Accelerating spatially explicit simulations of spread of Lyme disease

Abstract

The factors influencing spread of Lyme disease are often studied using computer-based simulations and spatially explicit models. However, simulating large and complex models is a time consuming task, even when parallel simulation techniques are employed. In an endeavor to accelerate such simulations, an alternative approach involving dynamic (i.e., during simulation) changes to spatial resolution of the model via a novel methodology called dynamic component substitution (DCS) is proposed. Changes to the resolution are performed such that the total number of interactions between the entities in the model is optimized, thereby improving overall performance but introducing minor (< /spl plusmn/1%) deviations in the results. This paper explores the effectiveness and issues involved in applying DCS to accelerate sequential and parallel simulations of spatially explicit Lyme disease models. The paper also presents a brief description of the simulation environment along with empirical results. Our experiments indicate that performance improvements can be obtained using the proposed approach.