Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model
From MaRDI portal
Publication:6589238
DOI10.1111/BIOM.13901zbMATH Open1543.62579MaRDI QIDQ6589238
David B. Dunson, David A. Buch, James E. Johndrow
Publication date: 19 August 2024
Published in: Biometrics (Search for Journal in Brave)
compartmental modelBayesianmechanistic modelCOVID-19latent Gaussian processsemiparametric nonlinear mixed-effects model
Cites Work
- Title not available (Why is that?)
- Modeling epidemics using cellular automata
- Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
- A contribution to the mathematical theory of epidemics.
- Stochastic epidemic models and their statistical analysis
- Limits of epidemic prediction using SIR models
- Forecasting seasonal influenza with a state-space SIR model
- The mathematics of infectious diseases
- A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
- Model-Based Geostatistics
- Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model
- Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments
- Efficient Gaussian process regression for large datasets
- Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data
- Discussion of “Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data”
- An adaptive Metropolis algorithm
- A Review of Multi‐Compartment Infectious Disease Models
- Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model
This page was built for publication: Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6589238)