Disease modelling and public health, part B:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier, North Holland
[2017]
|
| Schriftenreihe: | Handbook of statistics
volume 37 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvi, 374 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780444639752 |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV044454163 | ||
| 003 | DE-604 | ||
| 005 | 20180228 | ||
| 007 | t| | ||
| 008 | 170821s2017 xx a||| |||| 00||| eng d | ||
| 020 | |a 9780444639752 |9 978-0-444-63975-2 | ||
| 035 | |a (OCoLC)1011675686 | ||
| 035 | |a (DE-599)BVBBV044454163 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-739 |a DE-384 |a DE-473 |a DE-210 |a DE-703 | ||
| 084 | |a QH 200 |0 (DE-625)141539: |2 rvk | ||
| 084 | |a QH 230 |0 (DE-625)141545: |2 rvk | ||
| 084 | |a QH 232 |0 (DE-625)141547: |2 rvk | ||
| 084 | |a SK 840 |0 (DE-625)143261: |2 rvk | ||
| 245 | 1 | 0 | |a Disease modelling and public health, part B |c edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| 264 | 1 | |a Amsterdam |b Elsevier, North Holland |c [2017] | |
| 264 | 4 | |c © 2017 | |
| 300 | |a xvi, 374 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Handbook of statistics |v volume 37 | |
| 650 | 0 | 7 | |a Gesundheitswesen |0 (DE-588)4020775-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Krankheit |0 (DE-588)4032844-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistisches Modell |0 (DE-588)4121722-6 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Krankheit |0 (DE-588)4032844-2 |D s |
| 689 | 0 | 1 | |a Gesundheitswesen |0 (DE-588)4020775-4 |D s |
| 689 | 0 | 2 | |a Statistisches Modell |0 (DE-588)4121722-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Rao, Arni S. R. Srinivasa |0 (DE-588)1143256220 |4 edt | |
| 700 | 1 | |a Pyne, Saumyadipta |4 edt | |
| 700 | 1 | |a Rao, Calyampudi Radhakrishna |d 1920-2023 |0 (DE-588)119285924 |4 edt | |
| 830 | 0 | |a Handbook of statistics |v volume 37 |w (DE-604)BV000002510 |9 37 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855003&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-029855003 | |
Datensatz im Suchindex
| _version_ | 1817681167020720128 |
|---|---|
| adam_text |
Contents
Contributors xiii
Preface xv
Section VI
Statistical Methodologies
1. Imputation of Area-Level Covariates by Registry
Linking 3
J. Sunil Rao and fie Fan
1 Introduction 3
2 Prediction of Unknown Locations 4
2.1 The Linking Model 4
2.2 Classified Mixed Model Prediction 5
2.3 Incorporating Spatial Structure 6
2.4 Robust Classified Predictions 7
3 Simulations 7
3.1 Simulation 1—Spatially Correlated Locations:
Less Separable Clusters 7
3.2 Simulation 2—Spatially Correlated Locations:
More Separable Location Clusters 9
3.3 Simulations 3a and 3b 10
4 Predicting Community Characteristics for Colon Cancer
Patients From the Florida Cancer Data System 11
4.1 Clustering of Census Tracts Adds Robustness to Predictions 1 5
5 Discussion 17
Acknowledgments 20
References 20
2. Asymptotic Approaches to Discovering Cancer
Genomic Signatures 23
Madej P/etrzak and Grzegorz A. RempaJa
1 Introduction 23
1.1 Cancer Models and Next Generation Sequencing 23
1.2 Our Current Contribution 25
v
vi
Contents
2 Data and Methods 26
2.1 Data 26
2.2 Methods 26
2.3 Functional Annotation 29
3 Resuits 29
4 Summary and Conclusions 32
Acknowledgments 33
Appendix. Proof of Theorem 1 33
References 35
3. Emerging Statistical Methodologies in the Field
of Microoiome Studies 37
Siddhartha Mandal
1 Introduction 38
2 Microbial Sequencing Technologies and Associated Data 39
2.1 Targeted Amp!icon Sequencing 39
2.2 Metagenomic Sequencing 40
3 Statistical Methodologies for Microbiome Studies 41
3.1 Diversity of Microbial Communities 41
3.2 Compositional Analysis of Microbiome 42
3.3 Variable Selection in Microbiome
Association Studies 44
3.4 Prediction of Metagenomes From 16S Data 45
3.5 Statistical Learning in Microbiome Studies 45
3.6 Computational Tools for Microbiome Analysis 47
4 Discussion and Future Directions 47
References 49
Section VII
Advanced Mathematical Methods
4. Reaction-Diffusion Equations and Their Application
on Bacterial Communication 55
Christina Kuttler
1 Introduction 55
2 Bacterial Communication and Some Basic Mathematical
Model Approaches 56
2.1 Basic Model With Positive Feedback Loop 58
2.2 Including Bacterial Population Growth 62
2.3 Including a Negative Feedback and Delay 63
2.4 Outlook: Quorum Sensing in Space 65
3 Introduction of Reaction-Diffusion Equations 66
3.1 Diffusion Equation 66
3.2 Adding the Reaction to the Diffusion 69
Contents vií
3.3 Initial and Boundary Conditions 69
3.4 Special Solutions 70
3.5 Existence and Uniqueness of Solutions 71
4 Reaction-Diffusion Equations and Quorum Sensing 72
4.1 Working With Continuous Bacterial
Distributions 72
4.2 Traveling Wave Approach 73
4.3 Models for Single Cells 78
4.4 Approximate Equations for Point Sources 78
4.5 Dynamic Model for Single Cells in Space 81
5 Concluding Remarks 89
References 90
5. Hepatitis C Virus (HCV) Treatment as Prevention:
Epidemic and Cost-Effectiveness Modeling 93
Natasha K. Martin and Lara K. Marquez
1 Overview 94
2 Natural History of HCV 94
3 Epidemiology of HCV 95
3.1 Global Epidemiology of HCV 95
3.2 Transmission Routes for HCV 96
3.3 Epidemiology of HCV in Key Populations 96
4 HCV Screening, Treatment, and Prevention 97
4.1 HCV Screening and Diagnosis 97
4.2 HCV Treatment 98
4.3 HCV Prevention Strategies 99
5 Role of Epidemic Modeling in Public Health 101
6 Modeling HCV Treatment as Prevention 101
7 Cost-Effectiveness Modeling in HCV 106
7.1 Role of Cost-Effectiveness Modeling Including
Prevention Benefits 106
7.2 Evaluating the Cost֊Effectiveness of HCV Treatment
for PWID 107
7.3 Overall Aim and Methodology 107
7.4 Cost-Effectiveness Findings H4
8 Conclusions and Public Health Challenges 114
References 117
6. Mathematical Modeling of Mass Screening
and Parameter Estimation 121
Masayuki Kakehashi and Miwako Tsunematsu
1 Theoretical Framework of Mass Screening 1 22
2 Mathematical Modeling of Mass Screening 1 24
2.1 Basic Framework of the Mass Screening Model 124
2.2 Demography of Human Population 129
2.3 Theory of Mass Screening 132
132
133
134
135
135
140
144
148
151
151
153
153
154
։ 55
155
158
158
159
160
160
160
162
180
181
185
185
187
189
196
202
202
205
2.4 Stages of Cancer Progression
2.5 Survival Rates of Different Stages
2.6 Benefits and Harms
3 Simulation: Breast Cancer in Japan
3.1 Overview of Demography and Breast Cancer
Epidemiology in Japan
3.2 Model Building: The Framework of Breast Cancer
Model Based on Observed Data
3.3 Estimation of Transition Parameters
3.4 Results of Simulation
3.5 Characteristics of the Most Beneficial Mass Screening
4 Discussion
Acknowledgments
References
Further Reading
Inferring Patterns, Dynamics, and Model-Based
Metrics of Epidemiological Risks of Neglected
Tropical Diseases
Anuj Mubayi
1 Introduction
1.1 Definitions
1.2 Vectorial Capacity
1.3 Modeling Neglected Vector-Borne Diseases
2 Methods
2.1 Mapping and Relational Modeling of NTDs
2.2 Securing Data and Empirical Information for
Modeling NTDs
2.3 Dynamical Modeling of NTDs
3 Conclusions
References
Theory and Modeling for Time Required
to Vaccinate a Population in an Epidemic
Taejin Lee, Kurien Thomas, and Ami S.R. Srinivasa Rao
1 Introduction
2 Empirical Modeling
3 Spatial Spread Through Convolution
4 Numerical Example
5 Conclusions
Appendix. Generalization of the Time Function
References
Contents ix
Section VIII
Public Health and Epidemic Data Modeling
9. Frailty Models in Public Health 209
David D. Hanagal
1 Introduction 209
2 Shared Frailty Models 210
3 Consequences of Ignoring Frailty 211
4 Identifiability of Frailty Model 214
5 Modeling Frailty 215
6 Genera! Shared Frailty Model 216
7 Shared Gamma Frailty Model 218
8 Baseline Distributions 219
8.1 Generalized Log-Logistic Distribution 219
8.2 Generalized Weibull Distribution 220
9 Proposed Models 221
10 Likelihood Specification and Bayesian Estimation
of Parameters 222
11 Analysis of Kidney Infection Data 224
12 Other Frailty Models 233
12.1 Correlated Frailty Model 233
12.2 Frailty Models Based on Reversed Hazard Rate 237
12.3 Frailty Models Based on Additive Hazard 239
References 242
Further Reading 247
10. Structural Nested Mean Models or History-Adjusted
Marginal Structural Models for Time-Varymg Effect
Modification: An Application to Dental Data 249
Murthy N. Mittinty
1 Introduction 250
2 Problem, Notation, and Definitions 252
2.1 Definitions 253
2.2 Assumptions 255
3 Detailed Description of SNMMs 256
3.1 SNMMs for End of Study Outcome Measurement 256
3.2 Estimating the Intermediate Causal Effects for Bivariate
Point Treatment and End of Study Outcome 257
3.3 SNMMs for Time-Varying Outcomes 258
3.4 Estimating the Intermediate Causal Effects for
Time-Varying Outcomes 258
3.5 Counterfactual Creation and Blip Function 259
4 Hi story-Ad justed Marginal Structural Models 259
4.1 Creation of Inverse Probability Treatment Weight,
Referred to as Treatment Model 260
4.2 Outcome Model 261
X
Contents
5 Analysis of the Effect of Periodontal Treatment on
Arterial Stiffness 261
5.1 Simulated Data 261
5.2 Simulation Data Set 2 266
6 Discussion 268
6.1 Strengths and Limitations of the SNMM Models 268
6.2 Strengths and Limitations of the IPTW HA-MSM 268
Appendix 269
A.1 STATA Code Used in Simulation 1 and Generating
Tables 1 and 2 269
References 271
11. Conditional Growth Models: An Exposition and
Some Extensions 275
Clive Osmond and Caroline H.D. Fall
1 introduction: The Problem to Be Addressed 275
2 The New Delhi Birth Cohort Study 276
3 Conditional Growth Models 276
3.1 The Basic Concept 276
3.2 Data Checking and Choices in Model Formulation 278
3.3 An Extension Using Height and Weight Measures
Simultaneously 279
3.4 An Extension Using Height, Weight, and Skinfold
Thickness 280
3.5 An Extension Using the Reversal of Time 280
3.6 Selection of Suitable Age Intervals 281
4 Descriptive Data and Traditional Analyses 281
4.1 Descriptive Data 281
4.2 Choice of Age Intervals 281
4.3 Classical Approaches 286
5 Conditional Models Applied to the New Delhi Birth Cohort
Study Data 287
5.1 Models in Height, Weight, and Body Mass Index
Separately 288
5.2 Models for Height and Weight Simultaneously 288
5.3 Models That Reverse Time 291
6 Strengths and Weaknesses of the Conditional Growth Model;
Conclusions 293
6.1 These Models Are Limited to Internal Comparisons 293
6.2 The Reversal Paradox 293
6.3 Analogies With the Classical "Age, Period,
Cohort" Problem 296
6.4 Other Epidemiological Principles 296
6.5 Linear Spline Mixed Models 297
6.6 The Markov Principle and Regression to the Mean 298
Contents
XI
6.7 Public Health Relevance of the Results
Reported Here 298
6.8 Summary of the Conditional Growth Models 299
Acknowledgments 299
References 299
12. Parametric Model to Predict H1N1 Influenza
in Vellore District, Tamil Nadu, India 301
Daphne Lopez and Gunasekaran Manogaran
1 Introduction 301
2 Materials and Methods 303
3 Spatial Autoregressive Model 307
4 Result and Discussion 311
5 Conclusion 313
References 314
13. Public Health Eye Care: Modeling Techniques
to Translate Evidence Into Effective Action 317
Gudlavaileti US. Murthy and Neena S. John
1 Introduction 318
2 Magnitude of Blindness and Visual Impairment 321
2.1 Calculating Global Magnitude and Current Prevalence
of Blindness 322
2.2 Calculating Incidence of Blindness and Visual
Impairment 324
3 Causes of Blindness and Visual Impairment 325
3.1 Costing and Cost Analysis for Eye Care 326
3.2 Use of Statistical Modeling for Cause-Specific
Magnitude and Control Measures 327
3.3 Forecasting the Need for Cataract Surgical Services 328
4 Planning for Human Resource Needs for Future
Eye Care Needs 332
4.1 Developing a Model to Predict Requirement of
Ophthalmologists for Control of Cataract Blindness:
A Case Study From India 334
5 Conclusions 341
References 342
14. Individual-Based Models for Public Health 347
Benjamin Roche and Raphael Duboz
1 Background 347
2 Broad Model Philosophy 349
xi i Contents
3 Modeling ÍBMs 350
3.1 Specification 350
3.2 Unified Modeling Language and Individual-Based
Modeling 351
3.3 Toward a Formal Specification of IBMs by Using
the Discrete Events Specification System 351
4 Working With Mean-Field and Individual-Based Models 353
4.1 IBMs and MFMs of the Same System 353
4.2 The Coupling of IBM With MFM to Enable Scale
Transfer Modeling (Multiscale Modeling) 354
5 Specific Uses of IBMs 354
5.1 Spatially Explicit Models 354
5.2 Complex Behaviors 356
5.3 Multistrains Pathogens 356
6 IBM Calibration 357
6.1 Sensitivity Analysis 358
7 Biological Knowledge Gained Thanks to IBMs 358
8 Caveats of IBMs 360
9 Software Platforms 361
10 Conclusions 361
References 362
Index 367 |
| any_adam_object | 1 |
| author2 | Rao, Arni S. R. Srinivasa Pyne, Saumyadipta Rao, Calyampudi Radhakrishna 1920-2023 |
| author2_role | edt edt edt |
| author2_variant | a s r s r asrs asrsr s p sp c r r cr crr |
| author_GND | (DE-588)1143256220 (DE-588)119285924 |
| author_facet | Rao, Arni S. R. Srinivasa Pyne, Saumyadipta Rao, Calyampudi Radhakrishna 1920-2023 |
| building | Verbundindex |
| bvnumber | BV044454163 |
| classification_rvk | QH 200 QH 230 QH 232 SK 840 |
| ctrlnum | (OCoLC)1011675686 (DE-599)BVBBV044454163 |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 cb4500</leader><controlfield tag="001">BV044454163</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20180228</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">170821s2017 xx a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780444639752</subfield><subfield code="9">978-0-444-63975-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1011675686</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV044454163</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-739</subfield><subfield code="a">DE-384</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-210</subfield><subfield code="a">DE-703</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 200</subfield><subfield code="0">(DE-625)141539:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 230</subfield><subfield code="0">(DE-625)141545:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 232</subfield><subfield code="0">(DE-625)141547:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 840</subfield><subfield code="0">(DE-625)143261:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Disease modelling and public health, part B</subfield><subfield code="c">edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam</subfield><subfield code="b">Elsevier, North Holland</subfield><subfield code="c">[2017]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2017</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xvi, 374 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">24 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Handbook of statistics</subfield><subfield code="v">volume 37</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Gesundheitswesen</subfield><subfield code="0">(DE-588)4020775-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Krankheit</subfield><subfield code="0">(DE-588)4032844-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistisches Modell</subfield><subfield code="0">(DE-588)4121722-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Krankheit</subfield><subfield code="0">(DE-588)4032844-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Gesundheitswesen</subfield><subfield code="0">(DE-588)4020775-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Statistisches Modell</subfield><subfield code="0">(DE-588)4121722-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Arni S. R. Srinivasa</subfield><subfield code="0">(DE-588)1143256220</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pyne, Saumyadipta</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Calyampudi Radhakrishna</subfield><subfield code="d">1920-2023</subfield><subfield code="0">(DE-588)119285924</subfield><subfield code="4">edt</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Handbook of statistics</subfield><subfield code="v">volume 37</subfield><subfield code="w">(DE-604)BV000002510</subfield><subfield code="9">37</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Bamberg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855003&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029855003</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV044454163 |
| illustrated | Illustrated |
| indexdate | 2024-12-06T09:04:04Z |
| institution | BVB |
| isbn | 9780444639752 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029855003 |
| oclc_num | 1011675686 |
| open_access_boolean | |
| owner | DE-739 DE-384 DE-473 DE-BY-UBG DE-210 DE-703 |
| owner_facet | DE-739 DE-384 DE-473 DE-BY-UBG DE-210 DE-703 |
| physical | xvi, 374 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Elsevier, North Holland |
| record_format | marc |
| series | Handbook of statistics |
| series2 | Handbook of statistics |
| spelling | Disease modelling and public health, part B edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao Amsterdam Elsevier, North Holland [2017] © 2017 xvi, 374 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 37 Gesundheitswesen (DE-588)4020775-4 gnd rswk-swf Krankheit (DE-588)4032844-2 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Krankheit (DE-588)4032844-2 s Gesundheitswesen (DE-588)4020775-4 s Statistisches Modell (DE-588)4121722-6 s DE-604 Rao, Arni S. R. Srinivasa (DE-588)1143256220 edt Pyne, Saumyadipta edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 37 (DE-604)BV000002510 37 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855003&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Disease modelling and public health, part B Handbook of statistics Gesundheitswesen (DE-588)4020775-4 gnd Krankheit (DE-588)4032844-2 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| subject_GND | (DE-588)4020775-4 (DE-588)4032844-2 (DE-588)4121722-6 (DE-588)4143413-4 |
| title | Disease modelling and public health, part B |
| title_auth | Disease modelling and public health, part B |
| title_exact_search | Disease modelling and public health, part B |
| title_full | Disease modelling and public health, part B edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_fullStr | Disease modelling and public health, part B edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_full_unstemmed | Disease modelling and public health, part B edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_short | Disease modelling and public health, part B |
| title_sort | disease modelling and public health part b |
| topic | Gesundheitswesen (DE-588)4020775-4 gnd Krankheit (DE-588)4032844-2 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| topic_facet | Gesundheitswesen Krankheit Statistisches Modell Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029855003&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
| work_keys_str_mv | AT raoarnisrsrinivasa diseasemodellingandpublichealthpartb AT pynesaumyadipta diseasemodellingandpublichealthpartb AT raocalyampudiradhakrishna diseasemodellingandpublichealthpartb |