5.3
Using a dynamic population model of the Lyme disease vector to identify risk areas for Lyme disease spread in the face of climate change
Nick Hume Ogden, Université de Montréal, Saint-Hyacinthe, QC, Canada; and M. Bigras-Poulin, C. J. O'Callaghan, I. K. Barker, L. R. Lindsay, A. Maarouf, K. E. Tomic, D. Waltner-Toews, and D. Charron
It is thought likely that the geographic range of many terrestrial arthropod species, including vectors of zoonotic diseases, will spread northwards with projected climate change. The propensity for arthropod species distributions to be affected by climate change depends in part on how sensitive the population biology of the species is to temperature changes. Recent studies by our group have indicated that development rates between most instars of the tick vector of Lyme disease, Ixodes scapularis, increase significantly with temperature. These studies suggest too that development of adult ticks from nymphs is additionally determined by temperature-independent diapause. To understand the potential effects of climate change on I. scapularis distributions, as well as the distributions of pathogens transmitted by this vector, we have developed a dynamic population model of I. scapularis populations. In this model, tick development rates and host-seeking activity of ticks are determined by temperature data from Canadian meteorological stations using temperature-development relationships obtained in the laboratory and validated in the field. Incorporation of tick mortality rates and other population data from Canadian habitats allowed location-specific simulations for existing or hypothetical tick populations. The simulations provided a threshold of temperature conditions (using mean annual degree-days >0°C as an index), and a scale of likelihood, for tick establishment in Canadian habitats allowing us to map current and projected potential tick distributions. We performed sensitivity analyses of individual parameters, as well as global sensitivity analyses, to identify the data required to improve the predictive power of the model. The use of the model outcomes as an index for projections of Lyme disease risk under climate change scenarios, and future enhancement of the model are discussed. .
Session 5, Human Biometeorology: Modeling and Prediction
Thursday, 26 August 2004, 8:30 AM-9:45 AM
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