COVER
TITLE PAGE
CONTRIBUTORS
PREFACE
PART 1: INFECTIOUS DISEASE CONTROL AND MANAGEMENT
1. 1 OPTIMIZATION IN INFECTIOUS DISEASE CONTROL AND PREVENTION
  1. 1.1 TUBERCULOSIS EPIDEMIOLOGY AND BACKGROUND
  2. 1.2 MICROSIMULATIONS FOR DISEASE CONTROL
  3. 1.3 A MICROSIMULATION FOR TUBERCULOSIS CONTROL IN INDIA
  4. 1.4 CONCLUSION
  5. REFERENCES
2. 2 SAVING LIVES WITH OPERATIONS RESEARCH
  1. 2.1 INTRODUCTION
  2. 2.2 HIV RESOURCE ALLOCATION: THEORETICAL ANALYSES
  3. 2.3 HIV RESOURCE ALLOCATION: PORTFOLIO ANALYSES
  4. 2.4 HIV RESOURCE ALLOCATION: A TOOLFOR DECISION MAKERS
  5. 2.5 DISCUSSION AND FURTHER RESEARCH
  6. ACKNOWLEDGMENT
  7. REFERENCES
3. 3 ADAPTIVE DECISION‐MAKING DURING EPIDEMICS
  1. 3.1 INTRODUCTION
  2. 3.2 PROBLEM FORMULATION
  3. 3.3 METHODS
  4. 3.4 NUMERICAL RESULTS
  5. 3.5 CONCLUSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
4. 4 ASSESSING REGISTER‐BASED CHLAMYDIA INFECTION SCREENING STRATEGIES
  1. 4.1 Introduction
  2. 4.2 BACKGROUND LITERATURE REVIEW
  3. 4.3 MATHEMATICAL MODELING
  4. 4.4 STRATEGY ASSESSMENT
  5. 4.5 CONCLUSIONS AND FUTURE RESEARCH
  6. REFERENCES
5. 5 OPTIMAL SELECTION OF ASSAYS FOR DETECTING INFECTIOUS AGENTS IN DONATED BLOOD
  1. 5.1 INTRODUCTION AND CHALLENGES
  2. 5.2 THE NOTATION AND DECISION PROBLEM
  3. 5.3 THE CASE STUDY OF THE SUB‐SAHARAN AFRICA REGION AND THE UNITED STATES
  4. 5.4 CONTRIBUTIONS AND FUTURE RESEARCH DIRECTIONS
  5. ACKNOWLEDGMENTS
  6. REFERENCES
6. 6 MODELING CHRONIC HEPATITIS C DURING RAPID THERAPEUTIC ADVANCE
  1. 6.1 INTRODUCTION
  2. 6.2 METHOD
  3. 6.3 FOUR RESEARCH AREAS IN DESIGNING EFFECTIVE HCV INTERVENTIONS
  4. 6.4 CONCLUDING REMARKS
  5. REFERENCES
PART 2: NONCOMMUNICABLE DISEASE PREVENTION
1. 7 MODELING DISEASE PROGRESSION AND RISK‐DIFFERENTIATED SCREENING FOR CERVICAL CANCER PREVENTION
  1. 7.1 INTRODUCTION
  2. 7.2 LITERATURE REVIEW
  3. 7.3 MODELING CERVICAL CANCER SCREENING
  4. 7.4 MODEL‐BASED ANALYSES
  5. 7.5 CONCLUDING REMARKS
  6. REFERENCES
2. 8 USING FINITE‐HORIZON MARKOV DECISION PROCESSES FOR OPTIMIZING POST‐MAMMOGRAPHY DIAGNOSTIC DECISIONS
  1. 8.1 INTRODUCTION
  2. 8.2 MODEL FORMULATIONS
  3. 8.3 STRUCTURAL PROPERTIES
  4. 8.4 NUMERICAL RESULTS
  5. 8.5 SUMMARY
  6. ACKNOWLEDGMENTS
  7. REFERENCES
3. 9 PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES FOR PROSTATE CANCER SCREENING, SURVEILLANCE, AND TREATMENT
  1. 9.1 INTRODUCTION
  2. 9.2 REVIEW OF POMDP MODELS AND BENCHMARK ALGORITHMS
  3. 9.3 A POMDP MODEL FOR PROSTATE CANCER SCREENING, SURVEILLANCE, AND TREATMENT
  4. 9.4 BUDGETED SAMPLING APPROXIMATION
  5. 9.5 COMPUTATIONAL EXPERIMENTS
  6. 9.6 CONCLUSIONS
  7. REFERENCES
4. 10 COST‐EFFECTIVENESS ANALYSIS OF BREAST CANCER MAMMOGRAPHY SCREENING POLICIES CONSIDERING UNCERTAINTY IN WOMEN’S ADHERENCE
  1. 10.1 INTRODUCTION
  2. 10.2 MODEL FORMULATION
  3. 10.3 NUMERICAL STUDIES
  4. 10.4 RESULTS
  5. 10.5 SUMMARY
  6. REFERENCES
5. 11 AN AGENT‐BASED MODEL FOR IDEAL CARDIOVASCULAR HEALTH
  1. 11.1 INTRODUCTION
  2. 11.2 METHODOLOGY
  3. 11.3 RESULTS
  4. 11.4 SIMULATING THE MEDICARE‐AGE POPULATION AND THE DISEASE‐SPECIFIC SUBPOPULATIONS
  5. 11.5 FUTURE RESEARCH
  6. 11.6 SUMMARY
  7. REFERENCES
PART 3: TREATMENT TECHNOLOGY AND SYSTEM
1. 12 BIOLOGICAL PLANNING OPTIMIZATION FOR HIGH‐DOSE‐RATE BRACHYTHERAPY AND ITS APPLICATION TO CERVICAL CANCER TREATMENT
  1. 12.1 INTRODUCTION
  2. 12.2 CHALLENGES AND OBJECTIVES
  3. 12.3 MATERIALS AND METHODS
  4. 12.4 VALIDATION AND RESULTS
  5. 12.5 FINDINGS, IMPLEMENTATION, AND CHALLENGES
  6. 12.6 IMPACT AND SIGNIFICANCE
  7. ACKNOWLEDGMENT
  8. REFERENCES
2. 13 FLUENCE MAP OPTIMIZATION IN INTENSITY‐MODULATED RADIATION THERAPY TREATMENT PLANNING
  1. 13.1 INTRODUCTION
  2. 13.2 TREATMENT PLAN EVALUATION
  3. 13.3 FMO OPTIMIZATION MODELS
  4. 13.4 OPTIMIZATION APPROACHES
  5. 13.5 CONCLUSIONS
  6. REFERENCES
3. 14 SLIDING WINDOW IMRT AND VMAT OPTIMIZATION
  1. 14.1 INTRODUCTION
  2. 14.2 TWO‐STEP IMRT PLANNING
  3. 14.3 ONE‐STEP IMRT PLANNING
  4. 14.4 VOLUMETRIC MODULATED ARC THERAPY
  5. 14.5 FUTURE WORK FOR RADIOTHERAPY OPTIMIZATION
  6. 14.6 CONCLUDING THOUGHTS
  7. REFERENCES
4. 15 MODELING THE CARDIOVASCULAR DISEASE PREVENTION–TREATMENT TRADE‐OFF
  1. 15.1 INTRODUCTION
  2. 15.2 METHODS
  3. 15.3 RESULTS
  4. 15.4 DISCUSSION
  5. ACKNOWLEDGMENT
  6. REFERENCES
5. 16 TREATMENT OPTIMIZATION FOR PATIENTS WITH TYPE 2 DIABETES
  1. 16.1 INTRODUCTION
  2. 16.2 LITERATURE REVIEW
  3. 16.3 MODEL FORMULATION
  4. 16.4 NUMERICAL RESULTS
  5. 16.5 CONCLUSIONS
  6. REFERENCES
6. 17 MACHINE LEARNING FOR EARLY DETECTION AND TREATMENT OUTCOME PREDICTION
  1. 17.1 INTRODUCTION
  2. 17.2 BACKGROUND
  3. 17.3 MACHINE LEARNING WITH DISCRETE SUPPORT VECTOR MACHINE PREDICTIVE MODELS
  4. 17.4 APPLYING DAMIP TO REAL‐WORLD APPLICATIONS
  5. 17.5 SUMMARY AND CONCLUSION
  6. ACKNOWLEDGMENT
  7. REFERENCES
INDEX
END USER LICENSE AGREEMENT

List of Tables

Chapter 01
1. TABLE 1.1 Example Simulation Validation Measures
Chapter 02
1. TABLE 2.1 Summary of Models Reviewed
2. TABLE 2.2 REACH Model Results: Brazil
3. TABLE 2.3 REACH Model Results: Thailand
Chapter 03
1. TABLE 3.1 Calibration Procedure at the Epidemic Decision Point
Chapter 04
1. TABLE 4.1 Notation in the Age‐Structured Compartmental Model for CT Transmission and Intervention
2. TABLE 4.2 Additional Notation in the Model
3. TABLE 4.3 Model Parameter Values Extracted from the Literature
4. TABLE 4.4 Model Validation with Respect to CT Infection Prevalence
5. TABLE 4.5 Screening Strategies Tested
6. TABLE 4.6 One‐Way Sensitivity Analysis Results
Chapter 05
1. TABLE 5.1 Measures of Accuracy for Binary Diagnostic Tests
2. TABLE 5.2 Prevalence Rates (in %) in Sub‐Saharan Africa and the United States (See Bish et al. (2011) for Data References)
3. TABLE 5.3 Sub‐Saharan Africa Region: Risk Values for Test Sets That Meet the WHO Recommendations and for the Optimal Universal and Optimal Non‐universal Test Sets
4. TABLE 5.4 United States: Risk Values for Test Sets That Meet the FDA Requirements and for the Optimal universal and Optimal Non‐universal Test Sets
Chapter 06
1. TABLE 6.1 Fibrosis Progression: Annual Probabilities; Mean (Ranges)
2. TABLE 6.2 Mortality Hazard Ratios
Chapter 07
1. TABLE 7.1 Cost‐Effectiveness Analysis for Mixed‐Risk Populations
Chapter 09
1. TABLE 9.1 Performance Comparison of the Budgeted Sampling Approximation Method and Incremental Pruning (IP) via the Four Test Instances Starting from Prior Belief of 1 in No Cancer at Age 40
2. TABLE 9.2 Performance Comparison of the Budgeted Sampling Approximation Method and Incremental Pruning (IP) via the Four Test Instances Starting from Prior Belief of 0.5 No Cancer and 0.5 Extraprostatic Prostate Cancer at Age 70
3. TABLE 9.3 The Performance of the Budgeted Sampling Approximation Method under Different Budget Constraints of k = 11, 12, 13, 30, 300, and 3000 Given c ≥ k Compared with Incremental Pruning (IP)
4. TABLE 9.4 The Performance of the Budgeted Sampling Approximation Method under Different Randomized Policies, , 0.5, and 0.9 Compared with Incremental Pruning (IP)
Chapter 10
1. TABLE 10.1 Sources for Parameter Estimation
2. TABLE 10.2 Cost‐Effectiveness Analysis of Different Screening Policies Relative to a No Screening Policy (Doing Nothing) – perfect Adherence Case
3. TABLE 10.3 Cost‐Effectiveness Analysis of Different Screening Policies Relative to a No Screening Policy (Doing Nothing) – general population Adherence Case
Chapter 11
1. TABLE 11.1 Levels of Ideal Cardiovascular Health Based on Life’s Simple 7
2. TABLE 11.2 Data Sources for Major Parameters
3. TABLE 11.3 Population Characteristics of American Adults
4. TABLE 11.4 Characteristics of the Medicare‐Age Population and ThreeDisease‐Specific Subpopulations
5. TABLE 11.5 Simulated Population Health Outcomes for Normal Health Progression and Comprehensive Lifestyle Program
Chapter 12
1. TABLE 12.1 TCPs in Various Plans
2. TABLE 12.2 Dose Distribution in Standard Plans versus Escalated Plans (Using the Same Prescribed PTV Dose)
Chapter 13
1. TABLE 13.1 Common Physical Dose Criteria for Some Treatment Sites
2. TABLE 13.2 Interpretations of a Values in gEUD Equation
Chapter 15
1. TABLE 15.1 Baseline Parameter Values
2. TABLE 15.2 Optimization of Spending Mix (Percent of Annual Expenditures to Prevention)
3. TABLE 15.3 Impacts of Research Spending
Chapter 16
1. TABLE 16.1 Summary of the Cost and Utility Inputs for the Model
2. TABLE 16.2 Summary of Medication Costs that Represent the Lower Bound on Treatment Costs in the United States (Red Book 2009) and Medication Disutility Values (Tengs and Wallace 2000; Pignone et al. 2006; Mason et al. 2014)
3. TABLE 16.3 Comparison of QALYs, Costs ($), and ICERs ($/QALY) for Male Patients
4. TABLE 16.4 Comparison of QALYs, Costs ($), and ICERs ($/QALY) for Female Patients
Chapter 17
1. TABLE 17.1 Model Size
2. TABLE 17.2 Classification Results of Emory Data, 10‐Fold Cross‐Validation, and Blind Prediction
3. TABLE 17.3 Classification Results of the Same Emory Data, 10‐Fold Cross‐Validation, and Blind Prediction from 9 Score‐Type Features Instead of Raw Data

List of Illustrations

Chapter 01
1. Figure 1.1 Who‐mixes‐with‐whom matrix for a microsimulation.
2. Figure 1.2 Heath state transitions and treatment schematic for a microsimulation.
3. Figure 1.3 Calibration targets for microsimulation model of TB. WHO estimates are compared against model outputs.
4. Figure 1.4 Cost‐effectiveness frontier. Dx, diagnosis; GX, GeneXpert (Suen et al. 2015).
5. Figure 1.5 Probabilistic sensitivity analysis of healthcare costs and TB‐related quality of life. At a willingness‐to‐pay threshold of India’s per capita GDP ($1450), PPM with GeneXpert for all diagnosis is most likely cost‐effective. Dx, diagnosis; GX, GeneXpert (Suen et al. 2015).
Chapter 03
1. Figure 3.1 A framework for adaptive decision‐making during epidemics.
2. Figure 3.2 Sequence of events, actions, and observations during an epidemic.
3. Figure 3.3 Daily number of influenza notifications in San Francisco, CA, during the autumn wave of the 1918 influenza pandemic.
4. Figure 3.4 A simple model for the outbreak of a novel viral pathogen.
5. Figure 3.5 Comparison of the total number of cases observed at day 21 during the autumn wave the Spanish flu in San Francisco, CA, with the prediction from the epidemic model. Histogram in (a) is created using 1000 simulation runs for which R₀ and 1/μ are randomly drawn from prior distributions and , respectively. To construct histograms in (b) and (c), first, N₀ = 25,000 simulation runs are obtained and then to decide which trajectories to keep, (for b) and (for c) are used. The remaining trajectories are resampled 1000 times according to the mass probability function Π_z returned by the calibration procedure in Table 3.1.
6. Figure 3.6 Estimates for the basic reproductive number (a) and the mean of infectiousness period (b) using the first 21 observations during the Spanish flu in San Francisco, CA.
7. Figure 3.7 A dynamic control policy to inform decisions about the employment of a transmission‐reducing intervention (e.g., school or public place closure). (a) Affordability curve returns the expected total cost if dynamic policies with different values of WTP for health is chosen to be implemented. The dark gray area represents the 95% confidence interval for the expected cost, while the light gray area represents the 95% confidence interval for the predicted total cost. (b) Decision rule for WTP = $5000 for one additional case averted. The black and gray areas represents, respectively, the region where the transmission‐reducing intervention is recommended to be employed or lifted. The curve corresponds to one simulated epidemic trajectory.
8. Figure 3.8 Cost‐effectiveness plane comparing the performance of static versus dynamic transmission‐reducing (TR) policies. Each cost‐effectiveness frontier is obtained by optimizing the corresponding policy for different values of willingness to pay (WTP) for health.
Chapter 04
1. Figure 4.1 Reported CT infection rates by year in the United States, 1990–2010 (Chlamydia Surveillance Data 2011).
2. Figure 4.2 Reported CT infection rate by age and gender in the United States, 2010 (Chlamydia Surveillance Data 2011).
3. Figure 4.3 Disease progression tree for CT.
4. Figure 4.4 Age‐structured compartmental model for CT transmission and treatment.
5. Figure 4.5 Age‐structured compartmental model for CT infection.
6. Figure 4.6 Cost‐effectiveness analysis results.
Chapter 05
1. Figure 5.1 Joint distribution of risk and regret, in percent, between the robust scheme (R‐U‐RMP) and the FDA min‐risk scheme.
Chapter 06
1. Figure 6.1 Decision‐analytic modeling framework.
2. Figure 6.2 Markov model for chronic HCV.
3. Figure 6.3 A method for creating 16 life tables by sex (male, female), race (white, black), HCV infection status (positive, negative), and risk status (high, low).
4. Figure 6.4 Risk factors and HCV prevalence in birth cohort 1952–1961 (birth year).
5. Figure 6.5 Life expectancy of a 50‐year‐old by sex, race, HCV, and risk status.
6. Figure 6.6 HCV screening model schematics.
7. Figure 6.7 Liver fibrosis assessment model: Six strategies.
8. Figure 6.8 Patient’s health and technology change over time.
9. Figure 6.9 Model schematic of an integrated HCV care management system.
Chapter 07
1. Figure 7.1 Markov model of natural history of disease progression.
Chapter 08
1. Figure 8.1 An example of a DCL policy for optimal breast biopsy decision problem.
2. Figure 8.2 Optimal age‐dependent biopsy thresholds obtained by a finite‐horizon MDP model with two possible actions.
3. Figure 8.3 Optimal age‐dependent biopsy thresholds obtained by a finite‐horizon MDP model with three possible actions.
4. Figure 8.4 Optimal age‐dependent biopsy thresholds obtained by a budget constrained finite‐horizon MDP model.
Chapter 09
1. Figure 9.1 Markovian transitions among the prostate cancer states. Partially observable states are in the dotted box, completely observable states are in the solid box, and the triangle is the death from prostate cancer; transitions due to RP are represented by the dashed lines, and other transitions are represented using solid lines; death from other causes is possible from all states in the model but not shown in this figure.
2. Figure 9.2 Recurring screening, surveillance, and treatment decision process for prostate cancer at decision epochs t, t + 1, …, T. The vector denotes the patient’s belief state (probability of being in different health states) at decision stage i, PSA and denote having or not having a PSA test, B and denote having or not having a biopsy, and RP and denote performing or not performing RP, respectively. Death from any cause is possible but not shown in this figure.
3. Figure 9.3 Illustration of the bounds of the approximation algorithm for a two‐core‐state POMDP with a scalar of fully represent the belief state. (a) The true value function represented by a minimal α‐vector set. (b) A lower bound on the value function formed by an outer linearization represented by a subset of the minimal α‐vector set. (c) An upper bound on the value function formed by an inner linearization represented by a set of sampled belief points and their values. Note that dots in (a) and (c) denote the sampled belief points and dashed lines in (b) and (c) denote the true value function.
Chapter 10
1. Figure 10.1 Natural history of breast cancer.
2. Figure 10.2 Efficient frontier policies – perfect adherence case.
3. Figure 10.3 Efficient frontier policies – general population adherence case.
Chapter 11
1. Figure 11.1 Eight parallel state charts capturing individual health progression.
2. Figure 11.2 Annual smoking initiation probability.
3. Figure 11.3 Annual incidence rates for high cholesterol, hypertension, and diabetes.
4. Figure 11.4 User interface of the ABM of cardiovascular health.
5. Figure 11.5 Prevention effects of five alternative lifestyle interventions in different time horizons.
Chapter 12
1. Figure 12.1 The far left shows the cervix anatomy (top) and the associated HDR treatment with the applicator (bottom). The remaining images on the top show the Ir¹⁹² radioactive seeds and the Vienna ring CT‐MR applicator. The bottom middle shows the CT image of the catheters and seed positions with respect to the diseased cervix. The image is used for treatment‐planning optimization. The bottom right shows a transverse view with isodose curves overlaid.
2. Figure 12.2 This figure shows a CT treatment image for the cervix (left) and the resulting image with an overlay of the PET image for plan design and optimization (right). We can clearly see the PET tumor pockets (bright spots) inside the cervix.
3. Figure 12.3 (a) This figure shows the dose–volume histogram for a cervical cancer patient with a PET pocket that is 20.1% of the PTV. □: Standard HDR plan (PTV prescribed dose = 35 Gy); there is no separate curve for the PET. ○: Escalated PET (PTV prescribed dose = 35 Gy, PET ≥ 37 Gy). *: Escalated PET and reduced PTV dose (PTV prescribed dose = 33 Gy, PET ≥ 37 Gy). Δ: Escalated PET (PTV prescribed dose = 35 Gy, PET ≥ 40 Gy). : Escalated PET and reduced PTV dose (PTV prescribed dose = 33 Gy, PET ≥ 40 Gy). (b) This figure illustrates the dose–volume histogram for a cervical cancer patient with a PET pocket that is 10.4% of the PTV.
4. Figure 12.4 This figure contrasts the isodose curves of a standard plan (a), an escalated plan with 35 Gy of PTV prescribed dose (b), and an escalated plan with 33 Gy of PTV prescribed dose (c). We can observe the very conformed 100% isodose curves to the PTV contour. The 150% isodose curves in both escalated plans around the PET‐identified pockets are clearly absent from the standard plan. The 120% isodose curve is tighter in the bottom escalated plan, reflecting the lower dose to PTV because of the lower prescribed value (33 Gy vs. 35 Gy).
Chapter 13
1. Figure 13.1 IMRT treatment delivery devices. (a) Linear accelerator and (b) MLC (c) MLC impact on beam shape.
2. Figure 13.2 Visual evaluation measures of physical dose. (a) Example DVH and (b) CT slice with isodose lines and structure contours. LPG, left parotid gland; MB, mandible; RPG, right parotid gland; SC, spinal cord.
Chapter 14
1. Figure 14.1 Sliding window VMAT optimization. Fluence maps are optimized at the angles represented by the solid lines around the patient. The fluence maps are delivered by sweeping them out with left to right sweeps and then right to left sweeps over the angular sectors as depicted by the dotted lines. Provided the resolution of the angular spacing is fine enough, the discrepancy between the dose delivered by the fluence map at the solid line and the dose delivered when the fluence map is swept out by a moving gantry over the angular sector shown by the dotted curve will be tolerable.
Chapter 15
1. Figure 15.1 Model structure.
2. Figure 15.2 Illustrative impact of equations—death rate as a function of treatment spending.
3. Figure 15.3 Population growth in base case.
4. Figure 15.4 Marginal cost‐effectiveness of additional prevention spending as a function of treatment spending level.
5. Figure 15.5 Impact of discount rate on marginal cost‐effectiveness.
6. Figure 15.6 Total QALYs and marginal cost‐effectiveness as a function of spending mix.
7. Figure 15.7 Impact of prevention lag on optimal sending mix.
8. Figure 15.8 Impact of discount rate on optimal spending mix for alternative prevention lags.
9. Figure 15.9 Impact of time horizon on optimal spending mix.
10. Figure 15.10 Impact of research spending on population (not optimized).
Chapter 16
1. Figure 16.1 Simplified state transition diagram for the case of two medications, one blood pressure medication and one cholesterol medication. The occurrence of a stroke, a CHD event, or death can occur from any health state. When medications are initiated (actions denoted by the bold arrows), the risk factor values improve and the risk of adverse events is reduced.
2. Figure 16.2 Expected QALYs and expected costs for male patients when varying treatment policies—US guidelines or optimal treatment—and the maximum number of medications a patient can initiate over his or her lifetime.
3. Figure 16.3 Expected QALYs and expected costs for female patients when varying treatment policies—US guidelines or optimal treatment—and the maximum number of medications a patient can initiate over his or her lifetime.
Chapter 17
1. Figure 17.1 A tree diagram for two‐stage DAMIP classification and prediction of heart disease.

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CONTRIBUTORS

Oguzhan Alagoz, Department of Industrial and Systems Engineering, University of Wisconsin‐Madison, Madison, WI, USA

Dionne M. Aleman, Department of Mechanical & Industrial Engineering and Institute for Health Policy, Management & Evaluation, University of Toronto, Toronto, ON, Canada; Guided Therapeutics Core, Techna Institute, Toronto, ON, Canada

Sabina S. Alistar, Department of Management Science and Engineering, Stanford University, Palo Alto, CA, USA

Douglas R. Bish, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

Ebru K. Bish, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

Margaret L. Brandeau, Department of Management Science and Engineering, Stanford University, Palo Alto, CA, USA

Elizabeth S. Burnside, Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Jagpreet Chhatwal, Institute of Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

James C.H. Chu, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA

Ted Cohen, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT, USA

David Craft, Radiation Oncology, Department of Physics, Harvard Medical School, Boston, MA, USA

Brian T. Denton, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA

Hadi El‐Amine, Systems Engineering and Operations Research Department, George Mason University, Fairfax, VA, USA

Tarek Halabi, Radiation Oncology, Department of Physics, Harvard Medical School, Boston, MA, USA

Julia L. Higle, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA

Krystyna Kiel, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA

Nan Kong, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA

Mark A. Lawley, Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA; Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

Eva K. Lee, NSF‐Whitaker Center for Operations Research in Medicine and HealthCare, Georgia Institute of Technology, Atlanta, GA, USA; School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF I/UCRC Center for Health Organization Transformation, Arlington, VA, USA

Adriana Ley‐Chavez, Department of Industrial and Mechanical Engineering, Universidad de las Américas Puebla, Puebla, Mexico

Yan Li, Center for Health Innovation, The New York Academy of Medicine, New York, NY, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Shan Liu, Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA

Jennifer Mason Lobo, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

Mahboubeh Madadi, Department of Industrial Engineering, Louisiana Tech University, Ruston, LA, USA

George Miller, Center for Value in Health Care, Altarum, Ann Arbor, MI, USA

José A. Pagán, Center for Health Innovation, The New York Academy of Medicine, New York, NY, USA; Department of Public Health Policy and Management, College of Global Public Health, New York University, New York, NY, USA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA

Anthony D. Slonim, University of Nevada School of Medicine and Renown Health, Reno, NV, USA

Susan L. Stramer, Scientific Affairs, American Red Cross, Gaithersburg, MD, USA

Sze‐chuan Suen, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA

Alistair Templeton, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA

Yu Teng, Avenir Health, Glastonbury, CT, USA

Wanzhu Tu, Department of Biostatistics, Indiana University Medical School, Indianapolis, IN, USA

Sait Tunc, Department of Industrial and Systems Engineering, University of Wisconsin‐Madison, Madison, WI, USA

Reza Yaesoubi, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA

Rui Yao, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA

Fan Yuan, NSF‐Whitaker Center for Operations Research in Medicine and HealthCare, Georgia Institute of Technology, Atlanta, GA, USA; School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF I/UCRC Center for Health Organization Transformation, Arlington, VA, USA

Shengfan Zhang, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA

Jingyu Zhang, Enterprise Model Risk Management, Bank of America, Wilmington, DE, USA

PREFACE

Advances in disease prevention and treatment have greatly improved the quality of life of patients and the general population. However, it is challenging to truly harness these advances in patient‐centered medical decision‐making for the uncertainty associated with disease risks and care outcomes, as well as the complexity of the technologies. This book contains a collection of cutting‐edge research studies that apply decision analytics and optimization tools in disease prevention and treatment. Specifically, the book comprises the following three main parts.

Part 1: Infectious Disease Control and Management. Common infectious diseases are considered in this part, including tuberculosis (Chapter 1), HIV infection (Chapter 2), influenza (Chapter 3), chlamydia infection (Chapter 4), and hepatitis C (Chapter 6). Although not focusing on a specific type of infectious disease, Chapter 5 deals with the costs and efficacy of detecting infectious agents in donated blood. Controls and decisions investigated in this part include budget allocation (Chapter 2), school closure or children vaccination (Chapter 3), screening scheme design (Chapters 4 and 5), and a whole set of interventions (Chapter 6) such as behavior and public health interventions. Disease modeling techniques introduced in this part include microsimulation (Chapter 1), stochastic transmission dynamic model (Chapter 3), compartmental model (Chapter 4), and Markov‐based model (Chapter 6).

In this part, Chapters 1 and 6 provide excellent overviews of decision‐analytic modeling research in developing policy guidelines. Between the two chapters, the former focuses more on the disease modeling, whereas the latter focuses more on the analysis with a holistic view covering screening, monitoring, and treatment. In addition, Chapter 6 deals with long‐term management of an infectious disease, which helps make the transition to the second part of the book.

Part 2: Noncommunicable Disease Prevention. This part starts with Chapter 7, which examines screening strategies for the prevention of cervical cancers, which are mainly caused by human papillomavirus (HPV) infection. Chapter 7 concerns disease progression from the viewpoint of HPV infection rather than the infectious disease itself. The chapter provides a good connection with the first part of the book. Other prevalent noncommunicable diseases considered in this part include breast cancer (Chapters 8 and 10), prostate cancer (Chapter 9), and cardiovascular diseases (Chapter 11). Methodologies introduced in this part cover simulation with model‐based analyses for screening strategies (Chapter 7), Markov decision process (Chapter 8), partially observable Markov decision process (Chapter 9), cost‐effectiveness analysis under a partially observable Markov chain model (Chapter 10), and agent‐based modeling (Chapter 11).

Part 3: Treatment Technology and System. In this part, optimization studies of several treatment decisions and technologies are reported, including high‐dose‐rate brachytherapy (Chapter 12), intensity‐modulated radiation therapy (Chapters 13 and 14), volumetric modulated arc therapy (Chapter 14), cardiovascular disease prevention and treatment (Chapter 15), and various treatment decisions for type II diabetes (Chapter 16). Methodologies introduced comprise multiobjective, nonlinear, mixed‐integer programming model (Chapter 12), fluence map optimization (Chapter 13), sliding window optimization (Chapter 14), Markov modeling (Chapter 15), and Markov decision process (Chapter 16).

The book concludes with Chapter 17, which uniquely presents optimization‐based classification models for early detection of disease, risk prediction, and treatment design and outcome prediction. This chapter is expected to showcase extended potentials of optimization techniques and motivate more operations researchers to study biomedical data mining problems.

We believe this book can serve well as a handbook for researchers in the field of medical decision modeling, analysis, and optimization, a textbook for graduate‐level courses on OR applications in healthcare, and a reference for medical practitioners and public health policymakers with interest in health analytics.

Lastly, we would like to express our sincere gratitude to the following reviewers for taking their time to review book chapters and provide valuable feedback for our contributors in the blind‐review process: Turgay Ayer, Christine Barnett, Bjorn Berg, Margaret Brandeau, Brian Denton, Jeremy Goldhaber‐Fiebert, Shadi Hassani Goodarzi, Karen Hicklin, Julie Ivy, Amin Khademi, Anahita Khojandi, Yan Li, Jennifer Lobo, Maria Mayorga, Nisha Nataraj, Ehsan Salari, Burhan Sandikci, Joyatee Sarker, Carolina Vivas, Fan Wang, Xiaolei Xie, Yiwen Xu, and Yuanhui Zhang. We would also like to acknowledge the great support we received from Wiley editors, Sumathi Elangovan, Jon Gurstelle, Vishnu Narayanan, Kathleen Pagliaro, Vishnu Priya. R and former editor Susanne Steitz‐Filler.

Operations Research and Management Science

DECISION ANALYTICS AND OPTIMIZATION IN DISEASE PREVENTION AND TREATMENT

CONTRIBUTORS

PREFACE

PART 1
INFECTIOUS DISEASE CONTROL AND MANAGEMENT