How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study.
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.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; hsalje@jhu.edu.|Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France.|Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.|Center of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur, Paris 75015, France.
Henrik
H
Salje
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
Justin
J
Lessler
Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.
Kishor Kumar
0000-0002-6054-3571
KK
Paul
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
Andrew S
AS
Azman
Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.|Institute of Epidemiology Disease Control & Research, Mohakhali, Dhaka 1212, Bangladesh.
M Waliur
MW
Rahman
Institute of Epidemiology Disease Control & Research, Mohakhali, Dhaka 1212, Bangladesh.
Mahmudur
M
Rahman
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.|Department of Biology, University of Florida, Gainesville, FL 32603.
Derek
D
Cummings
Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.
Emily S
ES
Gurley
Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France.|Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.|Center of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur, Paris 75015, France.
Simon
S
Cauchemez
D001459:Bangladesh / Q000453:epidemiology; D050936:Basic Reproduction Number; D065632:Chikungunya Fever / Q000635:transmission*; D002980:Climate; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005191:Family Characteristics; D005260:Female; D005843:Geography; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012919:Social Behavior; D013997:Time Factors
Bayesian; chikungunya; data augmentation; outbreaks; spatial spread