Applications of Information Theory to Epidemiology

doi:10.3390/books978-3-0365-0317-2

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https://mdpi.com/books/pdfview/book/3590

Contributor(s)

Hughes, Gareth (editor)

Language

English

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Abstract

• Applications of Information Theory to Epidemiology collects recent research findings on the analysis of diagnostic information and epidemic dynamics. • The collection includes an outstanding new review article by William Benish, providing both a historical overview and new insights. • In research articles, disease diagnosis and disease dynamics are viewed from both clinical medicine and plant pathology perspectives. Both theory and applications are discussed. • New theory is presented, particularly in the area of diagnostic decision-making taking account of predictive values, via developments of the predictive receiver operating characteristic curve. • New applications of information theory to the analysis of observational studies of disease dynamics in both human and plant populations are presented.

URI

https://directory.doabooks.org/handle/20.500.12854/68569

Keywords

Ebola model; Caputo derivative; Caputo–Fabrizio derivative; Atangana–Baleanu derivative; numerical results; entropy; information theory; multiple diagnostic tests; mutual information; relative entropy; balance; Jensen–Shannon divergence; observational study; selection bias; probability; forecast; likelihood ratio; positive predictive value; negative predictive value; diagnostic information; Shannon entropy; epidemic model; transient behavior; vaccination and treatment intervention controls; diagnostic test; evaluation; ROC curve; PROC curve; binormal; prevalence; Bayes’ rule; leaf plot; expected mutual information; predictive ROC curve; PV-ROC curve; SS-ROC curve; SS/PV-ROC plot; empirical; urinary bladder cancer; sensitivity; specificity; HIV/AIDS epidemic; regression model; Newton–Raphson procedure; Fisher scoring algorithm; time series; early detection; Asiatic citrus canker; latent class; field diagnostic; scent signature; direct assay; deployment; average mutual information; stochastic processes; deterministic dynamics; n/a