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Within each population, there exists a great amount of variability in an
individual's response to infectious disease. Factors such as susceptibility to
pathogens, probability of developing symptoms, mortality and ongoing immunity,
to name but a few of the possibilities, heavily influence disease outcomes. Our
research seeks to elucidate how different subpopulations and socially mediated
interactions between them influence infectious disease dynamics in the
population as a whole.
Chui KKH, Wenger JB, Cohen SA, Naumova EN. Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs. PLoS ONE. 2011. 6(2): e14683. doi:10.1371/journal.pone.0014683
Cohen SA, Chui KKH, Naumova EN. Measuring disease burden in the older population using the slope-intercept method for population log-linear estimation (SIMPLE). Statistics in Medicine. 2011. Feb 28;30(5):480-8. doi: 10.1002/sim.3886. Epub 2011 Feb 2.
Egorov AI, Sempértegui F, Estrella B, Egas J, Naumova EN, Griffiths JK. The effect of Helicobacter pylori infection on growth velocity in young children from poor urban communities in Ecuador. Int J Infect Dis. 2010 Sep;14(9):e788-91. Epub 2010 Jul 17.
Mor SM, Tumwine JK, Ndeezi G, Srinivasan MG, Kaddu-Mulindwa DH, Tzipori S, Griffiths JK. Respiratory cryptosporidiosis in HIV-seronegative children, Uganda: potential for respiratory transmission. Clin Infect Dis. 2010;50(10):1366-72.
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