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Bridging machine learning and compartment models to predict an epidemic
Chau, M. T.
2022-10-11
epidemiology
10.1101/2022.10.07.22280853
medRxiv
Show abstract
This work proposes a Physics-informed Machine learning method to model and emulate the progression of COVID-19. Besides the high accuracy, lower data need, and interpretability, the method also estimates hidden parameters from data, which are useful for policymakers to flatten the curve and better understand public healthcare system.
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50% of probability mass above
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.