title: Modelling the effects of population structure on childhood disease: the case of varicella.
abstract: 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.
authors.affiliation: Université Pierre et Marie Curie-Paris 6, Paris, France. silhol@u707.jussieu.fr
authors.forename: Romain
authors.initials: R
authors.lastname: Silhol
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Boëlle
mesh_terms: D002644:Chickenpox / Q000453:epidemiology* / Q000635:transmission; D002648:Child; D002675:Child, Preschool; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003710:Demography; D014645:Herpesvirus 3, Human; D006801:Humans; D015994:Incidence; D007223:Infant; D008954:Models, Biological; D012574:Schools / Q000706:statistics & numerical data