  Mathematical and computational modeling approaches are increasingly used as
quantitative tools in the analysis and forecasting of infectious disease
epidemics. The growing need for realism in addressing complex public health
questions is however calling for accurate models of the human contact patterns
that govern the disease transmission processes. Here we present a data-driven
approach to generate effective descriptions of population-level contact
patterns by using highly detailed macro (census) and micro (survey) data on key
socio-demographic features. We produce age-stratified contact matrices for 277
sub-national administrative regions of countries covering approximately 3.5
billion people and reflecting the high degree of cultural and societal
diversity of the focus countries. We use the derived contact matrices to model
the spread of airborne infectious diseases and show that sub-national
heterogeneities in human mixing patterns have a marked impact on epidemic
indicators such as the reproduction number and overall attack rate of epidemics
of the same etiology. The contact patterns derived here are made publicly
available as a modeling tool to study the impact of socio-economic differences
and demographic heterogeneities across populations on the epidemiology of
infectious diseases.
