A modified Susceptible-Infected-Recovered model for observed under-reported incidence data.

ABSTRACT: Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the trans

SUBMITTER: Trejo I 

PROVIDER: S-EPMC8827465 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature