Screen LibraryDisease modelling and public health. Part A /
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 mod...
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| Group Author: | ; ; |
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| Published: |
North-holland, an imprint of Elsevier,
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| Publisher Address: | Amsterdam, Netherlands : |
| Publication Dates: | [2017] |
| Literature type: | Book |
| Language: | English |
| Series: |
Handbook of statistics,
volume 37 |
| Subjects: | |
| Summary: |
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 volume 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. |
| Carrier Form: | xviii, 496 pages : illustrations (some color), color map ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780444639684 0444639683 |
| Index Number: | RA652 |
| CLC: |
R19-32 R18-32 |
| Call Number: | R18-32/D611/pt.a |
| Contents: |
Contributors -- Preface. Section 6 Statistical Methodologies : 1 Imputation of area-level covariates by registry linking / J. Sunil Rao and Jie Fan : 1 Introduction -- 2 Prediction of unknown locations : 2.1 The linking model -- 2.2 Classified mixed model prediction -- 2.3 Incorporating spatial structure -- 2.4 Robust classified predictions -- 3 Simulations : 3.1 Simulation 1: spatially correlated locations: less separable clusters -- 3.2 Simulation 2: spatially correlated locations: more separable location clusters -- 3.3 Simulations 3a and 3b -- 4 Predicting community characteristics for colon cancer patients from the Florida Cancer Data System : 4.1 Clustering of census tracts adds robustness to predictions -- 5 Discussion -- Acknowledgments -- References. 2 Asymptotic approaches to discovering cancer genomic signatures / Maciej Pietrzak and Grzegorz A. Rempala : 1 Introduction : 1.1 Cancer models and next generation sequencing -- 1.2 Our current contribution -- 2 Data and methods : 2.1 Data -- 2.2 Methods -- 2.3 Functional annotation -- 3 Results -- 4 Summary and conclusions -- Acknowledgments -- Appendix: proof of Theorem 1 -- References. 3 Emerging statistical methodologies in the field of microbiome studies / Siddhartha Mandal : 1 Introduction -- 2 Microbial sequencing technologies and associated data : 2.1 Targeted amplicon sequencing -- 2.2 Metagenomic sequencing -- 3 Statistical methodologies for microbiome studies : 3.1 Diversity of microbial communities -- 3.2 Compositional analysis of microbiome -- 3.3 Variable selection in microbiome association studies -- 3.4 Prediction of metagenomes from 16S data -- 3.5 Statistical learning in microbiome analysis -- 4 Discussion and future directions -- References. Section 7 Advanced mathematical methods : 4 Reaction-diffusion equations and their application on bacterial communication / Christina Kuttler : 1 Introduction -- 2 Bacterial communication and some basic mathematical model approaches : 2.1 Basic model with positive feedback loop -- 2.2 Including bacterial population growth -- 2.3 Including a negative feedback and delay -- 2.4 Outlook: quorum sensing in space -- 3 Introduction of reaction-diffusion equations : 3.1 Diffusion equation -- 3.2 Adding the reaction to the diffusion -- 3.3 Initial and boundary conditions -- 3.4 Special solutions -- 3.5 Existence and uniqueness of solutions -- 4 Reaction-diffusion equations and quorum sensing -- 4.1 Working with continuous bacterial distributions -- 4.2 Traveling wave approach -- 4.3 Models for single cells -- 4.4 Approximate equations for point sources -- 4.5 Dynamic model for single cells in space -- 5 Concluding remarks -- References. 5 Hepatitis C virus (HCV) treatment as prevention: epidemic and cost-effectiveness modeling / Natasha K. Martin and Lara K. Marquez : 1 Overview -- 2 Natural history of HCV -- 3 Epidemiology of HCV : 3.1 Global epidemiology of HCV -- 3.2 Transmission routes for HCV -- 3.3 Epidemiology of HCV in key populations -- 4 HCV screening, treatment, and prevention : 4.1 HCV screening and diagnosis -- 4.2 HCV treatment -- 4.3 HCV prevention strategies -- 5 Role of epidemic modeling in public health -- 6 Modeling HCV treatment as prevention -- 7 Cost-effectiveness modeling in HCV : 7.1 Role of cost-effectiveness modeling including prevention benefits -- 7.2 Evaluating the cost-effectiveness of HCV treatment for PWID -- 7.3 Overall aim and methodology -- 7.4 Cost-effectiveness findings -- 8 Conclusions and public heath challenges -- References. 6 Mathematical modeling of mass screening and parameter estimation / Masayuki Kakehashi and Miwako Tsunematsu : 1 Theoretical framework of mass screening -- 2 Mathematical modeling of mass screening : 2.1 Basic framework of the mass screening model -- 2.2 Demography of human population -- 2.3 Theory of mass screening -- 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 -- 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. 7 Inferring patters, 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. 8 Theory and modeling for time required to vaccinate a population in an epidemic / Taejin Lee, Kurien Thomas, and Arni S.R. Srinivasa Rao : 1 introduction -- 2 Empirical modeling -- 3 Spatial spread through convolution -- 4 Numerical example -- 5 (Continued) Section 8 Public health and epidemic data modeling : 9 Frailty models in public health / David D. Hanagal : 1 Introduction -- 2 Shared frailty models -- 3 Consequences -- 4 Identifiability of frailty model -- 5 Modeling frailty -- 6 General shared frailty model -- 7 Shared gamma frailty model -- 8 Baseline distributions : 81 Generalized log-logistic distribution -- 8.2 Generalized Weibull distribution -- 9 Proposed models -- 10 Likelihood specification and Bayesian estimation of parameters -- 11 Analysis of kidney infection data -- 12 Other frailty models : 12.1 Correlated frailty model -- 12.2 Frailty models based on reversed hazard rate -- 12.3 Frailty models based on additive hazard -- References -- Further reading. 10 Structural nested mean models or history-adjusted marginal structural models for time-varying effect modification: an application to dental data / Murthy N. Mittinty : 1 introduction -- 2 Problem, notation, and definitions : 2.1 Definitions -- 2.2 Assumptions -- 3 Detailed description of SNMMs : 3.1 SNMMs for end of study outcome measurement -- 3.2 Estimating the intermediate causal effects for bivariate point treatment and end of study outcome -- 3.3 SNMMs for time-varying outcomes -- 3.4 Estimating the intermediate causal effects for time-varying outcomes -- 3.5 Counterfactual creation and blip function -- 4 History-adjusted marginal structural models : 4.1 Creation of inverse probability treatment weight, referred to as treatment model -- 4.2 Outcome model -- 4.3 Analysis of the effect of periodontal treatment on arterial stiffness : 5.1 Simulated data -- 5.2 Simulation data set 2 -- 6 Discussion : 6.1 Strengths and limitations of the SNMM models -- 6.2 Strengths and limitations of the IPTW HA-MSM -- Appendix : A.1 STATA code used in simulation 1 and generating Tables 1 and 2 -- References. 11 Conditional growth models: an exposition and some extensions / Clive Osmond and Caroline H.D. Fall : 1 Introduction : the problem to be addressed -- 2 The New Delhi birth cohort study -- 3 Conditional growth models : 3.1 The basic concept -- 3.2 Data checking and choices in model formulation -- 3.3 An extension using height and weight measures simultaneously -- 3.4 An extension using height, weight, and skinfold thickness -- 3.5 An extension using the reversal of time -- 3.6 Selection of suitable age intervals -- 4 Descriptive data and traditional analyses : 4.1 Descriptive data -- 4.2 Choice of age intervals -- 4.3 Classical approaches -- 5 Conditional models applied to the New Delhi birth cohort study data : 5.1 Models in height, weight, and body mass index separately -- 5.2 Models for height and weight simultaneously -- 5.3 Models that reverse time -- 6 Strengths and weaknesses of the conditional growth model: conclusions : 6.1 These models are limited to internal comparisons -- 6.2 The reversal paradox -- 6.3 Analogies with the classical "age, period, cohort" problem -- 6.4 Other epidemiological principles -- 6.5 Linear spline mixed models -- 6.6 The Markov principle and regression to the mean -- 6.7 Public health relevance of the results reported here -- 6.8 Summary of the conditional growth models -- Acknowledgments -- References. 12 Parametric model to predict H1N1 influenza in Vellore District, Tamil Nadu, India / Daphne Lopez and Gunasekaran Manogaran : 1 Introduction -- 2 Materials and methods -- 3 Spatial autoregressive model -- 4 Result and discussion -- 5 Conclusion -- References. 13 Public health eye care: modeling techniques to translate evidence into effective action / Gudlavalleti V.S. Murthy and Neena S. John : 1 Introduction -- 2 Magnitude of blindness and visual impairment : 2.1 Calculating global magnitude and current prevalence of blindness -- 2.2 Calculating incidence of blindness and visual impairment -- 3 Causes of blindness and visual impairment : 3.1 Costing and cost analysis for eye care -- 3.2 Use of statistical modeling for cause-specific magnitude and control measures -- 3.3 Forecasting the need for cataract surgical services -- 4 Planning for human resource needs for future eye care needs : 4.1 Developing a model to predict requirement of ophthalmologists for control of cataract blindness: a case study from India. 14 Individual-based models for public health / Benjamin Roche and Raphaël Duboz : 1 Background -- 2 Broad model philosophy -- 3 Modeling IBMs : 3.1 Specification -- 3.2 Unified modeling language and individual-based modeling -- 3.3 Toward a formal specification of IBMs by using the discrete events specification system -- 4 Working with mean-field and individual-based models : 4.1 IBMs and MFMs of the same system -- 4.2 The coupling of IBM with MFM to enable scale transfer modeling (multiscale modeling) -- 5 Specific uses of IBMs : 5.1 Spatially explicit models -- 5.2 Complex behaviors -- 5.3 Multistrains pathogen-- 6 IBM calibration : 6.1 Sensitivity analysis -- 7 Biological knowledge gained thanks to IBMs -- 8 Caveats of IBMs -- 9 Software |