Using time-use data to parameterize models for the spread of close-contact infectious diseases

Emilio Zagheni*, Francesco C. Billari, Piero Manfredi, Alessia Melegaro, Joel Mossong, W. John Edmunds

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    98 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1082-1090
    Number of pages9
    JournalAmerican Journal of Epidemiology
    Volume168
    Issue number9
    DOIs
    Publication statusPublished - Nov 2008

    Keywords

    • Chickenpox
    • Communicable diseases
    • Data interpretation, statistical
    • Endemic diseases

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