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Applications of Information Theory to Epidemiology

• 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...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2021
Matèries:
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
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science::PS Biology, life sciences
Accés en línia:ONIX_20210501_9783036503165_315
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Sumari:• 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.

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