Statistical models for infectious disease surveillance counts:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
München
Hut
2007
|
| Ausgabe: | 1. Aufl. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: München, Univ., Diss., 2006 |
| Beschreibung: | IV, 124 S. graph. Darst., Kt. |
| ISBN: | 9783899635041 |
Internformat
MARC
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| 245 | 1 | 0 | |a Statistical models for infectious disease surveillance counts |c Mathias Hofmann |
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| 264 | 1 | |a München |b Hut |c 2007 | |
| 300 | |a IV, 124 S. |b graph. Darst., Kt. | ||
| 336 | |b txt |2 rdacontent | ||
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| 500 | |a Zugl.: München, Univ., Diss., 2006 | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
1 Introduction 1
1.1 Data 2
1.2 Models 2
1.2.1 Models for chronic diseases 2
1.2.2 Models for infections diseases 4
1.3 Scope of thesis G
2 Bayesian inference 9
2.1 Markov chain Monte Carlo 10
2.1.1 Metropolis Hastings algorithm 11
2.1.2 Single component Metropolis Hastings 11
2.1.3 Gibbs sampler 11
2.1.4 The reversible jump algorithm 12
2.1.5 Effective sample size 12
2.1.G Deviance information criterion 13
2.2 Sequential Monte Carlo 14
2.2.1 Particle filter 14
2.2.2 Forward backward algorithm 17
3 Bayesian changepoint models 23
3.1 Model extensions 25
3.2 Application to coal mining disaster data 2(i
3.3 Posterior distribution 28
3.4 Markov state space form of the changepoint model 29
3.5 Markov structure of the changepoints 31
3.6 Estimation of the changepoint model 32
ii CONTENTS
3.6.1 Reversible jump MCMC 32
3.6.2 Particle filter 35
3.6.3 Forward backward algorithm 36
4 A model for multivariate infectious disease counts 41
4.1 Introduction 41
4.2 The univariate case 42
4.2.1 The endemic component 43
4.2.2 The epidemic component 45
4.3 Estimation by MCMC 45
4.3.1 Alternative representation using auxiliary variables 45
4.3.2 Update of the parameters 45
4.4 Application to univariate disease time series 46
4.5 The multivariate case 50
4.5.1 The endemic component 52
4.5.2 The epidemic component 53
4.6 Estimation by MCMC 54
4.6.1 Alternative representation using auxiliary variables 54
4.7 Update of the parameters 54
4.8 Measles in the districts of Bavaria 56
4.9 Discussion 58
5 A two component model for counts of infectious diseases 59
5.1 Introduction 59
5.2 Model 61
5.2.1 The endemic component 61
5.2.2 The epidemic component 61
5.2.3 Statistical analysis via MCMC 03
5.2.4 One step ahead prediction 63
5.3 Analysis of simulated data 64
5.4 Analysis of real data 65
5.4.1 Hepatitis A 65
5.4.2 Hepatitis B 71
5.4.3 Model comparison 71
5.5 Discussion 73
Contents iii
6 Sequential Monte Carlo methods 77
G.I Retrospective analysis using the forward backward algorithm 77
6.1.1 Update of the changepoints using the forward backward algorithm . 78
6.1.2 Performance of the forward backward algorithm 78
6.2 Particle filter for prospective surveillance 79
6.2.1 Sequential update using the particle filter 81
6.2.2 Application to the hepatitis A data 81
6.3 Discussion 82
7 A model for multivariate time series of infectious disease counts 83
7.1 Introduction 83
7.2 Model 86
7.2.1 The endemic component 87
7.2.2 The epidemic component 8!)
7.3 Statistical analysis by MCMC JO
7.4 The influence of influenza on meningococcal diseases Jl
7.5 Influenza in the federal states of Germany 100
7.6 Model comparison 105
7.7 Discussion 105
8 Conclusion 109
A Twins 111
A.I General information 113
A.2 Starting twins 113
A.3 Input files 113
A.3.1 The data 113
A.3.2 The parameters 113
A.4 Output files 114
A.5 Figures 114
|
| adam_txt |
Contents
1 Introduction 1
1.1 Data 2
1.2 Models 2
1.2.1 Models for chronic diseases 2
1.2.2 Models for infections diseases 4
1.3 Scope of thesis G
2 Bayesian inference 9
2.1 Markov chain Monte Carlo 10
2.1.1 Metropolis Hastings algorithm 11
2.1.2 Single component Metropolis Hastings 11
2.1.3 Gibbs sampler 11
2.1.4 The reversible jump algorithm 12
2.1.5 Effective sample size 12
2.1.G Deviance information criterion 13
2.2 Sequential Monte Carlo 14
2.2.1 Particle filter 14
2.2.2 Forward backward algorithm 17
3 Bayesian changepoint models 23
3.1 Model extensions 25
3.2 Application to coal mining disaster data 2(i
3.3 Posterior distribution 28
3.4 Markov state space form of the changepoint model 29
3.5 Markov structure of the changepoints 31
3.6 Estimation of the changepoint model 32
ii CONTENTS
3.6.1 Reversible jump MCMC 32
3.6.2 Particle filter 35
3.6.3 Forward backward algorithm 36
4 A model for multivariate infectious disease counts 41
4.1 Introduction 41
4.2 The univariate case 42
4.2.1 The endemic component 43
4.2.2 The epidemic component 45
4.3 Estimation by MCMC 45
4.3.1 Alternative representation using auxiliary variables 45
4.3.2 Update of the parameters 45
4.4 Application to univariate disease time series 46
4.5 The multivariate case 50
4.5.1 The endemic component 52
4.5.2 The epidemic component 53
4.6 Estimation by MCMC 54
4.6.1 Alternative representation using auxiliary variables 54
4.7 Update of the parameters 54
4.8 Measles in the districts of Bavaria 56
4.9 Discussion 58
5 A two component model for counts of infectious diseases 59
5.1 Introduction 59
5.2 Model 61
5.2.1 The endemic component 61
5.2.2 The epidemic component 61
5.2.3 Statistical analysis via MCMC 03
5.2.4 One step ahead prediction 63
5.3 Analysis of simulated data 64
5.4 Analysis of real data 65
5.4.1 Hepatitis A 65
5.4.2 Hepatitis B 71
5.4.3 Model comparison 71
5.5 Discussion 73
Contents iii
6 Sequential Monte Carlo methods 77
G.I Retrospective analysis using the forward backward algorithm 77
6.1.1 Update of the changepoints using the forward backward algorithm . 78
6.1.2 Performance of the forward backward algorithm 78
6.2 Particle filter for prospective surveillance 79
6.2.1 Sequential update using the particle filter 81
6.2.2 Application to the hepatitis A data 81
6.3 Discussion 82
7 A model for multivariate time series of infectious disease counts 83
7.1 Introduction 83
7.2 Model 86
7.2.1 The endemic component 87
7.2.2 The epidemic component 8!)
7.3 Statistical analysis by MCMC 'JO
7.4 The influence of influenza on meningococcal diseases 'Jl
7.5 Influenza in the federal states of Germany 100
7.6 Model comparison 105
7.7 Discussion 105
8 Conclusion 109
A Twins 111
A.I General information 113
A.2 Starting twins 113
A.3 Input files 113
A.3.1 The data 113
A.3.2 The parameters 113
A.4 Output files 114
A.5 Figures 114 |
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| edition | 1. Aufl. |
| format | Book |
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| id | DE-604.BV022301433 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:55:11Z |
| indexdate | 2024-07-09T20:54:30Z |
| institution | BVB |
| isbn | 9783899635041 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015511418 |
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| physical | IV, 124 S. graph. Darst., Kt. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
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| publisher | Hut |
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| spelling | Hofmann, Mathias 1974- Verfasser (DE-588)132801817 aut Statistical models for infectious disease surveillance counts Mathias Hofmann 1. Aufl. München Hut 2007 IV, 124 S. graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Univ., Diss., 2006 Infektionskrankheit (DE-588)4026879-2 gnd rswk-swf Epidemie (DE-588)4137380-7 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Infektionskrankheit (DE-588)4026879-2 s Epidemie (DE-588)4137380-7 s Statistik (DE-588)4056995-0 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015511418&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hofmann, Mathias 1974- Statistical models for infectious disease surveillance counts Infektionskrankheit (DE-588)4026879-2 gnd Epidemie (DE-588)4137380-7 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4026879-2 (DE-588)4137380-7 (DE-588)4056995-0 (DE-588)4113937-9 |
| title | Statistical models for infectious disease surveillance counts |
| title_auth | Statistical models for infectious disease surveillance counts |
| title_exact_search | Statistical models for infectious disease surveillance counts |
| title_exact_search_txtP | Statistical models for infectious disease surveillance counts |
| title_full | Statistical models for infectious disease surveillance counts Mathias Hofmann |
| title_fullStr | Statistical models for infectious disease surveillance counts Mathias Hofmann |
| title_full_unstemmed | Statistical models for infectious disease surveillance counts Mathias Hofmann |
| title_short | Statistical models for infectious disease surveillance counts |
| title_sort | statistical models for infectious disease surveillance counts |
| topic | Infektionskrankheit (DE-588)4026879-2 gnd Epidemie (DE-588)4137380-7 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Infektionskrankheit Epidemie Statistik Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015511418&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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