We present an epidemiological compartment model, SAIR(S), that explicitly captures the dynamics of asymptomatic infected individuals in an epidemic spread process. We first present a group model and then discuss networked versions. We provide an investigation of equilibria and stability properties for these models, and present simulation results illustrating the effects of asymptomatic-infected individuals on the spread of the disease. We also discuss local isolation effects on the epidemic dynamics in terms of the networked models. Finally, we provide initial parameter estimation results based on simple least-squares approaches and local test-site data.
We present an analysis of epidemiological compartment models that explicitly capture the dynamics of asymptomatic but infectious individuals. Our models can be viewed as an extension to classic SIR models, to which a distinct Asymptomatic compartment is added. We discuss both a group compartment model capturing a Susceptible-Asymptomatic-Infected-Recovered-Susceptible (SAIRS) epidemic process, and also introduce and evaluate SAIRS dynamics evolving over networks. We investigate equilibria and stability properties that include both disease-free and endemic equilibria states for these models, providing sufficient conditions for convergence to these equilibria. Model parameter estimation results based on local test-site and Peoria county clinic data are given, and a number of simulations illustrating the effects of asymptomatic-infected individuals and network structure on the spread and/or persistence of the disease are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.