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E-raamat: Disease Modelling and Public Health, Part A

Volume editor (Professor, Medical College of Georgia, USA), Volume editor (University of Hyderabad Campus, India), Volume editor (Indian Institute of Public Health, Hyderabad, India)
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  • Sari: Handbook of Statistics
  • Ilmumisaeg: 13-Oct-2017
  • Kirjastus: North-Holland
  • Keel: eng
  • ISBN-13: 9780444639691
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Disease Modelling and Public Health, Part ASuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Handbook of Statistics
  • Ilmumisaeg: 13-Oct-2017
  • Kirjastus: North-Holland
  • Keel: eng
  • ISBN-13: 9780444639691
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Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology.

As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.

  • Presents a comprehensive, two-part volume written by leading subject experts
  • Provides a unique breadth and depth of content coverage
  • Addresses the most cutting-edge developments in the field
  • Includes chapters on Ebola and the Zika virus; topics which have grown in prominence and scholarly output

Arvustused

"Simply put, the study converts qualitative data to quantitative data. Chapter 10 in the study is unique for a health scientist it looks at these risk behaviors and draws important conclusions regarding the contraction of various chronic and infectious diseases. This data could influence the way in which college students are educated on health risk behaviors in the future." --The State Press

Muu info

Addresses new challenges in existing and emerging diseases and how they are modeled
Contributors xv
Preface xvii
Section I Introduction and Disease Modeling
1 Fundamentals of Mathematical Models of Infectious Diseases and Their Application to Data Analyses
3(44)
Masayuki Kakehashi
Shoko Kawano
1 Introduction: Fundamentals of Infectious Disease Dynamical Models
4(11)
1.1 Population Dynamics of Biological Populations
5(1)
1.2 Infectious Disease Spread Models, or Theoretical Epidemiology
6(5)
1.3 Important Concepts in Infectious Disease Epidemiology
11(1)
1.4 Important Concepts From Dynamical Models of Infectious Diseases
12(3)
2 Analyses of Whole Population: Macroscopic Analyses
15(18)
2.1 Data Description
15(2)
2.2 Simple Regression Analysis
17(5)
2.3 The Effect of School Closure
22(2)
2.4 Incorporating Exposed Phase: SEIR Model
24(1)
2.5 Distributions of Latent and Infectious Periods
25(6)
2.6 Multiple Subgroups and Generation Matrix
31(2)
3 Stochastic Model of Infectious Disease Spread: Microscopic Model Considering Each Class
33(7)
3.1 Analyses for Counted Data
34(1)
3.2 Modeling the Reporting Delay
35(1)
3.3 Modeling the Transition of Infectious Diseases
36(2)
3.4 Reconstruction of the Values of State Variables of the System
38(1)
3.5 Analysis and Simulation, and the Validity of the Model
39(1)
4 An Analysis of Spatial Distribution
40(3)
4.1 Location of Schools
41(1)
4.2 Estimating Transition Kernel
41(2)
4.3 Influence of the Network of Transmission
43(1)
5 Conclusion
43(4)
References
44(1)
Further Reading
45(2)
2 Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study
47(20)
Jessica K. Barrett
Michael J. Sweeting
Angela M. Wood
1 Introduction
47(2)
2 Landmarking
49(4)
2.1 The Landmarking Method
50(2)
2.2 Dynamic Prediction
52(1)
3 Joint Models
53(3)
3.1 Model Specification
53(1)
3.2 Estimation
54(2)
3.3 Dynamic Prediction
56(1)
4 Assessing Predictive Performance
56(2)
4.1 Area Under the Receiver Operating Characteristic Curve
57(1)
4.2 Brier Score
58(1)
5 Example: The ARIC Study
58(4)
6 Discussion
62(5)
Acknowledgments
63(1)
References
64(3)
3 Statistical Models for Selected Infectious Diseases
67(8)
Poduri S.R.S. Rao
1 Common Cold and Asthma Exacerbation
67(1)
2 Influenza
68(2)
2.1 Surveillance and Estimates of the Center for Disease Control and Prevention
68(1)
2.2 Influenza and Respiratory Syncytial Virus in the United States
68(1)
2.3 Measles and Influenza Outbreaks
69(1)
2.4 SIRS and Hierarchical Bayesian Models
69(1)
2.5 Autoregressive and Bayesian Models for the Spread of Influenza
70(1)
2.6 Correlation of Surveillance Systems and Information Environment
70(1)
2.7 The Delphi System
70(1)
3 Tuberculosis
70(3)
3.1 Statistical Models for TB Incidence, Prevalence, and Mortality Estimates
70(1)
3.2 Mathematical Models
71(1)
3.3 Regression and Bayesian Models
71(1)
3.4 Statistical Relational Models for Structured Epidemiological Characteristics
72(1)
3.5 Bayesian Analysis for the Prevalence of TB
72(1)
3.6 Partial Least Squares and Weighted Regression for the Factors Affecting TB
72(1)
3.7 Mathematical Models for the Resistance and Mechanism of TB and Its Relapse
72(1)
4 Malaria
73(2)
References
73(1)
Further Reading
74(1)
4 Finite Mixture Models in Biostatistics
75(30)
Sharon X. Lee
Shu-Kay Ng
Geoffrey J. McLachlan
1 Introduction
75(1)
2 Finite Mixture Models
76(2)
3 Robust Mixture Models
78(2)
4 Analysis of Cytometric Data
80(9)
4.1 Automated Gating of Single Sample
80(1)
4.2 Clustering and Alignment of Cell Populations Across Multiple Samples
81(3)
4.3 Class Prediction for New Samples
84(5)
5 Analysis of Gene Expression Data
89(9)
5.1 Clustering of Gene Expression Data
90(1)
5.2 Ranking of Correlated Genes
91(5)
5.3 Controlling for FDR
96(2)
6 Conclusions
98(7)
References
99(6)
Section II Methods for Public Health Data
5 Alternative Sampling Designs for Time-to-Event Data With Applications to Biomarker Discovery in Alzheimer's Disease
105(62)
Michelle M. Nuno
Daniel L. Gillen
1 Introduction
106(1)
2 A Brief Review of Survival Analysis
107(9)
2.1 Censoring
107(2)
2.2 Statistical Functions of Interest in Time-to-Event Data
109(1)
2.3 Parametric Estimation of the Survival Distribution
110(1)
2.4 Nonparametric Estimation of the Survival Distribution
111(5)
3 Cox Proportional Hazards Model
116(3)
4 Influence of Cases and Controls in the Cox Model
119(2)
4.1 The Partial Information in the Two-Sample Case
119(1)
4.2 An Empirical Assessment of the Influence of Cases and Controls
120(1)
5 Nested Case-Control Study
121(12)
5.1 Introduction to the Nested Case--Control Design
121(3)
5.2 Equivalence of the Cox Proportional Hazards and Conditional Logistic Regression Model Under the Nested Case--Control Design
124(1)
5.3 Nested Case--Control Sampling Schemes
125(4)
5.4 Software Implementation of the Standard Nested Case--Control Design
129(1)
5.5 Simulated Performance of the Nested Case--Control Design
129(4)
6 Case--Cohort Design
133(9)
6.1 Introduction to the Case--Cohort Design
133(2)
6.2 Implementation of the Case--Cohort Design
135(4)
6.3 Software Implementation of the Case--Cohort Design
139(1)
6.4 Simulated Performance of the Case--Cohort Design
139(3)
7 Implementation of Sampling Designs Using Data From the Alzheimer's Disease Neuroimaging Initiative (ADNI)
142(6)
8 Explicit Adjustment for Confounding Variables Using Alternative Sampling Designs
148(4)
8.1 Matching in the Nested Case--Control Design
149(1)
8.2 The Exposure Stratified Case--Cohort Design
150(2)
9 Nested Case-Control Design vs the Case--Cohort Design
152(2)
9.1 Scientific Considerations
152(1)
9.2 Statistical Considerations
153(1)
10 Study Design
154(2)
11 Discussion
156(11)
Acknowledgments
157(1)
Appendix
157(1)
A.1 Implementing ADNI Analysis in R, SAS, and STATA
157(1)
A.2 Implementation of the ADNI Analysis Using R
158(4)
A.3 Implementation in STATA
162(2)
A.4 Implementation in SAS
164(1)
References
165(2)
6 Real-Time Estimation of the Case Fatality Ratio and Risk Factors of Death
167(8)
Hiroshi Nishiura
1 Introduction
167(1)
2 Right Censoring: Core Issue of Real-Time Estimation
168(2)
3 Right Censoring and Identification of Death Risk Factors
170(1)
4 Extensions and Future Challenges
171(4)
Acknowledgments
173(1)
References
173(2)
7 Nonparametric Regression of State Occupation Probabilities in a Multistate Model
175(30)
Sutirtha Chakraborty
Somnath Datta
Susmita Datta
1 Introduction
175(2)
2 The Proposed Methodology
177(5)
2.1 Data Structure and Notations
177(1)
2.2 Additive Models
178(2)
2.3 Conditional Transition Hazard Rates and State Occupation Probabilities
180(1)
2.4 Censoring Hazards and Estimation of the Weights Ki(t)
181(1)
3 Simulations
182(12)
3.1 The Simulation Design
182(1)
3.2 Conditionally Semi-Markov Network
183(1)
3.3 Conditionally Markov Network
184(1)
3.4 Study of the Censoring Bias
184(2)
3.5 Study of Overall Estimation Error
186(7)
3.6 Tests for Regression Effects and a Power Study
193(1)
4 Application to Bone Marrow Transplant Data
194(6)
5 Discussion
200(5)
Acknowledgments
201(1)
Appendix. (Proof of Theorem 1)
201(1)
References
202(3)
8 Gene Set Analysis: As Applied to Public Health and Biomedical Studies
205(24)
Shabnam Vatanpour
Irina Dinu
1 Introduction
205(5)
1.1 What Are DNA Microarrays?
206(1)
1.2 Challenges in the Analysis of DNA Microarray Studies
206(2)
1.3 Why Gene Set Analysis?
208(2)
2 Methods
210(9)
2.1 Individual Gene Analysis Methods
210(4)
2.2 Gene Set Analysis Methods
214(1)
2.3 GSA Methods for Continuous Outcomes
215(4)
3 An Application of GSA for Analysis of a Multivariate Continuous Outcome
219(1)
4 Discussion
220(9)
References
224(5)
9 Causal Inference in the Study of Infectious Disease
229(20)
Bradley C. Saul
Michael G. Hudgens
M. Elizabeth Halloran
1 Introduction
229(2)
2 Causal Assumptions
231(2)
3 Causal Inference for Single and Multiple Point Exposures
233(3)
3.1 Time-Varying Exposures and the g-Methods
233(3)
4 Alternative Approaches to Address Confounding
236(3)
4.1 Test-Negative Design
236(1)
4.2 Negative Controls
237(1)
4.3 Regression Discontinuity
238(1)
5 Principal Stratification
239(2)
5.1 Postinfection Selection
240(1)
5.2 Principal Surrogates
241(1)
6 Interference
241(2)
7 Summary
243(6)
References
243(6)
Section III Computing
10 Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases
249(56)
Anuj Mubayi
1 Introduction
250(5)
1.1 Social and Contextual Influences
251(1)
1.2 Ecological Models of Health Behavior
252(1)
1.3 Modeling Methods for Health Behaviors in Literature
252(3)
2 Quantitative Modeling Methods
255(24)
2.1 Gathering Data (Survey; Ecological Momentary Assessment) to Assess Ecological Complex Systems
255(2)
2.2 Agent-Based Model
257(4)
2.3 CART and Random Forests
261(9)
2.4 Uncertainty and Sensitivity Analysis of a Function Using CART and Random Forest
270(3)
2.5 Text Mining of Twitter Data
273(6)
3 Parameter Estimates and Sensitivity of a Dynamical System Model Using Berkeley Madonna
279(26)
3.1 Parameter Estimation of the Model
279(3)
3.2 Local Parameter Sensitivity Analysis of the Model
282(3)
Appendix. Codes
285(1)
A.1 Example to Analyze Survey Data in R
285(1)
A.2 NetLogo Code for ABM
285(1)
A.3 SPARTAN Codes for Sensitivity Analysis of ABM
285(1)
A.4 CART and Random Forests R Codes
285(6)
A.5 Uncertainty and Sensitivity Analysis Using CART in MATLAB®
291(5)
A.6 R Code for Mining Twitter Data
296(2)
A.7 Dynamical System Model in Berkeley Madonna
298(3)
A.8 Data Sets
301(1)
References
301(4)
11 Data-Driven Computational Disease Spread Modeling: From Measurement to Parametrization and Control
305(24)
Stefan Engblom
Stefan Widgren
1 Introduction
305(1)
2 A Generic Data-Driven Epidemiological Framework
306(9)
2.1 Continuous-Time Markov Chains
307(2)
2.2 Concentration Variables
309(1)
2.3 Spatio-Temporal Epidemic Networks
309(1)
2.4 Discretization in Time
310(2)
2.5 Simlnf. An R Package for Data-Driven Stochastic Disease Spread Simulations
312(3)
3 Measurement, Parametrization, and Control
315(12)
3.1 A Running Example: The SISE Model
316(1)
3.2 Equilibrium Behavior
317(5)
3.3 Synthetic Feasibility Study of Parametrization
322(3)
3.4 Exploring Options for Control
325(2)
4 Conclusions
327(2)
Acknowledgments
327(1)
References
327(2)
12 Individual and Collective Behavior in Public Health Epidemiology
329(40)
Jiangzhuo Chen
Bryan Lewis
Achla Marathe
Madhav Marathe
Samarth Swarup
Anil K.S. Vullikanti
1 Introduction
329(4)
1.1 Organization
331(2)
2 Background
333(2)
3 Qualitative/Verbal Models
335(2)
4 Formal Models for Representing Behaviors
337(8)
4.1 Computational Considerations
338(1)
4.2 Game-Theoretic Models That Capture Strategic Behavior
339(3)
4.3 Markov Decision Process Models
342(2)
4.4 Belief--Desire--Intention Model
344(1)
5 Simulations to Study Coevolving Behaviors and Epidemics
345(4)
5.1 Specification and Implementation of Behaviors in Simulations
347(2)
6 Inferring Health Behaviors Using Real-World Data
349(5)
6.1 Inferring Behavior Using Online and Offline Survey Methods
350(1)
6.2 Inferring Behaviors Using Social Media Data
350(3)
6.3 Inferring Behaviors Using Crowdsourced Webapps
353(1)
6.4 Mapping Behavioral Models on Synthetic Agents
353(1)
7 Behavioral Interventions and Interactions
354(2)
7.1 Behavioral Interactions
355(1)
8 Case Studies
356(13)
8.1 Distribution of Limited Antivirals During an Influenza Pandemic
356(1)
8.2 Primary Caregivers' Behavior and Their Role in Containing Secondary Transmission Within Households
356(1)
8.3 Friendship Networks, Social Norms, and Obesity
357(1)
Acknowledgments
358(1)
References
358(11)
Section IV Mathematical Modeling and Methods
13 Theoretical Advances in Type 2 Diabetes
369(28)
Pranay Goel
1 Introduction
369(2)
2 Causal Theories of Diabetes
371(1)
3 Clinical Assessment of Diabetes
372(3)
3.1 Open-Loop Approach: The Glucose Clamp Technique
373(1)
3.2 Closed-Loop Models of Glucose Tolerance
374(1)
4 Mathematical Models of Glucose Intolerance
375(2)
4.1 Bergman Minimal Model
375(1)
4.2 The HOMA Model
376(1)
4.3 Other Models
377(1)
5 Life Course Models of Diabetes
377(5)
5.1 The Topp Model
378(1)
5.2 The Ha Model
379(2)
5.3 The Hypersecretion Model
381(1)
6 Obesity and Models of Weight Loss
382(6)
6.1 The Hall Model of CR
382(3)
6.2 Criticism of Energy Balance Models
385(2)
6.3 Personalization of Nutrition
387(1)
7 Data Science-Based Models
388(1)
8 Further Reading and Future Directions
389(8)
Acknowledgment
391(1)
References
392(5)
14 Helminth Dynamics: Mean Number of Worms, Reproductive Rates
397(10)
Arni S.R. Srinivasa Rao
Roy M. Anderson
1 Mean Number of Worms
397(4)
1.1 Cross-Sectional Mean
398(1)
1.2 Cohort Mean
399(1)
1.3 Theorems on Worm Growth Potential in Hosts
400(1)
2 Net Production Rates Within and Outside Human Host
401(1)
3 Impact of Chemotherapy
402(1)
4 Discussion
403(4)
References
404(3)
Section V Bayesian Methods
15 Bayesian Methods in Public Health
407(36)
Wesley O. Johnson
Elizabeth B. Ward
Daniel L. Gillen
1 Introduction
407(10)
2 Comparing Proportions
417(10)
2.1 Bayesian Inference for Cross-sectional or Cohort Sampling
422(3)
2.2 Bayesian Inference for Case-Control Sampling
425(2)
3 Logistic Regression Modeling and Inference
427(9)
4 Mixed/Hierarchical Modeling and Inference
436(5)
5 Conclusions
441(2)
Acknowledgment
441(1)
References
441(2)
16 Bayesian Disease Mapping for Public Health
443(40)
Andrew Lawson
Duncan Lee
1 Introduction
443(2)
2 Spatial Modeling
445(4)
2.1 Data and Overall Model
445(1)
2.2 Random Effect Models
446(2)
2.3 Choice of Priors
448(1)
2.4 Goodness of Fit and Variable Selection
448(1)
3 Space-Time Modeling
449(3)
4 Multivariate Modeling
452(3)
4.1 Likelihood Models
452(1)
4.2 Multivariate Spatial Correlation and MCAR Models
453(2)
5 Software
455(3)
5.1 General Purpose Software
456(1)
5.2 Specialized Spatial Modeling Software
457(1)
6 Cluster Identification
458(2)
7 Boundary Detection (Wombling)
460(2)
8 Ecological Regression in Public Health
462(1)
9 Disease Map Surveillance
463(6)
9.1 Surveillance Concepts
464(1)
9.2 Syndromic Surveillance
465(1)
9.3 Process Control Ideas
465(1)
9.4 Single Disease Sequence
466(1)
9.5 Multiple Disease Sequences
466(1)
9.6 Infectious Disease Surveillance
466(1)
9.7 Spatial and Spatiotemporal Surveillance
467(2)
10 Spatial Survival Analysis
469(3)
10.1 Endpoint Distributions
469(2)
10.2 Censoring
471(1)
10.3 Random Effect Specification
471(1)
10.4 General Hazard Model
472(1)
11 Example
472(3)
12 Discussion and Future Directions
475(8)
Acknowledgments
475(1)
References
475(8)
Index 483
Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, and artificial intelligence and applications in medicine. He is a Professor at the Medical College of Georgia, Augusta University, U.S.A, and the Director of the Laboratory for Theory and Mathematical Modeling housed within the Division of Infectious Diseases, Medical College of Georgia, Augusta, U.S.A. Previously, Dr. Rao conducted research and/or taught at Mathematical Institute, University of Oxford (2003, 2005-07), Indian Statistical Institute (1998-2002, 2006-2012), Indian Institute of Science (2002-04), University of Guelph (2004-06). Until 2012, Dr. Rao held a permanent faculty position at the Indian Statistical Institute. He has won the Heiwa-Nakajima Award (Japan) and Fast Track Young Scientists Fellowship in Mathematical Sciences (DST, New Delhi). Dr. Rao also proved a major theorem in stationary population models, such as, Rao's Partition Theorem in Populations, Rao-Carey Theorem in stationary populations, and developed mathematical modeling-based policies for the spread of diseases like HIV, H5N1, COVID-19, etc. He developed a new set of network models for understanding avian pathogen biology on grid graphs (these were called chicken walk models), AI Models for COVID-19 and received wide coverage in the science media. Recently, he developed concepts such as Exact Deep Learning Machines”, and Multilevel Contours” within a bundle of Complex Number Planes.

PhD, Professor, Indian Institute of Public Health, Hyderabad, India C. R. Rao is a world famous statistician who earned a place in the history of statistics as one of those who developed statistics from its adhoc origins into a firmly grounded mathematical science.”

He was employed at the Indian Statistical Institute (ISI) in 1943 as a research scholar after obtaining an MA degree in mathematics with a first class and first rank from Andhra University in1941 and MA degree in statistics from Calcutta University in 1943 with a first class, first rank, gold medal and record marks which remain unbroken during the last 73 years.

At the age of 28 he was made a full professor at ISI in recognition of his creativity.” While at ISI, Rao went to Cambridge University (CU) in 1946 on an invitation to work on an anthropometric project using the methodology developed at ISI. Rao worked in the museum of archeology and anthropology in Duckworth laboratory of CU during 1946-1948 as a paid visiting scholar. The results were reported in the book Ancient Inhabitants of Jebel Moya” published by the Cambridge Press under the joint authorship of Rao and two anthropologists. On the basis of work done at CU during the two year period, 1946-1948, Rao earned a Ph.D. degree and a few years later Sc.D. degree of CU and the rare honor of life fellowship of Kings College, Cambridge.

He retired from ISI in 1980 at the mandatory age of 60 after working for 40 years during which period he developed ISI as an international center for statistical education and research. He also took an active part in establishing state statistical bureaus to collect local statistics and transmitting them to Central Statistical Organization in New Delhi. Rao played a pivitol role in launching undergraduate and postgraduate courses at ISI. He is the author of 475 research publications and several breakthrough papers contributing to statistical theory and methodology for applications to problems in all areas of human endeavor. There are a number of classical statistical terms named after him, the most popular of which are Cramer-Rao inequality, Rao-Blackwellization, Raos Orthogonal arrays used in quality control, Raos score test, Raos Quadratic Entropy used in ecological work, Raos metric and distance which are incorporated in most statistical books.

He is the author of 10 books, of which two important books are, Linear Statistical Inference which is translated into German, Russian, Czec, Polish and Japanese languages,and Statistics and Truth which is translated into, French, German, Japanese, Mainland Chinese, Taiwan Chinese, Turkish and Korean languages.

He directed the research work of 50 students for the Ph.D. degrees who in turn produced 500 Ph.D.s. Rao received 38 hon. Doctorate degree from universities in 19 countries spanning 6 continents. He received the highest awards in statistics in USA,UK and India: National Medal of Science awarded by the president of USA, Indian National Medal of Science awarded by the Prime Minister of India and the Guy Medal in Gold awarded by the Royal Statistical Society, UK. Rao was a recipient of the first batch of Bhatnagar awards in 1959 for mathematical sciences and and numerous medals in India and abroad from Science Academies. He is a Fellow of Royal Society (FRS),UK, and member of National Academy of Sciences, USA, Lithuania and Europe. In his honor a research Institute named as CRRAO ADVANCED INSTITUTE OF MATHEMATICS, STATISTICS AND COMPUTER SCIENCE was established in the campus of Hyderabad University.
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