Modeling Direct Transmission Diseases Using Parallel Bitstring Agent-Based Models

Agent-based models (ABMs) are gaining importance over traditional epidemiological modeling due to advances in computing technology and catalyzed by the need for detailed epidemiological analysis of emergent diseases. Unfortunately, the advantages of ABMs are realized at the cost of significantly large execution times and high memory consumption for large-scale simulations. To address the memory issue, we designed and implemented an ABM using an innovative feature: the bitstring approach, in which the attributes of each agent are represented by an array of hits instead using traditional data structures. To cope with the high computational demands, we developed a parallel version of model aiming multicore CPUs and GPUs architectures using Thrust parallel algorithms library. The results of our model were validated comparing them with data of a spread of Influenza A in the Cascavel City, South Brazil, occurred in 2009. The model presented good qualitative results and an excellent performance on GPUs. The application of bitstring technique is proved to be relevant in economy of memory, allowing to store the same attributes using 41% less memory space and improving the data copy time between CPU and GPU up to 52%.