Social contact patterns are a critical explanatory factor of the spread of close-contact infectious agents. Both indirect (via observed epidemiologic data) and direct (via diaries that record at-risk events) approaches to the measurement of contacts by age have been proposed in the literature. In this paper, the authors discuss the possibilities offered by time-use surveys to measure contact patterns and to explain observed seroprevalence profiles. The authors first develop a methodology to estimate time-of-exposure matrices, and then they apply it to time-use data for the United States (1987-2003). Finally, the authors estimate age-specific transmission parameters for varicella, commonly known as "chickenpox," from age-specific time-of-exposure and seroprevalence data (United States, 1988-1994). The estimated time-of-exposure matrix reveals a strong element of assortativeness by age. In addition, there are peaks of exposure between people who were born one generation apart (i.e., parents and their children). Models based on the estimated age-specific transmission parameters fit the observed patterns of infection of endemically circulating varicella in a satisfactory way. The availability of time-use data for a large number of countries and their potential to supplement contact surveys make the methods developed extremely valuable and suitable for implementation in several different contexts.

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Knowledge of the determinants of infectious disease transmission is a public health priority as it allows the design of optimal control strategies for endemic or emerging infections. We analyse a detailed dataset on contact patterns across five European countries and use available serological profiles for varicella and parvovirus B19 infections to identify the types of contact that may be most relevant for transmission. We show that models informed by contact data fit well the observed serological profiles of both infections. We find that intimate types of contacts explain the pattern of acquisition of serological markers by age better than other types of social contacts. We observe similar patterns in each of the countries analysed, suggesting that there are consistent biological mechanisms at work.

What does statistics have to offer science and society, in this age of massive data, machine learning algorithms, and multiple online sources of tools for data analysis? I recall a few situations where statistics made a real difference and reinforced the impact of our discipline on society. Sometimes the difference lay in the insightful analysis and inference enabled by groundbreaking methods in our field like hypothesis testing, likelihood ratios, Bayesian models, jackknife, and bootstrap. But perhaps more often, the impacts came from thoughtful analyses before data were collected, and the questions that arose after the statistical analysis. The impact of understanding the problem, designing the experiment and data collections, conducting the pilot surveys, and raising important questions, is substantial. Through sensible explorations following formal statistical procedures, statisticians have made contributions in many domains. In this presentation, I recall some examples which made a long-lasting impact. Some of them, like randomization in clinical trials, known and familiar to all, are so ingrained in our practice that the role of statistics has been forgotten. Others may be less familiar but nonetheless benefited greatly from the critical input of statisticians. All remind us that our field remains today not only relevant but critical to science and society.