Model selection and evaluation based on emerging infectious disease data sets including A/H1N1 and ebola

DOI10.1155/2015/207105zbMath1335.92097DBLPjournals/cmmm/LiuTX15OpenAlexW1792367798WikidataQ35801346 ScholiaQ35801346MaRDI QIDQ278131

Wendi Liu, Sanyi Tang, Yanni Xiao

Publication date: 2 May 2016

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/207105

zbMATH Keywords

Bayes factorsA/H1N1ebolaemerging infectious diseases

Mathematics Subject Classification ID

Epidemiology (92D30)

Related Items (4)

Phenomenological and mechanistic models for predicting early transmission data of COVID-19 ⋮ Mathematical modeling of the propagation of COVID-19 pandemic waves in the world ⋮ Model selection and parameter estimation for an improved approximate Bayesian computation sequential Monte Carlo algorithm ⋮ Hospital preparedness during epidemics using simulation: the case of COVID-19

Cites Work

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• A unified approach to the Richards-model family for use in growth analyses: why we need only two model forms
• The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda
• Model Selection and Multimodel Inference
• Introducing Monte Carlo Methods with R

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