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Storing sparse and repeated data in multivariate Markovian models of tuberculosis spread
R A Bielefeld1, S M Debanne, D Y Rowland
1Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Summary
Sparse storage techniques significantly reduced memory usage in a multivariate Markovian model for tuberculosis (TB) spread in the US. This optimization is crucial for large-scale epidemiological modeling.
Area of Science:
- Computational epidemiology
- Public health informatics
- Disease modeling
Background:
- Multivariate Markovian models are essential for understanding tuberculosis (TB) spread dynamics.
- Previous implementations faced significant memory constraints, limiting model scalability and application.
- Large datasets for population and transition probabilities posed substantial storage challenges.
Purpose of the Study:
- To investigate the efficacy of sparse storage techniques in reducing memory requirements for a TB spread model.
- To optimize computational resources for simulating TB transmission in the United States up to 2010.
- To assess the trade-offs between memory reduction and computational performance (initialization and lookup times).
Main Methods:
- Implementation of sparse storage techniques for population data and year-to-year transition probabilities.
- Comparison of memory usage between sparse and standard array-based implementations.
- Evaluation of data structure initialization and data lookup times for both approaches.
Main Results:
- Memory usage was reduced by 96% for population data and over 99.9% for transition probability data.
- Initialization time for population data increased by a factor of 16.48; lookup time increased by 11.3.
- Initialization and lookup times for transition data showed negligible increases.
Conclusions:
- Sparse storage techniques offer a highly effective method for memory optimization in complex epidemiological models.
- The substantial memory reduction outweighs the moderate increase in computational time for population data.
- This approach enables more feasible and scalable simulations of tuberculosis spread.