How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study

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  • Henrik Salje
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
  • Justin Lessler
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
  • Kishor Kumar Paul
    Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh;
  • Andrew S. Azman
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
  • M. Waliur Rahman
    Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh;
  • Mahmudur Rahman
    Institute of Epidemiology Disease Control & Research, Mohakhali, Dhaka 1212, Bangladesh;
  • Derek Cummings
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
  • Emily S. Gurley
    Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh;
  • Simon Cauchemez
    Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France;

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<jats:title>Significance</jats:title> <jats:p>Although the determinants of infectious disease transmission have been extensively investigated in small social structures such as households or schools, the impact of the wider environment (e.g., neighborhood) on transmission has received less attention. Here we use an outbreak of chikungunya as a case study where detailed epidemiological data were collected and combine it with statistical approaches to characterize the multiple factors that influence the risk of infectious disease transmission and may depend on characteristics of the individual (e.g., age, sex), of his or her close relatives (e.g., household members), or of the wider neighborhood. Our findings highlight the role that integrating statistical approaches with in-depth information on the at-risk population can have on understanding pathogen spread.</jats:p>

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