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Epidemic Modeling Approach Could Speed Pandemic Simulations

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A sparsified U.S. network based on effective resistance sampling.

The research helps reduce the computational cost of simulating large-scale pandemics, while preserving important details about disease spread,

Credit: Santa Fe Institute

A new epidemic modeling approach developed by Santa Fe Institute researchers Alexander Mercier, Samuel Scarpino and Cristopher Moore could significantly accelerate pandemic simulations.

The researchers used a sparsification technique to identify the most critical network links for disease proliferation.

They employed data from the U.S. Census Bureau to design a mobility network describing nationwide commuting patterns and determined the effective resistance sparsification method to be optimal for reducing network density while maintaining overall disease-spread dynamics.

The resulting network contained 25 million fewer edges, or roughly 7% of the original U.S. commuting network, which reduced computation time for modeling epidemics by more than 90%.

From Santa Fe Institute
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Abstracts Copyright © 2023 SmithBucklin, Washington, DC, USA


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