We prove that the exact closure of SIR pairwise epidemic equations on a configuration model network is possible if and only if the degree distribution is Poisson, Binomial, or Negative Binomial.
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.
We present a new method for analyzing stochastic epidemic models under minimal assumptions.
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.
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
To shed light on this process, we develop a method for forecasting elections from the perspective of dynamical systems.
Physics and Society Social and Information Networks Dynamical Systems Adaptation and Self-Organizing Systems Populations and Evolution