Search Results for author:
Found 7 papers, 3 papers with code
Towards inferring network properties from epidemic data
However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference.
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio
The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery.
Hypergraphon Mean Field Games
We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and the notion of hypergraphons, which are obtained as limits of large hypergraphs.
Dynamic Survival Analysis for non-Markovian Epidemic Models
We present a new method for analyzing stochastic epidemic models under minimal assumptions.
A Machine Learning Model for Nowcasting Epidemic Incidence
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data.
Incorporating age and delay into models for biophysical systems
We show how the limiting PDE system can be used for the purpose of further model reductions and for devising efficient simulation algorithms.
Populations and Evolution 92B05
Inverse Reinforcement Learning in Swarm Systems
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data.
