Inferring high-resolution human mixing patterns for disease modeling

• Published in: Nature communications Vol. 12; no. 1; pp. 323 - 12
• Main Authors: Mistry, Dina, Litvinova, Maria, Pastore y Piontti, Ana, Chinazzi, Matteo, Fumanelli, Laura, Gomes, Marcelo F. C., Haque, Syed A., Liu, Quan-Hui, Mu, Kunpeng, Xiong, Xinyue, Halloran, M. Elizabeth, Longini, Ira M., Merler, Stefano, Ajelli, Marco, Vespignani, Alessandro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.01.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 692/700/478/174; Age; Age Factors; Australia - epidemiology; Basic Reproduction Number; Census; China - epidemiology; Cluster Analysis; Communicable Diseases - epidemiology; Communicable Diseases - transmission; Computer applications; Demographics; Disease control; Disease transmission; Economic models; Epidemics; Epidemiology; Etiology; Humanities and Social Sciences; Humans; Impact analysis; Infectious diseases; Influenza, Human - epidemiology; Influenza, Human - transmission; Mathematical analysis; Mathematical models; Matrix methods; Models, Statistical; multidisciplinary; Polls & surveys; Public health; Questions; Realism; Science; Science (multidisciplinary); Sociodemographics; Surveys and Questionnaires; Australia; China
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-20544-y

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 population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those 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. The growing need for realism in addressing complex public health questions calls for accurate models of the human contact patterns that govern disease transmission. Here, the authors generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features.