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Mathematical and computational modeling approaches are increasingly used asquantitative tools in the analysis and forecasting of infectious diseaseepidemics. The growing need for realism in addressing complex public healthquestions is however calling for accurate models of the human contact patternsthat govern the disease transmission processes. Here we present a data-drivenapproach to generate effective descriptions of population-level contactpatterns by using highly detailed macro (census) and micro (survey) data on keysocio-demographic features. We produce age-stratified contact matrices for 277sub-national administrative regions of countries covering approximately 3.5billion people and reflecting the high degree of cultural and societaldiversity of the focus countries. We use the derived contact matrices to modelthe spread of airborne infectious diseases and show that sub-nationalheterogeneities in human mixing patterns have a marked impact on epidemicindicators such as the reproduction number and overall attack rate of epidemicsof the same etiology. The contact patterns derived here are made publiclyavailable as a modeling tool to study the impact of socio-economic differencesand demographic heterogeneities across populations on the epidemiology ofinfectious diseases.

Inferring high-resolution human mixing patterns for disease modeling