Inference of the generalized-growth model via maximum likelihood estimation: a reflection on the impact of overdispersion

DOI10.1016/j.jtbi.2019.110029zbMath1425.92183OpenAlexW2976898066WikidataQ90377684 ScholiaQ90377684MaRDI QIDQ2010885

Tapiwa Ganyani, Christel Faes, Niel Hens

Publication date: 28 November 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2019.110029

zbMATH Keywords

maximum likelihood estimationoverdispersionsub-exponential growthearly epidemic growth phasegeneralized-growth model

Mathematics Subject Classification ID

Epidemiology (92D30)

Related Items (1)

A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic

Uses Software

• Stata
• pomp
• incidence

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Modeling and inference for infectious disease dynamics: a likelihood-based approach
• Time series analysis via mechanistic models
• A generalization of the Kermack-McKendrick deterministic epidemic model
• Bootstrap methods: another look at the jackknife
• Tutorial on maximum likelihood estimation
• A comparison of inferential methods for highly nonlinear state space models in ecology and epidemiology
• Comparative assessment of parameter estimation methods in the presence of overdispersion: a simulation study
• Model Selection and Model Averaging
• Analysis of Overdispersed Count Data by Mixtures of Poisson Variables and Poisson Processes
• Applied Statistical Inference
• Bias‐Corrected Maximum Likelihood Estimator of the Negative Binomial Dispersion Parameter

This page was built for publication: Inference of the generalized-growth model via maximum likelihood estimation: a reflection on the impact of overdispersion