title: Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy.
abstract: 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 influenza-like illness (ILI) symptoms in two primary schools of Trento, Italy.
  
  
  
  The data collected in the two schools were used to calibrate a discrete-time 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 R0from 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.
  
  
  
  The discrepancy between the values of R0estimated from the epidemic curve or from the within-school 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 within-class transmission is key to controlling the spread of influenza in school settings and, as a consequence, in the general population.
authors.affiliation: Department of Mathematics, University of Trento, Via Sommarive 14, Trento, 38123, Italy.
authors.forename: Valentina
authors.initials: V
authors.lastname: Clamer
authors.affiliation: MRC Centre for Outbreak Analysis & Modelling, Department of Infectious Disease Epidemiology, Imperial College, London, UK.
authors.forename: Ilaria
authors.initials: I
authors.lastname: Dorigatti
authors.affiliation: Center for Information Technology, Bruno Kessler Foundation, Trento, Italy.
authors.forename: Laura
authors.initials: L
authors.lastname: Fumanelli
authors.affiliation: Istituto Superiore di Sanità, Roma, Italy.
authors.forename: Caterina
authors.initials: C
authors.lastname: Rizzo
authors.affiliation: Department of Mathematics, University of Trento, Via Sommarive 14, Trento, 38123, Italy. andrea.pugliese@unitn.it.
authors.forename: Andrea
authors.identifier: 0000-0002-3512-8560
authors.initials: A
authors.lastname: Pugliese
mesh_terms: D003198:Computer Simulation; D006801:Humans; D015994:Incidence; D053118:Influenza A Virus, H1N1 Subtype / Q000502:physiology*; D007251:Influenza, Human / Q000453:epidemiology* / Q000635:transmission* / Q000821:virology; D007558:Italy; D008962:Models, Theoretical; D058873:Pandemics / Q000706:statistics & numerical data*; D011336:Probability; D012574:Schools / Q000706:statistics & numerical data*
keywords: Bayesian inference; Discrete-time SIR epidemic model; Influenza; Transmission probability in schools