Massive ensembles of simulations are needed to accurately capture the state space of epidemiological outbreak models. This is because the outbreak has tremendous spatial dynamism and the intervention actions have significant temporal effects. The spread of infectious diseases depends not only on the interactions among the hosts in a confined community, but also on the spatial reach of hosts among different communities. Further, intervention policies to control the epidemic may depend on various constraints such as budget, community size, movement patterns among communities. In this work, we present EpiClone, a fast and efficient spatial epidemic simulator to execute millions of different epidemic simulations at various spatial resolutions. Based on our simulation cloning framework called CloneX, EpiClone runs reaction-diffusion models of epidemiological outbreaks. EpiClone uses GPUs effectively to execute a large number of what-if decision scenarios stemming from different intervention policies. EpiClone has been benchmarked to scale up to 1024 GPUs with speedup of over two orders of magnitude compared to replicated runs.
Significance and Impact
Epidemic simulation is an important topic in the field of computational epidemiology. Simulation of many epidemic scenarios including outbreaks, and interventions are of paramount importance to prevent future epidemics management. EpiClone offers an efficient framework to conduct many parallel simultaneous epidemic solutions. We are currently investigating to provide more analytical support on EpiClone from the conducted experiments.
- Designed and implemented distributed memory parallel algorithm for parallel epidemic simulations
- Provided exhaustive experimental analysis of the system
EpiClone is a simulation cloning application for spatial epidemic simulation which is efficient both in terms of runtime and memory. EpiClone has been shown to scale to thousands of GPU with significant speedup. We are focusing to add significant analytical abilities to EpiClone for further analysis of diseases propagation under various if-else conditions.