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

Thursday, 10th of November 2016

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

 Authors

1. Henrik Salje^a,^b,^c,^d,¹,
2. Justin Lessler^a,
3. Kishor Kumar Paul^e,
4. Andrew S. Azman^a,
5. M. Waliur Rahman^e,^f,
6. Mahmudur Rahman^f,
7. Derek Cummings^a,^g,
8. Emily S. Gurley^e,², and
9. Simon Cauchemez^b,^c,^d,²
10. 1.   ^aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
11. 2.   ^bMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France;
12. 3.   ^cCentre National de la Recherche Scientifique, URA3012, Paris 75015, France;
13. 4.   ^dCenter of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur, Paris 75015, France;
14. 5.   ^eCenter for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh;
15. 6.   ^fInstitute of Epidemiology Disease Control & Research, Mohakhali, Dhaka 1212, Bangladesh;
16. 7.   ^gDepartment of Biology, University of Florida, Gainesville, FL 32603
17. Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved September 30, 2016 (received for review July 15, 2016)

Significance

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.

Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8–17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2–0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77–113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2–1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.

• ↵¹To whom correspondence should be addressed. Email: hsalje@jhu.edu.
• ↵²E.S.G. and S.C. contributed equally to this work.
• Author contributions: H.S., K.K.P., M.W.R., M.R., and E.S.G. designed research; H.S., K.K.P., M.W.R., M.R., and E.S.G. performed research; H.S., J.L., A.S.A., D.C., and S.C. contributed new reagents/analytic tools; H.S., A.S.A., and S.C. analyzed data; and H.S., E.S.G., and S.C. wrote the paper.
• The authors declare no conflict of interest.
• This article is a PNAS Direct Submission.
• This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1611391113/-/DCSupplemental.

Freely available online through the PNAS open access option.