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International Biometric Society USING DIFFERENT MODEL SELECTION TOOLS TO ELUCIDATE THE TRANSMISSION POTENTIAL OF VZV IN EUROPE FROM A SOCIAL CONTACT PERSPECTIVE Santermans, E.1, Goeyvaerts, N.1,2, Melegaro, A.4, Gay, N.4,5, Edmunds, J.6, Aerts, M.1, Beutels, P.2,3, and Hens, N.1,2 1 Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium 2 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium 3 School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia 4 DONDENA Centre for Research on Social Dynamics, Bocconi University, Milan, Italy 5 Modelling and Economics Unit, Public Health England, London, United Kingdom 6 London School of Hygiene & Tropical Medicine, London, United Kingdom The basic reproduction number 𝑅0 and the effective reproduction number 𝑅 are pivotal parameters in infectious disease epidemiology, quantifying the transmission potential of an infection in a population. We estimate both parameters from 13 pre-vaccination serological data sets on varicella zoster virus (VZV) in 12 European countries under the assumption of endemic equilibrium. This means that varicella may undergo cyclical epidemics, however fluctuating around a stationary average over time. Under this assumption, the expected value of the effective reproduction number is 1. Estimating transmission rates for an airborne infection such as VZV requires assumptions on the underlying age-specific mixing patterns. The basic reproduction number 𝑅0 has been shown to be highly sensitive to these mixing assumptions. Serological surveys, however, do not provide complete information about these mixing patterns, since they reflect the rate at which susceptible individuals become infected, but not who is infecting them. We address this unidentifiability by informing the mixing pattern with data from population-based social contact surveys, assuming transmission rates are proportional to contact rates [1,2]. Rates of close contact lasting at least 15 minutes are estimated using a bivariate smoothing approach, while non-parametric bootstrap is used to assess variability. Further, we evaluate how constant and age-specific proportionality assumptions affect the estimated 𝑅0 values and the fit to the serology using the inferred effective reproduction number as a model eligibility criterion combined with AIC as a model selection criterion. In all countries, primary infection with VZV most likely occurs in early childhood, but there is substantial variation in transmission potential with 𝑅0 ranging from 2.8 in England and Wales to 7.6 in the Netherlands. Two non-parametric methods, the maximal information coefficient (MIC) [3] and a random forest approach, are used to explore these differences in 𝑅0 by means of country-specific characteristics as for example childcare attendance, population density and average absolute humidity. The results indicate that higher day care participation rates and higher pre-school attendance rates relate to larger varicella transmission potential. This illustrates the need to consider epidemiological differences between European countries when parameterizing mathematical models to inform VZV vaccination. They also reveal for the first time based on serological data, which factors are important in explaining epidemiological differences between European countries. [1] B. Ogunjimi, N. Hens, N. Goeyvaerts, M. Aerts, P. Van Damme, and P. Beutels. Using empirical social contact data to model person to person infectious disease transmission: an illustration for varicella. Mathematical Biosciences, 218:80-87, 2009. [2] J. Wallinga, P. Teunis, and M. Kretzschmar. Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. American Journal of Epidemiology, 164:936944, 2006. [3] D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher, and P. C. Sabeti. Detecting novel associations in large data sets. Science, 334:1518-1524, 2011. International Biometric Conference, Florence, ITALY, 6 – 11 July 2014