Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Scaling Epidemic Inference on Contact Networks: Theory and Algorithms
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Studies scalable epidemic inference on contact networks, proposing theory and algorithms as an alternative to costly Monte Carlo simulation (details limited to the abstract excerpt).
Problem
Monte Carlo simulations used to predict infection peaks, estimate reproduction numbers, and evaluate NPIs in computational epidemiology are effective but computationally expensive at scale.
Approach
Based on the abstract only: a theory-grounded, non-MC inference approach for epidemic spread on contact networks intended to scale better than MC-based methods. Specific mechanism not stated in the provided text.
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