.8

11 to 34

40.2

258

215 to 292

Short infectiousness

^{-1 }= 4 days

HET

5000

41.3

16.8

2.3

1 to 7

0.5

14.0

11 to 25

41.4

257

213 to 292

Transmission rate

β = 15.10^{-5}/s

HOM

5000

35.7

14.8

2.4

1 to 7

0.4

15.2

11 to 21

49.2

314

284 to 339

^{a}Networks: DYN = dynamic; HET = heterogenous; HOM = homogenous.

** ^{b}**Repetition.

** ^{c}**Random shuffling.

** ^{d}**Constrained shuffling.

**Supplementary figure 4**. Box plots showing the distributions of the number of final cases when the final attack rate is larger than 10%, according to the different scenarios and network types.

Click here for file

Regarding the peak times of disease spread in the various cases (Figure

Boxplots (symbols as in Fig 3.) showing the distributions of the prevalence peak time t_{peak }according to the different scenarios and network types

**Boxplots (symbols as in Fig 3.) showing the distributions of the prevalence peak time t _{peak }according to the different scenarios and network types**. Only runs with attack rate (AR) > 10% were taken into account. Very short latency, very short infectiousness scenario: σ

**Supplementary table 2**. Mean values, variances and 90% CI of the prevalence peak time t_{peak }according to the different scenarios and network types.

Click here for file

Using the evolution in time of the number of infectious and recovered individuals for the different data-extension procedures and for the two sets of SEIR parameters, the temporal behavior of disease spread was analyzed (Figure

Temporal evolution of the spreading process for the three networks with the parameters σ^{-1 }= 1 days, ^{-1 }= 2 days and β = 3.10^{-4}/s (very short latency, very short infectiousness)

**Temporal evolution of the spreading process for the three networks with the parameters σ ^{-1 }= 1 days, v^{-1 }= 2 days and β = 3.10^{-4}/s (very short latency, very short infectiousness)**. (

Distribution of the final number of cases for the three networks with the parameters σ^{-1 }= 2 days, ^{-1 }= 4 days and β = 15.10^{-5}/s (short latency, short infectiousness) in the repetition (REP) procedure

**Distribution of the final number of cases for the three networks with the parameters σ ^{-1 }= 2 days, v^{-1 }= 4 days and β = 15.10^{-5}/s (short latency, short infectiousness) in the repetition (REP) procedure**.

Interesting differences were seen in the results of simulations on datasets extended with different procedures (Figure _{∞ }was larger. In fact, we systematically found _{∞}(REP) < _{∞}(CONSTR-SH) < _{∞}(RAND-SH), and the more the identities of the tags were shuffled, the more efficient was the spread.

Discussion

Using a recently developed data collection technique deployed during a 2-day conference involving 405 volunteers, we measured the dynamics of contact (face-to-face) interactions between individuals during such a social event. We used the data to compare the simulated spread of communicable diseases on this dynamic network (DYN) and on two networks, one heterogeneous (HET) and one homogeneous (HOM), obtained by aggregating the dynamic network at two distinct levels of precision. To compensate for the relatively short duration of the observation period (2 days), we designed two different models to construct dynamical contact networks spanning an extended time period during which the spread of an infectious disease could be simulated.

The broad distributions of the various network characteristics reported in this study were consistent with those seen in other contexts

In the three networks, disease extinction occurred as frequently (between 36% and 47%) as large outbreaks (between 34% and 49%). Outbreaks tended to be explosive (attack rate between 51% and 80%), consistently with previous work

The comparison between disease spread in the HET and DYN networks provides insights into whether temporal constraints due to the precise sequence of the contacts might affect the propagation of disease. Given two individuals, the overall expected probability of a transmission occurring during the interval ΔT is the same in both cases (that is, _{AB}), so the only difference is that the contact is not continuously present in the DYN network, but it may be intermittent and repeated only during the actual recorded contacts. This introduces time constraints on the paths that the infectious agent can follow between individuals in the DYN network, which may slow down disease spread on the DYN network compared with the HET network. However, this slowing down of infection and the differences in the final number of cases between the HET and DYN networks were too small to be relevant for the simulations investigated here. The similarity between the spreading behaviors in the HET and DYN networks was independent of the different procedures used to extend the initial 2-day dataset. These procedures created successive artificial 'days' which differed from each other by various amounts, that is, with a different level of repetition of contacts from one day to the next. The robustness of the comparison between HET and DYN therefore indicates that the observed similarity between the spreading on the HET and DYN networks is due to the discrepancy between the timescales considered for propagation (of the order of days), and the temporal resolution and the contact durations (of 20 seconds and of the order of minutes up to a few hours, respectively). The total time spent in contact by each pair of individuals was in this context sufficient to describe precisely the propagation pattern, as shown by the peak time and the final number of cases. Therefore, for the simulation of diseases such as those considered in this study, contact information at a daily resolution might be enough to characterize disease spread, and the precise order of the sequence of contacts might not be needed. However, this would not be the case for extremely fast-spreading processes, as shown in previous work

Finally, the difference between the results obtained for the different procedures REP, RAND-SH and CONSTR-SH shows the importance of knowledge of the respective fractions of repeated and new contacts between successive days

Compared with other approaches

Limitations

Unsupervised data-collection systems based on RFID infrastructures, such as the one presented here

Another issue, well known in the field of social networks, is due to the partial sampling of the population. Of the 1,200 attendees at this conference, 405 (34%) participated in the data collection. Consequently, only these attendees were taken into account in the model of disease spread, whereas they were in fact also in contact with the non-participating attendees. Previous investigation

Finally, the limited period (2 days) of data collection made it necessary to generate artificially longer datasets by different procedures in order to model the spread of pathogens on realistic timescales. Deployment of the measuring infrastructure on much longer timescales is planned so as to validate such generation procedures and to measure their effect.

Conclusions

Despite the limitations described above, the present study emphasizes the effects of contact heterogeneity on the dynamics of communicable diseases. On the one hand, the small differences between simulated spread on both the HET and DYN networks shows that taking into account the very detailed actual time ordering of the contacts between individuals, with a time resolution of minutes, does not seem to be essential to describe disease spread on a timescale of several days or weeks. On the other hand, the large differences in disease spread in the HOM network emphasize the need to include detailed information about the heterogeneity of contact duration (compared with an assumption of homogeneity) to model disease spread, as also found previously

In this context, a data collection infrastructure such as the one used in this study seems to be very effective, as it gives access to the level of information needed, and also allows the simulation of very fast-spreading processes characterized by timescales comparable with those intrinsic to social dynamics, where even the precise ordering of contact events becomes crucial. These measurements should be also extended to other contexts in which individuals interact closely in different ways, such as workplaces, schools or hospitals

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

JS, NV, AB, CC, VC, LI, CR, JFP, WVdB and PV conceived of and designed the experiments; NV, AB, CC, CR, JFP, NK, WVdB and PV performed the data collection; JS, NV, AB, CC, VC, LI and JFP analyzed the data; and JS, NV, AB, CC, VC, LI, JFP and PV wrote the paper. All authors read and approved the final manuscript.

Acknowledgements

We acknowledge the contribution of all partners of the SocioPatterns project. We are grateful to the organizers of the conference of the Société Française d'Hygiène Hospitalière (SFHH). VC is partially supported by the ERC Ideas contract number ERC-2007-Stg204863 (EPIFOR) and by the FET projects, EC-ICT contract number 231807 (EPIWORK) and EC-FET contract number 233847 (DYNANETS). LI is partially supported by the FET project Dynanets. This project was partly supported by La Société Française d'Hygiène Hospitalière and GOJO France. This study was partly supported by a grant of the Programme de Recherche, A(H1N1) co-ordinated by the Institut de Microbiologie et Maladies Infectieuses. We thank all the attendees at the conference who volunteered to participate in the data collection. We would like to thank the reviewers for their constructive comments, which have substantially improved the presentation of this manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here: