Muutke küpsiste eelistusi

Disease Modelling and Public Health, Part B, Volume 37 [Kõva köide]

Volume editor (Professor, Medical College of Georgia, USA), Volume editor (University of Hyderabad Campus, India), Volume editor (Indian Institute of Public Health, Hyderabad, India)
  • Formaat: Hardback, 390 pages, kõrgus x laius: 229x152 mm, kaal: 750 g
  • Sari: Handbook of Statistics
  • Ilmumisaeg: 31-Oct-2017
  • Kirjastus: North-Holland
  • ISBN-10: 0444639756
  • ISBN-13: 9780444639752
Teised raamatud teemal:
Disease Modelling and Public Health, Part B, Volume 37Suurem pilt
  • Formaat: Hardback, 390 pages, kõrgus x laius: 229x152 mm, kaal: 750 g
  • Sari: Handbook of Statistics
  • Ilmumisaeg: 31-Oct-2017
  • Kirjastus: North-Holland
  • ISBN-10: 0444639756
  • ISBN-13: 9780444639752
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780444639752.html
Märksõnad:

Handbook of Statistics: Disease Modelling and Public Health, Part B, Volume 37 addresses new challenges in existing and emerging diseases. As a two part volume, this title covers an extensive range of techniques in the field, with this book including chapters on Reaction diffusion equations and their application on bacterial communication, Spike and slab methods in disease modeling, Mathematical modeling of mass screening and parameter estimation, Individual-based and agent-based models for infectious disease transmission and evolution: an overview, and a section on Visual Clustering of Static and Dynamic High Dimensional Data.

This series covers the lack of availability of complete data relating to disease symptoms and disease epidemiology, one of the biggest challenges facing vaccine developers, public health planners, epidemiologists and health sector researchers.

  • 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

Muu info

Two-part volume covering an extensive range of techniques in the field of statistics and disease modeling
Contributors xiii
Preface xv
Section VI Statistical Methodologies
1 Imputation of Area-Level Covariates by Registry Linking
3(20)
J. Sunil Rao
Jie Fan
1 Introduction
3(1)
2 Prediction of Unknown Locations
4(3)
2.1 The Linking Model
4(1)
2.2 Classified Mixed Model Prediction
5(1)
2.3 Incorporating Spatial Structure
6(1)
2.4 Robust Classified Predictions
7(1)
3 Simulations
7(4)
3.1 Simulation 1---Spatially Correlated Locations: Less Separable Clusters
7(2)
3.2 Simulation 2---Spatially Correlated Locations: More Separable Location Clusters
9(1)
3.3 Simulations 3a and 3b
10(1)
4 Predicting Community Characteristics for Colon Cancer Patients From the Florida Cancer Data System
11(6)
4.1 Clustering of Census Tracts Adds Robustness to Predictions
15(2)
5 Discussion
17(6)
Acknowledgments
20(1)
References
20(3)
2 Asymptotic Approaches to Discovering Cancer Genomic Signatures
23(14)
Maciej Pietrzak
Grzegorz A. Rempala
1 Introduction
23(3)
1.1 Cancer Models and Next Generation Sequencing
23(2)
1.2 Our Current Contribution
25(1)
2 Data and Methods
26(3)
2.1 Data
26(1)
2.2 Methods
26(3)
2.3 Functional Annotation
29(1)
3 Results
29(3)
4 Summary and Conclusions
32(5)
Acknowledgments
33(1)
Appendix. Proof of Theorem 1
33(2)
References
35(2)
3 Emerging Statistical Methodologies in the Field of Microbiome Studies
37(18)
Siddhartha Mandal
1 Introduction
38(1)
2 Microbial Sequencing Technologies and Associated Data
39(2)
2.1 Targeted Amplicon Sequencing
39(1)
2.2 Metagenomic Sequencing
40(1)
3 Statistical Methodologies for Microbiome Studies
41(6)
3.1 Diversity of Microbial Communities
41(1)
3.2 Compositional Analysis of Microbiome
42(2)
3.3 Variable Selection in Microbiome Association Studies
44(1)
3.4 Prediction of Metagenomes From 16S Data
45(1)
3.5 Statistical Learning in Microbiome Studies
45(2)
3.6 Computational Tools for Microbiome Analysis
47(1)
4 Discussion and Future Directions
47(8)
References
49(6)
Section VII Advanced Mathematical Methods
4 Reaction--Diffusion Equations and Their Application on Bacterial Communication
55(38)
Christina Kuttler
1 Introduction
55(1)
2 Bacterial Communication and Some Basic Mathematical Model Approaches
56(10)
2.1 Basic Model With Positive Feedback Loop
58(4)
2.2 Including Bacterial Population Growth
62(1)
2.3 Including a Negative Feedback and Delay
63(2)
2.4 Outlook: Quorum Sensing in Space
65(1)
3 Introduction of Reaction--Diffusion Equations
66(6)
3.1 Diffusion Equation
66(3)
3.2 Adding the Reaction to the Diffusion
69(1)
3.3 Initial and Boundary Conditions
69(1)
3.4 Special Solutions
70(1)
3.5 Existence and Uniqueness of Solutions
71(1)
4 Reaction--Diffusion Equations and Quorum Sensing
72(17)
4.1 Working With Continuous Bacterial Distributions
72(1)
4.2 Traveling Wave Approach
73(5)
4.3 Models for Single Cells
78(1)
4.4 Approximate Equations for Point Sources
78(3)
4.5 Dynamic Model for Single Cells in Space
81(8)
5 Concluding Remarks
89(4)
References
90(3)
5 Hepatitis C Virus (HCV) Treatment as Prevention: Epidemic and Cost-Effectiveness Modeling
93(28)
Natasha K. Martin
Lara K. Marquez
1 Overview
94(1)
2 Natural History of HCV
94(1)
3 Epidemiology of HCV
95(2)
3.1 Global Epidemiology of HCV
95(1)
3.2 Transmission Routes for HCV
96(1)
3.3 Epidemiology of HCV in Key Populations
96(1)
4 HCV Screening, Treatment, and Prevention
97(4)
4.1 HCV Screening and Diagnosis
97(1)
4.2 HCV Treatment
98(1)
4.3 HCV Prevention Strategies
99(2)
5 Role of Epidemic Modeling in Public Health
101(1)
6 Modeling HCV Treatment as Prevention
101(5)
7 Cost-Effectiveness Modeling in HCV
106(8)
7.1 Role of Cost-Effectiveness Modeling Including Prevention Benefits
106(1)
7.2 Evaluating the Cost-Effectiveness of HCV Treatment for PWID
107(1)
7.3 Overall Aim and Methodology
107(7)
7.4 Cost-Effectiveness Findings
114(1)
8 Conclusions and Public Health Challenges
114(7)
References
117(4)
6 Mathematical Modeling of Mass Screening and Parameter Estimation
121(34)
Masayuki Kakehashi
Miwako Tsunematsu
1 Theoretical Framework of Mass Screening
122(2)
2 Mathematical Modeling of Mass Screening
124(11)
2.1 Basic Framework of the Mass Screening Model
124(5)
2.2 Demography of Human Population
129(3)
2.3 Theory of Mass Screening
132(1)
2.4 Stages of Cancer Progression
132(1)
2.5 Survival Rates of Different Stages
133(1)
2.6 Benefits and Harms
134(1)
3 Simulation: Breast Cancer in Japan
135(16)
3.1 Overview of Demography and Breast Cancer Epidemiology in Japan
135(5)
3.2 Model Building: The Framework of Breast Cancer Model Based on Observed Data
140(4)
3.3 Estimation of Transition Parameters
144(4)
3.4 Results of Simulation
148(3)
3.5 Characteristics of the Most Beneficial Mass Screening
151(1)
4 Discussion
151(4)
Acknowledgments
153(1)
References
153(1)
Further Reading
154(1)
7 Inferring Patterns, Dynamics, and Model-Based Metrics of Epidemiological Risks of Neglected Tropical Diseases
155(30)
Anuj Mubayi
1 Introduction
155(5)
1.1 Definitions
158(1)
1.2 Vectorial Capacity
158(1)
1.3 Modeling Neglected Vector-Borne Diseases
159(1)
2 Methods
160(20)
2.1 Mapping and Relational Modeling of NTDs
160(1)
2.2 Securing Data and Empirical Information for Modeling NTDs
160(2)
2.3 Dynamical Modeling of NTDs
162(18)
3 Conclusions
180(5)
References
181(4)
8 Theory and Modeling for Time Required to Vaccinate a Population in an Epidemic
185(24)
Taejin Lee
Kurien Thomas
Arni S.R. Srinivasa Rao
1 Introduction
185(2)
2 Empirical Modeling
187(2)
3 Spatial Spread Through Convolution
189(7)
4 Numerical Example
196(6)
5 Conclusions
202(7)
Appendix. Generalization of the Time Function
202(3)
References
205(4)
Section VIII Public Health and Epidemic Data Modeling
9 Frailty Models in Public Health
209(40)
David D. Hanagal
1 Introduction
209(1)
2 Shared Frailty Models
210(1)
3 Consequences of Ignoring Frailty
211(3)
4 Identifiability of Frailty Model
214(1)
5 Modeling Frailty
215(1)
6 General Shared Frailty Model
216(2)
7 Shared Gamma Frailty Model
218(1)
8 Baseline Distributions
219(2)
8.1 Generalized Log-Logistic Distribution
219(1)
8.2 Generalized Weibull Distribution
220(1)
9 Proposed Models
221(1)
10 Likelihood Specification and Bayesian Estimation of Parameters
222(2)
11 Analysis of Kidney Infection Data
224(9)
12 Other Frailty Models
233(16)
12.1 Correlated Frailty Model
233(4)
12.2 Frailty Models Based on Reversed Hazard Rate
237(2)
12.3 Frailty Models Based on Additive Hazard
239(3)
References
242(5)
Further Reading
247(2)
10 Structural Nested Mean Models or History-Adjusted Marginal Structural Models for Time-Varying Effect Modification: An Application to Dental Data
249(26)
Murthy N. Mittinty
1 Introduction
250(2)
2 Problem, Notation, and Definitions
252(4)
2.1 Definitions
253(2)
2.2 Assumptions
255(1)
3 Detailed Description of SNMMs
256(3)
3.1 SNMMs for End of Study Outcome Measurement
256(1)
3.2 Estimating the Intermediate Causal Effects for Bivariate Point Treatment and End of Study Outcome
257(1)
3.3 SNMMs for Time-Varying Outcomes
258(1)
3.4 Estimating the Intermediate Causal Effects for Time-Varying Outcomes
258(1)
3.5 Counterfactual Creation and Blip Function
259(1)
4 History-Adjusted Marginal Structural Models
259(2)
4.1 Creation of Inverse Probability Treatment Weight, Referred to as Treatment Model
260(1)
4.2 Outcome Model
261(1)
5 Analysis of the Effect of Periodontal Treatment on Arterial Stiffness
261(7)
5.1 Simulated Data
261(5)
5.2 Simulation Data Set 2
266(2)
6 Discussion
268(1)
6.1 Strengths and Limitations of the SNMM Models
268(1)
6.2 Strengths and Limitations of the IPTW HA-MSM
268(1)
Appendix
269(6)
A.1 STATA Code Used in Simulation 1 and Generating Tables 1 and 2
269(2)
References
271(4)
11 Conditional Growth Models: An Exposition and Some Extensions
275(26)
Clive Osmond
Caroline H.D. Fall
1 Introduction: The Problem to Be Addressed
275(1)
2 The New Delhi Birth Cohort Study
276(1)
3 Conditional Growth Models
276(5)
3.1 The Basic Concept
276(2)
3.2 Data Checking and Choices in Model Formulation
278(1)
3.3 An Extension Using Height and Weight Measures Simultaneously
279(1)
3.4 An Extension Using Height, Weight, and Skinfold Thickness
280(1)
3.5 An Extension Using the Reversal of Time
280(1)
3.6 Selection of Suitable Age Intervals
281(1)
4 Descriptive Data and Traditional Analyses
281(6)
4.1 Descriptive Data
281(1)
4.2 Choice of Age Intervals
281(5)
4.3 Classical Approaches
286(1)
5 Conditional Models Applied to the New Delhi Birth Cohort Study Data
287(6)
5.1 Models in Height, Weight, and Body Mass Index Separately
288(1)
5.2 Models for Height and Weight Simultaneously
288(3)
5.3 Models That Reverse Time
291(2)
6 Strengths and Weaknesses of the Conditional Growth Model: Conclusions
293(8)
6.1 These Models Are Limited to Internal Comparisons
293(1)
6.2 The Reversal Paradox
293(3)
6.3 Analogies With the Classical "Age, Period, Cohort" Problem
296(1)
6.4 Other Epidemiological Principles
296(1)
6.5 Linear Spline Mixed Models
297(1)
6.6 The Markov Principle and Regression to the Mean
298(1)
6.7 Public Health Relevance of the Results Reported Here
298(1)
6.8 Summary of the Conditional Growth Models
299(1)
Acknowledgments
299(1)
References
299(2)
12 Parametric Model to Predict H1N1 Influenza in Vellore District, Tamil Nadu, India
301(16)
Daphne Lopez
Gunasekaran Manogaran
1 Introduction
301(2)
2 Materials and Methods
303(4)
3 Spatial Autoregressive Model
307(4)
4 Result and Discussion
311(2)
5 Conclusion
313(4)
References
314(3)
13 Public Health Eye Care: Modeling Techniques to Translate Evidence Into Effective Action
317(30)
Gudlavalleti V.S. Murthy
Neena S. John
1 Introduction
318(3)
2 Magnitude of Blindness and Visual Impairment
321(4)
2.1 Calculating Global Magnitude and Current Prevalence of Blindness
322(2)
2.2 Calculating Incidence of Blindness and Visual Impairment
324(1)
3 Causes of Blindness and Visual Impairment
325(7)
3.1 Costing and Cost Analysis for Eye Care
326(1)
3.2 Use of Statistical Modeling for Cause-Specific Magnitude and Control Measures
327(1)
3.3 Forecasting the Need for Cataract Surgical Services
328(4)
4 Planning for Human Resource Needs for Future Eye Care Needs
332(9)
4.1 Developing a Model to Predict Requirement of Ophthalmologists for Control of Cataract Blindness: A Case Study From India
334(7)
5 Conclusions
341(6)
References
342(5)
14 Individual-Based Models for Public Health
347(20)
Benjamin Roche
Raphael Duboz
1 Background
347(2)
2 Broad Model Philosophy
349(1)
3 Modeling IBMs
350(3)
3.1 Specification
350(1)
3.2 Unified Modeling Language and Individual-Based Modeling
351(1)
3.3 Toward a Formal Specification of IBMs by Using the Discrete Events Specification System
351(2)
4 Working With Mean-Field and Individual-Based Models
353(1)
4.1 IBMs and MFMs of the Same System
353(1)
4.2 The Coupling of IBM With MFM to Enable Scale Transfer Modeling (Multiscale Modeling)
354(1)
5 Specific Uses of IBMs
354(3)
5.1 Spatially Explicit Models
354(2)
5.2 Complex Behaviors
356(1)
5.3 Multistrains Pathogens
356(1)
6 IBM Calibration
357(1)
6.1 Sensitivity Analysis
358(1)
7 Biological Knowledge Gained Thanks to IBMs
358(2)
8 Caveats of IBMs
360(1)
9 Software Platforms
361(1)
10 Conclusions
361(6)
References
362(5)
Index 367
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.