The 2005 ESA Annual Meeting and Exhibition
December 15-18, 2005
Ft. Lauderdale, FL

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Friday, December 16, 2005 - 8:30 AM
0451

Dengue transmission and control in an age-structured human population

Sharon L. Minnick, slminnick@ucdavis.edu1, Amy C. Morrison, acmorrison@ucdavis.edu1, Tadeusz J. Kochel, kochel@nmrcd.med.navy.mil2, James G. Olson, olson@nmrcd.med.navy.mil2, and Thomas W. Scott, twscott@ucdavis.edu1. (1) University of California, Davis, Entomology, One Shields Ave, Davis, CA, (2) US Naval Medical Research Center, Lima, Peru, C/O American Embassy-Peru, Unit number 3800, APO AA 34031-0008, Lima, Peru

Data from DNA fingerprinting of bloodmeals indicate that Aedes aegypti take a greater proportion of bloodmeals from adults than from children. Age-dependent biting rates could result in age-dependent force of infection. If force of infection increases with age then we would expect the control effort necessary to interrupt virus transmission to be underestimated by an age-independent model. To test this hypothesis, we estimated age specific force of infection for dengue viruses in Iquitos, Peru from a cohort of schoolchildren and their families using longitudinal serological data from 1999-2003. Force of infection was found to be significantly higher in older age groups during the interepidemic period with 18% of susceptibles over 15 years seroconverting and only 9% of susceptibles under 9 years seroconverting (p<0.001). However, during the dengue 3 epidemic beginning in late 2001, the force of infection dramatically increased for all ages with nearly 40% seroconverting. In that different epidemiological circumstance, the age difference was not significant, possibly due to the sampling interval of six months. A susceptible-infectious-immune simulation model will be presented to estimate the basic reproductive number of dengue viruses in an age-structured popuation and to determine the effect of heterogeneous transmission on control effort efficacy.


Species 1: Diptera Culicidae Aedes aegypti (yellow fever mosquito)
Keywords: Arbovirus, Disease modeling