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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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (238 p.) |
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| 100 | 1 | |a Hughes, Gareth |4 edt | |
| 245 | 1 | 0 | |a Applications of Information Theory to Epidemiology |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (238 p.) | ||
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| 520 | |a • 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. | ||
| 546 | |a English | ||
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| 653 | |a Ebola model | ||
| 653 | |a Caputo derivative | ||
| 653 | |a Caputo-Fabrizio derivative | ||
| 653 | |a Atangana-Baleanu derivative | ||
| 653 | |a numerical results | ||
| 653 | |a entropy | ||
| 653 | |a information theory | ||
| 653 | |a multiple diagnostic tests | ||
| 653 | |a mutual information | ||
| 653 | |a relative entropy | ||
| 653 | |a balance | ||
| 653 | |a Jensen-Shannon divergence | ||
| 653 | |a observational study | ||
| 653 | |a selection bias | ||
| 653 | |a probability | ||
| 653 | |a forecast | ||
| 653 | |a likelihood ratio | ||
| 653 | |a positive predictive value | ||
| 653 | |a negative predictive value | ||
| 653 | |a diagnostic information | ||
| 653 | |a Shannon entropy | ||
| 653 | |a epidemic model | ||
| 653 | |a transient behavior | ||
| 653 | |a vaccination and treatment intervention controls | ||
| 653 | |a diagnostic test | ||
| 653 | |a evaluation | ||
| 653 | |a ROC curve | ||
| 653 | |a PROC curve | ||
| 653 | |a binormal | ||
| 653 | |a prevalence | ||
| 653 | |a Bayes' rule | ||
| 653 | |a leaf plot | ||
| 653 | |a expected mutual information | ||
| 653 | |a predictive ROC curve | ||
| 653 | |a PV-ROC curve | ||
| 653 | |a SS-ROC curve | ||
| 653 | |a SS/PV-ROC plot | ||
| 653 | |a empirical | ||
| 653 | |a urinary bladder cancer | ||
| 653 | |a sensitivity | ||
| 653 | |a specificity | ||
| 653 | |a HIV/AIDS epidemic | ||
| 653 | |a regression model | ||
| 653 | |a Newton-Raphson procedure | ||
| 653 | |a Fisher scoring algorithm | ||
| 653 | |a time series | ||
| 653 | |a early detection | ||
| 653 | |a Asiatic citrus canker | ||
| 653 | |a latent class | ||
| 653 | |a field diagnostic | ||
| 653 | |a scent signature | ||
| 653 | |a direct assay | ||
| 653 | |a deployment | ||
| 653 | |a average mutual information | ||
| 653 | |a stochastic processes | ||
| 653 | |a deterministic dynamics | ||
| 776 | |z 3-0365-0316-1 | ||
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| 700 | 1 | |a Hughes, Gareth |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2024-02-03 13:08:55 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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