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

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          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.

          Abstract

          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.

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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proc. Natl. Acad. Sci. U.S.A
          pnas
          pnas
          PNAS
          Proceedings of the National Academy of Sciences of the United States of America
          National Academy of Sciences
          0027-8424
          1091-6490
          22 November 2016
          7 November 2016
          7 November 2016
          : 113
          : 47
          : 13420-13425
          Affiliations
          [1] aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD 21205;
          [2] bMathematical Modelling of Infectious Diseases Unit, Institut Pasteur , Paris 75015, France;
          [3] c Centre National de la Recherche Scientifique , URA3012, Paris 75015, France;
          [4] dCenter of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur , Paris 75015, France;
          [5] e Center for Communicable Diseases , International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh;
          [6] f Institute of Epidemiology Disease Control & Research , Mohakhali, Dhaka 1212, Bangladesh;
          [7] gDepartment of Biology, University of Florida , Gainesville, FL 32603
          Author notes
          1To whom correspondence should be addressed. Email: hsalje@ 123456jhu.edu .

          Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved September 30, 2016 (received for review July 15, 2016)

          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.

          2E.S.G. and S.C. contributed equally to this work.

          Author information
          http://orcid.org/0000-0002-6054-3571
          Article
          PMC5127331 PMC5127331 5127331 201611391
          10.1073/pnas.1611391113
          5127331
          27821727
          520090f9-5e03-4942-8e1a-ac4d86303fff

          Freely available online through the PNAS open access option.

          History
          Page count
          Pages: 6
          Funding
          Funded by: HHS | Centers for Disease Control and Prevention (CDC) 100000030
          Award ID: 5U01CI000628
          Funded by: HHS | National Institutes of Health (NIH) 100000002
          Award ID: R01AI10293901A1
          Categories
          Biological Sciences
          Ecology

          data augmentation,Bayesian,chikungunya,outbreaks,spatial spread

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