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Volume 28, Number 10—October 2022
Research

Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens

Nicky McCreeshComments to Author , Mbali Mohlamonyane, Anita Edwards, Stephen Olivier, Keabetswe Dikgale, Njabulo Dayi, Dickman Gareta, Robin Wood, Alison D. Grant, Richard G. White, and Keren Middelkoop
Author affiliations: London School of Hygiene and Tropical Medicine TB Centre, London, UK (N. McCreesh, A.D. Grant, R.G. White); The Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa (M. Mohlamonyane, R. Wood, K. Middelkoop); University of KwaZulu-Natal Africa Health Research Institute, Durban, South Africa (A. Edwards, A.D. Grant); Africa Health Research Institute, Durban (S. Olivier, K. Dikgale, N. Dayi, D. Gareta); University of Cape Town Department of Medicine, Cape Town (R. Wood, K. Middelkoop); University of the Witwatersrand School of Public Health, Johannesburg, South Africa (A.D. Grant)

Main Article

Figure 1

Summary of data used to estimate age-mixing matrices for a study of social contact patterns for airborne transmission of respiratory pathogens, KwaZulu Natal and Western Cape Provinces, South Africa, 2019. Diagram showing how age-mixing matrices relevant for the transmission of droplet infections, airborne infections, and Mycobacterium tuberculosis were estimated using empirical data on close contact numbers, close contact time, and casual contact time.

Figure 1. Summary of data used to estimate age-mixing matrices for a study of social contact patterns for airborne transmission of respiratory pathogens, KwaZulu Natal and Western Cape Provinces, South Africa, 2019. Diagram showing how age-mixing matrices relevant for the transmission of droplet infections, airborne infections, and Mycobacterium tuberculosis were estimated using empirical data on close contact numbers, close contact time, and casual contact time.

Main Article

Page created: August 15, 2022
Page updated: September 20, 2022
Page reviewed: September 20, 2022
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