Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy
 Valentina Clamer^{1},
 Ilaria Dorigatti^{2},
 Laura Fumanelli^{3},
 Caterina Rizzo^{4} and
 Andrea Pugliese^{1}Email authorView ORCID ID profile
DOI: 10.1186/s1297601600452
© The Author(s) 2016
Received: 1 June 2016
Accepted: 1 October 2016
Published: 12 October 2016
Abstract
Background
Epidemic models are being extensively used to understand the main pathways of spread of infectious diseases, and thus to assess control methods. Schools are well known to represent hot spots for epidemic spread; hence, understanding typical patterns of infection transmission within schools is crucial for designing adequate control strategies. The attention that was given to the 2009 A/H1N1pdm09 flu pandemic has made it possible to collect detailed data on the occurrence of influenzalike illness (ILI) symptoms in two primary schools of Trento, Italy.
Results
The data collected in the two schools were used to calibrate a discretetime SIR model, which was designed to estimate the probabilities of influenza transmission within the classes, grades and schools using Markov Chain Monte Carlo (MCMC) methods. We found that the virus was mainly transmitted within class, with lower levels of transmission between students in the same grade and even lower, though not significantly so, among different grades within the schools. We estimated median values of R _{0} from the epidemic curves in the two schools of 1.16 and 1.40; on the other hand, we estimated the average number of students infected by the first school case to be 0.85 and 1.09 in the two schools.
Conclusions
The discrepancy between the values of R _{0} estimated from the epidemic curve or from the withinschool transmission probabilities suggests that household and community transmission played an important role in sustaining the school epidemics. The high probability of infection between students in the same class confirms that targeting withinclass transmission is key to controlling the spread of influenza in school settings and, as a consequence, in the general population.
Keywords
Influenza Transmission probability in schools Bayesian inference Discretetime SIR epidemic modelBackground
Epidemic models are being extensively used to understand the main pathways of spread of infectious diseases, and thus to assess control methods. Generally, they are fitted to rather aggregated datasets reporting the number of new cases (possibly stratified by age or other variables of interest) in each time interval (often a week, although sometimes daily reports are available, especially at the initial outbreak of an infection). In some cases, data on all individuals of a small community have been available [1], and this has allowed obtaining a better understanding of the persontoperson spread. Still, the question arises of whether small isolated communities are representative of disease spread in more usual contexts. The attention that was given to the 2009 A/H1N1pdm09 influenza pandemic has made it possible to collect detailed data on the epidemic spread in more typical contexts. Schools are well known to represent hot spots for epidemic spread [2–11]. Contact rates within schools are generally higher than outside, as was noticed in [3, 12, 13]. Using detailed data on an outbreak of 2009 pandemic influenza in a school, Cauchemez et al. [14] estimated the different infection probabilities within each class, or grade, and in the whole school, as well as quantified the spread through other household members, and were also able to assess the role of heterogeneities in contact rates. In this work we provide estimates for transmission rates of 2009 A/H1N1pdm09 pandemic influenza at the three levels of class, grade and school by analyzing data on the occurrence of influenzalike illness (ILI) symptoms among pupils of two primary schools in Trento (Italy). The data were collected retrospectively in December 2009, a few weeks after the epidemic peak, through a questionnaire delivered to the parents of the pupils attending the two primary schools. The overall vaccination rate in the agegroup was extremely low in Italy (0.3 %) and the use of antiviral drugs was recommended by the Italian Ministry of Health only for severe cases of pandemic influenza and for symptomatic patients with underlying medical conditions [15]. Although we did not ask specific information about responses to influenza, it seems unlikely that antiviral or vaccine use have significantly affected the epidemic dynamics in the two schools. We developed a discretetime SIR model to analyze the collected data, where the transmission parameters were then estimated via Markov chain Monte Carlo methods, appropriate to make parameter inference in presence of missing data [16, 17]. In order to understand the power of the method, we also applied the algorithm to simulated data, generated to reproduce a school structure, under several hypotheses on the transmission dynamics. This work on simulated data made us, on the one hand, get a better interpretation of the results obtained, showing for instance to which degree parameters are identifiable; on the other hand, assess the loss in accuracy resulting from missing data and other sources of error.
Methods
Data
Summary of the main features emerging from the questionnaires collected in schools A and B in Trento, Italy in 2009
School A  School B  

School size  307  214 
Number of classes  14  10 
Number of responses  260  168 
Number of ILI cases  121  103 
Response rate  85 %  79 % 
Reported Attack rate  46 %  61 % 
Epidemic model and parameter estimation
The epidemic process is described using a discretetime SIR model, with a time step of 1 day. Following [18], we assume that the incubation period (time from infection to symptom occurrence) is on average 2 days and varies between 1 and 3 days, and that the infectiousness profile is as described in [19]. Furthermore, we assume that, after the development of ILI symptoms, a child is kept at home, according to the usual practice in Italy. Combining this assumption with the estimate of the incubation period, we conclude that if a child is infected at school on day t, he/she will be at school and infectious on day t+1; on day t+2 he/she will be infectious and either kept at home, or still at school with probability γ (we estimate γ=0.1 from Figures 1b) and 1d) of [18]); after that, she/he will certainly be kept home and will not contribute further to withinschool transmission. The assumptions are consistent with the estimate in [14] of 1.1 days for the withinschool generation time. Hence, we assume that the school population can be divided into: susceptible individuals S, infectious individuals I (infected children who can transmit the disease, divided into two subcompartments I _{1} and I _{2} depending on them being in the first or second day of infectiousness, respectively) and recovered individuals R (including both recovered children and children kept at home after symptoms onset).

\(I_{t}^{{j,h}}\) the number of infectious students (either in their first or second day of infectiousness) in grade j, class h at time t;

\(I_{t'}^{{j,h}} = \sum _{k\not = h} I_{t}^{{j,k}} \) the number of infectious students in the classes of grade j other than h at time t \(= {I_{t}^{j}} \) (number of infectious students in all classes of grade j) \(  I_{t}^{{j,h}}\) ;

\(I_{t^{\prime \prime }}^{j} = \sum _{i \not = {j, h}} I_{t}^{{i,h}}\) the number of infectious students in grades other than j at time t =I _{ t } (number of infectious individuals in all classes of the school) \(  {I_{t}^{j}}\),
and \(1  p_{t}^{{j,h}} \) is the probability of becoming infectious and moving into compartment I _{1} at time t+1.
Model parameters and variables
Symbol  Description 

q _{ c }  Withinclass infection probability 
q _{ g }  Same grade infection probability 
q _{ s }  Withinschool infection probability 
ε  Outsideschool infection probability 
γ  Probability to remain infective for two days 
I _{ t }  Number of infective subjects at time t in the whole school 
\({I_{t}^{j}}\)  Number of infective subjects at time t in grade j 
\(I_{t}^{j,h}\)  Number of infective subjects at time t in grade j and class h 
\(S_{t}^{j,h}\)  Number of susceptible individuals at time t of grade j and class h 
n _{ j }  Number of classes of grade j 
Model variants
We considered the following two simplifications: model CS (ClassSchool) where we differentiate between withinclass transmission and withinschool transmission only, without considering a separate probability of transmission within the grade; and model S (School only), where we assume that the probability of transmission is the same for all students in the school. We explore a further variant of model CGS (CGSvar), where the probability of infection from outside the school, instead of being constant over time, is assumed to be proportional (through a constant ε) to the ILI incidence in the corresponding week in the province of Trento, as reported by the surveillance system InfluNet of the Italian Institute of Health [21].
Tests on simulated data
We tested the model and the estimation algorithm on simulated data obtained using model CGS under different parameterizations (see the Additional file 1 for details).
Reproduction number
Results
The overall response rate to the questionnaire was 82 %(428/521) and the reported ILI cases were 224 (52 %) (see Table 1). In school A, the first two cases were reported on 16 October 2009 and the last case was reported 56 days later; in school B, the first case was reported on 10 October 2009 and the last case occurred 64 days later (Fig. 1).
We estimated the initial growth rate r both from the (grouped) incidence curve and from the cumulative curve (Fig. 2), selecting those time windows in the growing part of the epidemic for which R ^{2} was sufficiently high (>0.95 for the cumulative curve, >0.7 for the incidence curve); assuming that the infectious period at school T _{ I } is 1.1 day, we obtained a median R _{0} of 1.16 for school A and 1.40 for school B; the overall range of confidence intervals (obtained from the different time windows) is 0.931.43 for school A; 1.081.76 for school B using the fit from incidence curves. The intervals obtained from cumulative curve are much narrower, but may be deceivingly so [25].
Transmission probabilities estimates
Parameters  School A  School B 

q _{ c }, mean [ 95 % CI]  1.39×10^{−2} [ 8.10×10^{−3}−2.03×10^{−2}]  1.96×10^{−2} [ 1.11×10^{−2}−2.89×10^{−2}] 
q _{ g }, mean [ 95 % CI]  4.36×10^{−3} [ 9.61×10^{−4}−8.34×10^{−3}]  4.61×10^{−3} [ 2.98×10^{−4}−1.15×10^{−2}] 
q _{ s }, mean [ 95 % CI]  9.52×10^{−4} [ 2.87×10^{−4}−1.82×10^{−3}]  2.96×10^{−3} [ 1.64×10^{−3}−4.45×10^{−3}] 
ε, mean [ 95 % CI]  3.70×10^{−3} [ 1.95×10^{−3}−5.69×10^{−3}]  2.65×10^{−3} [ 1.37×10^{−3}−4.20×10^{−3}] 
As for comparisons between the two schools, estimates of class and grade transmission probability are similar, as is the probability of transmission from outside the school. On the other hand, estimates of transmission probability within school are rather different (95 %credible intervals barely overlap).
Using these estimates for transmission probabilities, we obtain from Eq. (3) the values of R _{0} shown in Fig. 3 b, with an average of 0.85 in school A and 1.09 in school B. Note that Eq. (3) is based only on withinschool transmission and does not include transmissions to household members or acquaintances; on the other hand, the estimates based on Fig. 2 depend on all infected students, whatever their source of infection.
Model comparison
Model  School A  School B 

CGS (q _{ c },q _{ g },q _{ s },ε)  702.83  757.91 
S (q _{ c }=q _{ g }=q _{ s },ε)  799.91  779.38 
CS (q _{ c },q _{ g }=q _{ s },ε)  774.91  761.16 
CGSvar (q _{ c },q _{ g },q _{ s },ε _{ var })  751.20  426.21 
Finally, we tested the model and the estimation algorithm on simulated data obtained using model CGS under different parameterizations and found that the infection probabilities q _{ c }, q _{ g }, q _{ s } and ε were successfully identified (see Table and Figure S2 in the Additional file 1).
Discussion
We estimated influenza transmission probabilities in a school setting, using the data collected through a retrospective survey conducted in December 2009 in two primary schools and we found that, in both schools, influenza was mainly transmitted within classes (Fig. 3). Same and differentgrade transmission, as well as outsideschool transmission, were all significantly lower than withinclass transmission, with no significant difference between them (Fig. 3).
We found that for both primary schools model CGS (that distinguishes withinclass, samegrade and differentgrade transmission) has the lowest DIC, i.e. is the favorite model overall. According to the DIC, models CGS and CS (that distinguishes withinclass transmission from the general withinschool transmission only) are equivalently good for school B, which reflects the similarity observed in the estimated samegrade and differentgrade transmissions (Fig. 3).
Similar results were obtained in [14], where the transmission probability between students of the same class was estimated to be five times larger than the transmission probability between students of the same grade and, in turn, this was five times larger than the transmission probability between students of different grades. The estimates we obtained are similar, with factors of 34 instead of 5, except for the grade/school ratio in school B, which is just above 1. These results are also consistent with the studies presented in [27, 28], where wearable sensors were used to determine the structure of contacts in schools: these studies found that children spent on average three times more time with children of the same class than with children of other classes. The fact that withinclass transmission is estimated to be higher than withinschool transmission can have implications on the design of school closure policies aimed at mitigating the spread of influenza, especially on evaluating the effectiveness of gradual closures (where single classes close first, then grades and finally the entire school) [27, 29–31].
Another interesting result emerges from the comparison between the two schools involved in the study: while the estimates for withinclass q _{ c } and withingrade q _{ g } transmission probabilites are similar for the two schools, the estimate for schoolwide transmission q _{ s } is remarkably different, as 95 %credible intervals barely overlap. This result can bear on the issue of whether infection transmission should depend on the density or the frequency of infectious individuals [32, 33]. In the model, we have assumed that the transmission probability per individual is constant. Alternatively, we could have adhered to the more usual assumption that transmission probability is inversely proportional to the number of individuals in that setting [34]; in the case of school transmission, we should have used q _{ s }(A)=c/n _{ s }(A) and q _{ s }(B)=c/n _{ s }(B). As n _{ s }(A)≈1.5n _{ s }(B), this results into q _{ s }(B)≈1.5q _{ s }(A). The mean estimated q _{ s } for school B is about 3 times the estimated mean for school A, but 1.5 sits well inside the ratios of values in the 95 %credible intervals. Thus we can conclude that a frequencydependent transmission probability is fully compatible with our findings, whereas data are borderline with respect to rejecting densitydependent transmission.
The estimates of the withinschool R _{0} (mean and 95 % CI: 0.85 (0.591.13) for school A, 1.09 (0.851.37) for school B) lie in the low end of the spectrum of values estimated from influenza spread in schools [35]. Similarly, the estimates of withinclass and withingrade (but not those of withinschool) transmission probabilities obtained in [14] are somewhat higher than ours. It is possible that such differences reflect actual behavioural differences between students in Pennsylvania and in Northern Italy. Alternatively, such differences may be simply due to stochastic variations in the school epidemics, which could also be the reason for the different estimates in schools A and B. In order to explore the plausibility of the latter explanation, in Section D of the Additional file 1 we show the median estimates of R _{0} obtained from different realizations of the stochastic model; although the parameter values are the same for all realizations and yield through (3) R _{0}=1.48, the estimated R _{0} vary between 1.02 and 1.61 in 95 % of simulations. It has also to be remarked that many investigations have focused on schools where an unusually high infection spread had been observed [6] so that possibly the value of R _{0} estimated from them is higher than average. On the other hand, the two schools in our investigation have been chosen purely for convenience; thus, they may be more representative of usual behaviour of infection spread.
In particular, our model estimate of R _{0} lower than 1 for school A highlights the relevance of transmission from outside (with a likely crucial role of households) in maintaining the school outbreak, similarly to the findings of [14]. As information on household cases collected with the questionnaires was inadequate, we relied on two simple alternatives for transmission probability from outside school ε: either a constant, or proportional to the actual influenza incidence in the population. Concerning the latter, we could use only the weekly ILI incidence estimated through the surveillance system InfluNet [21] at the Trento province level, that, on the one hand, is much larger than the territory where the students of the two schools actually live, and, on the other hand, is smaller than the recommended aggregation level of sentinel data that makes them statistically significant; furthermore, the sentinel data may include information from the students in our population study, although their influence should be negligible given the structure of GP and surveillance systems. Despite these limitations, we deem that this choice yields the best available alternative to a constant probability of infection transmission from outside school. The outcomes of the comparison between the two model variants are not unequivocal: for school A model CGS with varying ε yields a larger DIC than model CGS with a constant ε, while for school B the model with varying ε performs much better than the model with constant ε. This statistical result reflects the different pattern in the distribution of cases (see Figure 1), but we cannot find any obvious explanation for it.
The value of γ=0.1 for the probability that the effective (at school) infectious period lasts 2 days has been extrapolated from limited data presented in [18], following the usual practice of estimating the generation time from household studies or other instances where dates of infections can be independently established [33, 36]. In Section D of the Additional file 1 Text, we show that a joint estimation of transmission probability and infectious period, as in the study by White et al. [37], is generally very difficult. Anyway, the main conclusions obtained on the differences between transmission probabilities in the different contexts and between two different schools do not depend on the exact value of γ; changing its value simply results in changing the numerical estimates of q _{ c }, q _{ g } and q _{ s } but not their relative features.
The model we used is very simplified in several respects, from employing a very stylized infectious period, to ignoring school closure during weekends or asymptomatic cases.
It has been estimated [38] that school closures during weekends contribute to decrease the effective reproduction number of about 8 %. Since the generation time in the school setting is short, weekends can break the transmission chain at school thus having an impact on the transmission pattern [27, 28, 34, 38]. On the other hand, household and outside transmission is likely to increase during weekends, as often assumed in modelling studies [34, 39]. Again, for the sake of simplicity, we preferred to avoid the introduction of parameters that may not be easily estimated, but in principle the model could be extended to distinguish between weekdays and weekends.
Our model assumes that all infections are symptomatic and lead to the same level of infectiousness. Indeed, using raw data, the estimate of children showing influenza symptoms is 52 %. This value is comparable to the estimate of 56.9 % infection rate for 2009 A/H1N1pdm09 in primaryschool children in Italy, that was derived from serological data [40]; thus, it seems likely that only a small number of children in those schools got infected with influenza without showing symptoms. On the other hand, it is certainly possible that the fraction of children of the schools considered in our study that got infected was much higher than the national average of 56.9 %. Alternatively, it is possible that some of the children that reported symptoms were not actually infected with influenza virus, while others were infected but did not show symptoms. The lack of serological data prevents from a choice between different alternatives. Accordingly, we decided to use the most parsimonious alternative, namely to neglect asymptomatic infections.
Data from a questionnaire have several other shortcomings: a retrospective survey is prone to recall bias, and this may have affected the parameter estimates and the quality of the results. Furthermore, nonrespondents may differ in several respects from those about which we collected information; for instance, it is possible that parents of students not infected or asymptomatically infected could have chosen not to respond to the questionnaire more often than parents of students with symptomatic infection. We tested the effect of some sources of errors in the analysis of simulated data (see Section D in the Additional file 1), assuming that, beyond missing data, 30 % of reported symptom dates were incorrect. Such errors seem not to bias the resulting estimates, but only to somewhat increase the width of credible intervals; however, other sources of errors (for instance, if no nonrespondent had been infected, or vice versa) could lead to over or underestimation.
Despite these limitations, our analysis provides evidence of different influenza transmission in class and grade. We have shown that the MCMC algorithm used can yield plausible results even starting from incomplete and possibly inaccurate data (such as those derived from questionnaires); further and more detailed data (including serology as well) would be useful to improve the model and the corresponding estimates.
Conclusions
The analysis of data on ILI during the 2009 H1N1 influenza pandemic collected through a questionnaire in two primary schools shows that withinclass transmission has been higher (of a factor 3 to 4) than transmission to students in other classes, confirming the estimates obtained in [14] analyzing a school outbreak in Pennsylvania. The analysis thus confirms the relevance of targeting withinclass transmission for controlling the spread of influenza in school settings, but also provides a quantitative estimate, useful for planning and assessing possible interventions. Since empirical evidence on withinschool infection transmission is scarce, we believe the estimates obtained are quite relevant for the several mathematical models that include specific transmission routes through school contacts.
Several other results have emerged from the analysis:
Estimated average number of infected students per school case was close to 1, suggesting that household and community transmission played an important role in sustaining the school epidemics.
Transmission to students in the school (but in different classes) was lower in the larger school, confirming that number of contacts modulates the transmission strength.
Declarations
Acknowledgements
We gratefully thank the headmaster, Ivana Pulisizzi, and the teachers of the primary schools of Povo and Villazzano, Trento (Italy) for permitting us to run the survey and the students’ parents and carers for their consent and participation. We are grateful to Mimmo Iannelli and Antonella Lunelli for their collaboration in the design and distribution of the questionnaire and digitalization of the data. We thank Gianpaolo Scalia Tomba for useful discussions on the MCMC algorithm.
Funding
ID acknowledges research funding from the European Union EMPERIE project, the Bill and Melinda Gates foundation (grants P20064 and P46908) and the Imperial College Junior Research Fellowship. LF acknowledges funding from the EU Horizon 2020 CIMPLEX project. The data collection and preliminary analysis were performed when AP and ID were supported by European Union FLUMODCONT project.
Availability of data and material
The datasets supporting the conclusions of this article are included within the additional files.
Authors’ contributions
AP conceived the study and designed the questionnaire together with ID and CR. VC performed the analysis of the data, with contributions from ID, LF and AP, and wrote a first draft of the manuscript. All authors contributed to write the final version of the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
At that time, in Italy, the EC’s approval was not necessary for conducting observational studies, according to the ethical requirements of the Italian Ministry of Health. However, we requested a verbal informed consent to participants to use the data collected through questionnaires guaranteeing that the data were anonymized and always presented as aggregated.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
 Camacho A, Ballesteros S, Graham AL, Carrat F, Ratmann O, Cazelles B. Explaining rapid reinfections in multiplewave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study. Proc R Soc B Biol Sci. 2011; 278(1725):3635–643.View ArticleGoogle Scholar
 Calatayud L, Kurkela S, Neave PE, Brock A, Perkins S, Zuckerman M, Sudhanva M, Bermingham A, Ellis J, Pebody R, Catchpole M, Heathcock R, Maguire H. Pandemic (H1N1) 2009 virus outbreak in a school in London, AprilMay 2009: an observational study. Epidemiol Infect. 2010; 138:183–91.View ArticlePubMedGoogle Scholar
 Guinard A, Grout L, Durand C, Schwoebel V. Outbreak of influenza A(H1N1)v without travel history in a school in the Toulouse district, France, June 2009. Euro Surveill. 2009; 14:2335–346.Google Scholar
 Health Protection Agency West Midlands H1N1v Investigation Team: Preliminary descriptive epidemiology of a large school outbreak of influenza A(H1N1)v in the West Midlands, United Kingdom, May 2009. Euro Surveill. 2009; 14:19264.
 KarPurkayastha I, Ingram C, Maguire H, Roche A. The importance of school and social activities in the transmission of influenza A(H1N1)v: England, AprilJune 2009. Euro Surveill. 2009; 14:19311.PubMedGoogle Scholar
 Lessler J, Reich NG, Cummings DAT. Outbreak of 2009 Pandemic Influenza A (H1N1) at a New York City School. N Engl J Med. 2009; 361(27):2628–636.View ArticlePubMedGoogle Scholar
 Smith A, Coles S, Johnson S, Saldana L, Ihekweazu C, O’Moore E. An outbreak of influenza A(H1N1)v in a boarding school in South East England, MayJune 2009. Euro Surveill. 2009; 14:19263.PubMedGoogle Scholar
 Gurav Y, Pawar S, Chadha M, Potdar V, Deshpande A, Koratkar S, Hosmani A, Mishra A. Pandemic influenza A(H1N1) 2009 outbreak in a residential school at Panchgani, Maharashtra, India. Indian J Med Res. 2010; 132(1):67–71.PubMedGoogle Scholar
 Marchbanks TL, Bhattarai A, Fagan RP, Ostroff S, Sodha SV, Moll ME, Lee BY, Chang CCH, Ennis B, Britz P, Fiore A, Nguyen M, Palekar R, Archer WR, Gift TL, Leap R, Nygren BL, Cauchemez S, Angulo FJ, Swerdlow D, Group PW. An Outbreak of 2009 Pandemic Influenza A (H1N1) Virus Infection in an Elementary School in Pennsylvania. Clin Infect Dis. 2011; 52(suppl 1):154–60.View ArticleGoogle Scholar
 Zhao H, Joseph C, Phin N. Outbreaks of influenza influenzalike illness in schools in England and Wales, 2005/06. Euro Surveill. 2007; 12:3–4.Google Scholar
 Baguelin M, Flasche S, Camacho A, Demiris N, Miller E, Edmunds WJ. Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study. PLoS Med. 2013; 10(10):1–19.View ArticleGoogle Scholar
 Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, Salmaso S, Tomba GS, Wallinga J, Heijne J, SadkowskaTodys M, Rosinska M, Edmunds WJ. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLOS Med. 2008; 5:381–91.View ArticleGoogle Scholar
 Fumanelli L, Ajelli M, Manfredi P, Vespignani A, Merler S. Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread. PLoS Comput Biol. 2012; 8(9):1002673.View ArticleGoogle Scholar
 Cauchemez S, Bhattarai A, Marchbanks TL, Fagan RP, Ostroff S, Ferguson NM, Swerdlow D, Sodha SV, Moll ME, Angulo FJ, Palekar R, Archer WR, Finelli L. Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proc Natl Acad Sci. 2011; 108(7):2825–830.View ArticlePubMedPubMed CentralGoogle Scholar
 Rizzo C, Rota MC, Bella A, Giannitelli S, De Santis S, Nacca G, Pompa MG, Vellucci L, Salmaso S, Declich S. Response to the 2009 influenza A(H1N1) pandemic in Italy. Eurosurv. 2010; 15(49):pii=19744.Google Scholar
 O’Neill PD, Balding DJ, Becker NG, Eerola M, Mollison D. Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods. J R Stat Soc Ser C (Applied Stat.) 2000; 49(4):517–42.View ArticleGoogle Scholar
 O’Neill PD, Roberts GO. Bayesian inference for partially observed stochastic epidemics. J R Stat Soc Ser A. 1999; 162:121–9.View ArticleGoogle Scholar
 Ghani AC, Baguelin M, Griffin J, Flasche S, Pebody R, van Hoek AJ, Cauchemez S, Hall IM, Donnelly C, Robertson C, White MT, Barrass I, Fraser C, Bermingham A, Truscott J, Ellis J, Jenkins H, Kafatos G, Garske T, Harris R, McMenamin J, Hawkins C, Phin N, Charlett A, Zambon M, Edmonds WJ, Catchpole M, Leach S, White P, Ferguson NM, Cooper B. The Early Transmission Dynamics of H1N1pdm Influenza in the United Kingdom. PLoS Curr Influ. 2009;:RRN1130. https://www.ncbi.nlm.nih.gov/pubmed/20029668. Accessed 27 Dec 2015.
 Suess T, Buchholz U, Dupke S, Grunow R, an der Heiden M, Heider A, Biere B, Schweiger B, Haas W, Krause G. Shedding and Transmission of Novel Influenza Virus A/H1N1 Infection in Households–Germany, 2009. Am. J. Epidemiol. 2010; 171(11):1157–1164.View ArticlePubMedGoogle Scholar
 Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in Practice. London: Chapman & Hall/CRC; 1996.Google Scholar
 Italian Institute of Health. InfluNet. http://www.iss.it/iflu/. Accessed 5 Apr 2016.
 Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B. 2002; 64(4):583–639.View ArticleGoogle Scholar
 van der Linde A. DIC in variable selection. Stat Neerl. 2005; 59(1):45–56.View ArticleGoogle Scholar
 Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc R Soc B Biol Sci. 2007; 274(1609):599–604.View ArticleGoogle Scholar
 King AA, Domenech de Celles M, Magpantay FMG, Rohani P. Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola. Proc R Soc B Biol Sci. 2015; 282:20150347–0150347.View ArticleGoogle Scholar
 Smith DL, McKenzie FE, Snow RW, Hay SI. Revisiting the Basic Reproductive Number for Malaria and Its Implications for Malaria Control. PLoS Biol. 2007; 5(3):42.View ArticleGoogle Scholar
 Gemmetto V, Barrat A, Cattuto C. Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect Dis. 2014; 14(1):1–10.View ArticleGoogle Scholar
 Stehlé J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton JF, Quaggiotto M, Van den Broeck W, Régis C, Lina B, Vanhems P. HighResolution Measurements of FacetoFace Contact Patterns in a Primary School. PLoS ONE. 2011; 6(8):23176.View ArticleGoogle Scholar
 Fumanelli L, Ajelli M, Merler S, Ferguson NM, Cauchemez S. ModelBased Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics. PLoS Comput Biol. 2016; 12(1):1–15.View ArticleGoogle Scholar
 Cauchemez S, Van Kerkhove MD, Archer BN, Cetron M, Cowling BJ, Grove P, Hunt D, Kojouharova M, Kon P, Ungchusak K, Oshitani H, Pugliese A, Rizzo C, Saour G, Sunagawa T, Uzicanin A, Wachtel C, Weisfuse I, Yu H, Nicoll A. School closures during the 2009 influenza pandemic: national and local experiences. BMC Infect Dis. 2014; 14(1):1–11.View ArticleGoogle Scholar
 Nishiura H, Ejima K, Mizumoto K, Nakaoka S, Inaba H, Imoto S, Yamaguchi R, Saito MM. Costeffective length and timing of school closure during an influenza pandemic depend on the severity. Theor Biol Med Model. 2014; 11(1):1–14.View ArticleGoogle Scholar
 de Jong M, Diekmann O, Heesterbeek H. How does transmission of infection depend on population size? In: Mollison D, editor. Epidemic Model. Their Struct. Relat. to Data. Cambridge: Cambridge University Press: 1995. p. 84–94.Google Scholar
 Cauchemez S, Carrat F, Viboud C, Valleron AJ, Boelle PY, Boëlle PY, Valleron AJ. A {Bayesian MCMC} approach to study transmission of influenza: application to household longitudinal data. Stat Med. 2004; 23(22):3469–487.View ArticlePubMedGoogle Scholar
 Ajelli M, Merler S, Pugliese A, Rizzo C. Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy. Epidemiol Infect. 2004; 139:68–79.View ArticleGoogle Scholar
 Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL, Matrajt L, Potter G, Kenah E, Longini IM. The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus. Science (80.) 2009; 326(5953):729–33.View ArticleGoogle Scholar
 Boëlle PY, Ansart S, Cori A, Valleron AJ. Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review. Influenza Other Respi Viruses. 2011; 5(5):306–16.View ArticleGoogle Scholar
 White LF, Wallinga J, Finelli L, Reed C, Riley S, Lipsitch M, Pagano M. Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA. Influenza Other Respi Viruses. 2009; 3(6):267–76.View ArticleGoogle Scholar
 Ajelli M, Poletti P, Melegaro A, Merler S. The role of different social contexts in shaping influenza transmission during the 2009 pandemic. Sci Rep. 2014; 4:7218.View ArticlePubMedPubMed CentralGoogle Scholar
 Cauchemez S, Valleron AJJ, Boelle PY, Flahault A, Ferguson NM, Boëlle PY, Flahault A, Ferguson NM. Estimating the impact of school closure on influenza transmission from Sentinel data. Nature. 2008; 452(7188):750–4.View ArticlePubMedGoogle Scholar
 Merler S, Ajelli M, Camilloni B, Puzelli S, Bella A, Rota MC, Tozzi AE, Muraca M, Meledandri M, Iorio AM, Donatelli I, Rizzo C. Pandemic Influenza A/H1N1pdm in Italy: Age, Risk and Population Susceptibility. PLoS ONE. 2013; 8(10):1–8.View ArticleGoogle Scholar