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Found 3 papers, 2 papers with code
On the calibration of compartmental epidemiological models
Finally, we discuss how the calibration of parameters of epidemiological compartmental models is an emerging field that has the potential to improve the accuracy of disease modeling and public health decision-making.
Planning Multiple Epidemic Interventions with Reinforcement Learning
We illustrate how to effectively apply state-of-the-art actor-critic reinforcement learning algorithms (PPO and SAC) to search for plans that minimize overall costs.
Stochastic Package Queries in Probabilistic Databases
We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides.
Decision Making
Decision Making Under Uncertainty
+1
Databases
