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

Area of Science:

• Epidemiology
• Social Network Analysis
• Infectious Disease Modeling

Background:

• Social contact patterns are crucial for understanding infectious disease transmission.
• Traditional methods for measuring contacts include indirect epidemiologic data and direct diary studies.
• Existing methods have limitations in capturing comprehensive contact patterns across diverse populations.

Purpose of the Study:

• To explore the utility of time-use surveys for measuring social contact patterns.
• To develop a methodology for estimating time-of-exposure matrices from time-use data.
• To apply this methodology to model the transmission of varicella (chickenpox).

Main Methods:

• Developed a novel methodology to estimate time-of-exposure matrices using time-use survey data.
• Applied the methodology to US time-use data from 1987-2003.
• Estimated age-specific transmission parameters for varicella using time-of-exposure and seroprevalence data (1988-1994).

Main Results:

• The estimated time-of-exposure matrix showed significant age-based assortativeness.
• Identified distinct exposure peaks between individuals separated by one generation (e.g., parents and children).
• Models incorporating estimated transmission parameters successfully replicated observed varicella infection patterns.

Conclusions:

• Time-use surveys offer a valuable tool for quantifying social contact patterns relevant to disease transmission.
• The developed methodology provides a robust framework for estimating exposure and transmission parameters.
• The approach is highly adaptable for use with international time-use data, enhancing epidemiological research globally.