Modeling Infectious Disease Parameters Based on Serological and Social Contact Data : A Modern Statistical Perspective
Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force...
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| 主要な著者: | , , , , , |
|---|---|
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
New York, NY :
Springer New York
2012.
Cham : Springer Nature |
| シリーズ: | Statistics for Biology and Health
63 |
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Modeling infectious disease parameters based on serological and social contact data, a modern statistical perspective, Niels Hens, Ziv Shkedy, Marc Aerts...[et al.], 2012, Berlin, Springer, 1 vol. (XVI-298 p.), Statistics for biology and health, 978-1-4614-4071-0 • Modeling Infectious Disease Parameters Based on Serological and Social Contact Data, Texte imprimé, 9781461440734 • Modeling Infectious Disease Parameters Based on Serological and Social Contact Data, Texte imprimé, 9781489987969 |
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| 020 | |a 9781461440727 | ||
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| 082 | |a 519.5 | ||
| 100 | 1 | |a Hens, Niels. | |
| 245 | 1 | 0 | |a Modeling Infectious Disease Parameters Based on Serological and Social Contact Data : |b A Modern Statistical Perspective |c by Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. |
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c 2012. | ||
| 490 | 0 | |a Statistics for Biology and Health |v 63 |x 2197-5671 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Mathematical models for infectious diesease -- The static model -- The dynamic model -- The stochastic model -- Implementation of models in MATLAB -- Data sources for modelling infectious diseases -- Estimation from serological data -- Parametric models for teh prevalence and the force of infection -- Non-parametric approaches to model the prevalence and force of infection -- Semi-parametric approaches to model the prevalence and force of infection -- A Bayesian approach -- Modelling the prevalence and the force of infection direction from antibody levels -- Modelling multivariate serological data -- Estimation from other data sources -- Estimating mixing patterns and Ro in a heterogenous population -- Modelling in a homogeneous population -- Modelling in a heterogeneous population -- Modelling AIDS outbreak data -- Modelling hepatitis C among injection drug users -- Modelling dengue -- Modelling bovine herpes virus in cattle. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology. It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt University) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. This book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration. This book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with software guidance, such as R packages, and with data, as far as they can be made publicly available. . | ||
| 700 | 1 | |a Shkedy, Ziv. |4 aut | |
| 700 | 1 | |a Aerts, Marc. |4 aut | |
| 700 | 1 | |a Faes, Christel. |4 aut | |
| 700 | 1 | |a Van Damme, Pierre, |d 19..- |4 aut | |
| 700 | 1 | |a Beutels, Philippe. |4 aut | |
| 776 | 0 | |0 165599855 |t Modeling infectious disease parameters based on serological and social contact data |o a modern statistical perspective |f Niels Hens, Ziv Shkedy, Marc Aerts...[et al.] |d 2012 |c Berlin |n Springer |p 1 vol. (XVI-298 p.) |s Statistics for biology and health |z 978-1-4614-4071-0 | |
| 776 | 0 | |t Modeling Infectious Disease Parameters Based on Serological and Social Contact Data |b Texte imprimé |z 9781461440734 | |
| 776 | 0 | |t Modeling Infectious Disease Parameters Based on Serological and Social Contact Data |b Texte imprimé |z 9781489987969 | |
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