Comparison of two stochastic spatial dynamical models of Aedes aegypti

MARCELO JAVIER OTERO; VICTORIA ROMEO AZNAR; MATHIEU LEGROS; THOMAS SCOTT; ALUN LLOYD; FRED GOULD; HERNÁN GUSTAVO SOLARI

Carlos Paz, Córdoba

Congreso; XIII Latin American Workshop on Nonlinear Phenomena; 2013

Universidad Nacional de Córdoba, FaMAF / IFEG-CONICET

Mosquito population models are useful tools for guiding the development of successful vector control programs. Models can differ in their level of complexity, which affects in an elaborate fashion their predictive ability and robustness. In order to investigate the impact of this complexity in model assumptions, we present a direct comparison of two detailed, spatially-explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue and yellow fever. Both models describe the mosquito?s biological and ecological characteristics, but differ in their level of complexity. We compared the predictions of these models in two selected climatic settings, a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbed conditions in both settings, by adjusting parameters regulating densities in each model (density of breeding sites and amount of nutritional resources). We show that the models differ in their sensitivity to environmental conditions (temperature and rainfall), and trace differences to specific model assumptions, e.g., egg hatching and larval competition. Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more widely under strongly seasonal Buenos Aires conditions. We model control interventions in selected areas by simulating killing of larvae and/or adults. We show that predictions of population recovery differ substantially, an effect likely related to model assumptions regarding larval development and (direct or delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model complexity on population dynamics predictions in unperturbed and perturbed conditions.