Problem definition: Infectious disease screening can be expensive and capacity constrained. We develop cost- and capacity-efficient testing designs for multidisease screening, considering (1) multiplexing (disease bundling), where one... more
Problem definition: Infectious disease screening can be expensive and capacity constrained. We develop cost- and capacity-efficient testing designs for multidisease screening, considering (1) multiplexing (disease bundling), where one assay detects multiple diseases using the same specimen (e.g., nasal swabs, blood), and (2) pooling (specimen bundling), where one assay is used on specimens from multiple subjects bundled in a testing pool. A testing design specifies an assay portfolio (mix of single-disease/multiplex assays) and a testing method (pooling/individual testing per assay). Methodology/results: We develop novel models for the nonlinear, combinatorial multidisease testing design problem: a deterministic model and a distribution-free, robust variation, which both generate Pareto frontiers for cost- and capacity-efficient designs. We characterize structural properties of optimal designs, formulate the deterministic counterpart of the robust model, and conduct a case study of respiratory diseases (including coronavirus disease 2019) with overlapping clinical presentation. Managerial implications: Key drivers of optimal designs include the assay cost function, the tester’s preference toward cost versus capacity efficiency, prevalence/coinfection rates, and for the robust model, prevalence uncertainty. When an optimal design uses multiple assays, it does so in conjunction with pooling, and it uses individual testing for at most one assay. Although prevalence uncertainty can be a design hurdle, especially for emerging or seasonal diseases, the integration of multiplexing and pooling, and the ordered partition property of optimal designs (under certain coinfection structures) serve to make the design more structurally robust to uncertainty. The robust model further increases robustness, and it is also practical as it needs only an uncertainty set around each disease prevalence. Our Pareto designs demonstrate the cost versus capacity trade-off and show that multiplexing-only or pooling-only designs need not be on the Pareto frontier. Our case study illustrates the benefits of optimally integrated designs over current practices and indicates a low price of robustness. Funding: This work was supported by the National Science Foundation [Grant 1761842]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0296 .
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Abstract Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via... more
Abstract Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via a single test. The pool design, i.e., the number and sizes of testing pools, has a substantial impact on estimation accuracy. Determining an optimal pool design is challenging, especially for emerging or seasonal diseases for which information on the status of the disease is unreliable or unavailable prior to testing. We develop novel optimization models for testing pool design under uncertainty and limited resources, and characterize structural properties of optimal pool designs. We apply our models to estimate the prevalence of West Nile virus in mosquitoes (the main vector of transmission to humans). Our findings suggest that estimation accuracy can be substantially improved over the status quo through the proposed optimal pool designs.
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BACKGROUNDBabesia microti causes transfusion‐transmitted babesiosis (TTB); currently, blood donor screening assays are unlicensed but used investigationally.STUDY DESIGN AND METHODSWe developed a decision tree model assessing the... more
BACKGROUNDBabesia microti causes transfusion‐transmitted babesiosis (TTB); currently, blood donor screening assays are unlicensed but used investigationally.STUDY DESIGN AND METHODSWe developed a decision tree model assessing the comparative‐ and cost‐effectiveness of B. microti blood donation screening strategies in endemic areas compared to the status quo (question regarding a history of babesiosis), including testing by: 1) universal antibody (Ab), 2) universal polymerase chain reaction (PCR), 3) universal Ab/PCR, and 4) recipient risk–targeted Ab/PCR. The model predicted the number of TTB cases, complicated TTB cases, cases averted, and quality‐adjusted life years (QALYs). Economic outcomes included each strategy's per‐donation cost, waste (number of infection‐free units incorrectly discarded), and waste index (number wasted units/number true positives). Sensitivity analyses examined uncertainty in transmission probabilities, prevalence rates, and other key model inputs.RESULTSUniversal PCR in four endemic states would prevent 24 to 31 TTB cases/100,000 units transfused (pht) at an incremental cost‐effectiveness ratio (ICER) of $26,000 to $44,000/QALY (transmission probability dependent) and waste index of zero. Universal Ab/PCR would prevent 33 to 42 TTB cases pht at an ICER of $54,000 to $83,000/QALY and waste index of 0.05. The questionnaire is most wasteful (99.62 units wasted pht; 208.62 waste index), followed by the risk‐targeted strategy (76.27 units wasted pht; 0.68 waste index). The model predicted zero cases of TTB or complicated TTB with universal Ab/PCR (versus [33, 42] and [13, 18] pht, respectively [no screening]). Results are highly sensitive to transmission probabilities.CONCLUSIONSUniversal PCR in endemic states is an effective blood donation screening strategy at a threshold of $50,000/QALY. Using a higher cost‐effectiveness ratio, universal Ab/PCR is the most effective strategy.
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... International Journal of Production Research , 29(8): 16451659. [Taylor & Francis Online], [Web of Science ®], [CSA] View all references; Pepe, 200461. Pepe, MS 2004. ... [Taylor & Francis Online], [Web of Science ®],... more
... International Journal of Production Research , 29(8): 16451659. [Taylor & Francis Online], [Web of Science ®], [CSA] View all references; Pepe, 200461. Pepe, MS 2004. ... [Taylor & Francis Online], [Web of Science ®], [CSA] View all references, and the references therein). ...
Research Interests: Resource Allocation, Medicine, Case Study, United States, Specification Tests, and 12 moreChagas disease, Health System, West nile virus, Human immunodeficiency virus, Lower Bound, Sub Saharan Africa, Decision Maker, Nonlinear Optimization, False Negative, Knapsack Problem, False Positive, and optimal algorithm
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Improving Newborn Screening for Genetic DiseasesScreening newborns for life-threatening genetic diseases is an important public health initiative. Cystic fibrosis is one of the most prevalent diseases in this context. As part of the... more
Improving Newborn Screening for Genetic DiseasesScreening newborns for life-threatening genetic diseases is an important public health initiative. Cystic fibrosis is one of the most prevalent diseases in this context. As part of the cystic fibrosis screening process, all states in the United States use multiple tests, including genetic tests that detect a subset of the more than 300 genetic variants (specific mutations) that cause cystic fibrosis. In “Optimal Genetic Screening for Cystic Fibrosis,” El-Hajj, D.R. Bish, and E.K. Bish develop a decision support model to select which genetic variants to screen for, considering the trade-off between classification accuracy and testing cost, and the technological constraints that limit the number of variants selected. Because variant prevalence rates are highly uncertain, a robust optimization framework is developed. Further, two commonly used cystic fibrosis screening processes are analytically compared, and conditions under which each process dominates are established. A case study based on published data are provided.
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Testing multiple subjects within a group, with a single test applied to the group (i.e., group testing), is an important tool for classifying populations as positive or negative for a specific binary characteristic in an efficient manner.... more
Testing multiple subjects within a group, with a single test applied to the group (i.e., group testing), is an important tool for classifying populations as positive or negative for a specific binary characteristic in an efficient manner. We study the design of easily implementable, static group testing schemes that take into account operational constraints, heterogeneous populations, and uncertainty in subject risk, while considering classification accuracy- and robustness-based objectives. We derive key structural properties of optimal risk-based designs and show that the problem can be formulated as network flow problems. Our reformulation involves computationally expensive high-dimensional integrals. We develop an analytical expression that eliminates the need to compute high-dimensional integrals, drastically improving the tractability of constructing the underlying network. We demonstrate the impact through a case study on chlamydia screening, which leads to the following insi...
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... International Journal of Production Research , 29(8): 16451659. [Taylor & Francis Online], [Web of Science ®], [CSA] View all references; Pepe, 200461. Pepe, MS 2004. ... [Taylor & Francis Online], [Web of Science ®],... more
... International Journal of Production Research , 29(8): 16451659. [Taylor & Francis Online], [Web of Science ®], [CSA] View all references; Pepe, 200461. Pepe, MS 2004. ... [Taylor & Francis Online], [Web of Science ®], [CSA] View all references, and the references therein). ...
Research Interests: Resource Allocation, Medicine, Case Study, United States, Specification Tests, and 12 moreChagas disease, Health System, West nile virus, Human immunodeficiency virus, Lower Bound, Sub Saharan Africa, Decision Maker, Nonlinear Optimization, False Negative, Knapsack Problem, False Positive, and optimal algorithm
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Newborn screening (NBS) is a state-level initiative that detects life-threatening genetic disorders for which early treatment can substantially improve health outcomes. Cystic fibrosis (CF) is among the most prevalent disorders in NBS. CF... more
Newborn screening (NBS) is a state-level initiative that detects life-threatening genetic disorders for which early treatment can substantially improve health outcomes. Cystic fibrosis (CF) is among the most prevalent disorders in NBS. CF can be caused by a large number of mutation variants to the CFTR gene. Most states use a multitest CF screening process that includes a genetic test (DNA). However, due to cost concerns, DNA is used only on a small subset of newborns (based on a low-cost biomarker test with low classification accuracy), and only for a small subset of CF-causing variants. To overcome the cost barriers of expanded genetic testing, we explore a novel approach, of multipanel pooled DNA testing. This approach leads not only to a novel optimization problem (variant selection for screening, variant partition into multipanels, and pool size determination for each panel), but also to novel CF NBS processes. We establish key structural properties of optimal multipanel pooled...
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Importance Screening and vaccination are essential in the fight against infectious diseases, but need to be integrated and customized based on community and disease characteristics. Objective To develop effective screening and vaccination... more
Importance Screening and vaccination are essential in the fight against infectious diseases, but need to be integrated and customized based on community and disease characteristics. Objective To develop effective screening and vaccination strategies, customized for a college campus, to reduce COVID-19 infections, hospitalizations, deaths, and peak hospitalizations. Design, setting, and participants We construct a compartmental model of disease spread under vaccination and routine screening, and study the efficacy of four mitigation strategies (routine screening only, vaccination only, vaccination with partial or full routine screening), and a no-intervention strategy. The study setting is a hypothetical college campus of 5,000 students and 455 faculty members during the Fall 2021 academic semester, when the Delta variant was the predominant strain. For sensitivity analysis, we vary the screening frequency, daily vaccination rate, initial vaccine coverage, and screening and vaccinati...
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Limited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the... more
Limited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the pool tests positive, then new samples from the collected specimens are individually tested, while if the pool tests negative, the subjects are classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. We develop a mathematical model to determine the best pool size for different risk groups, based on each group’s estimated COVID-19 prevalence. Our approach takes into consideration the sensitivity and specificity of the test, and a dynamic and uncertain prevalence, and provides a robust pool size for each group. For practical relevance, we also develop a companion COVID-19 pooling design tool (through a spread sheet). To demonstrate the potential value of poo...
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We propose a novel analytic approach for the comparative statics analysis of multiproduct multiresource newsvendor networks under responsive pricing. Our approach involves exploiting the properties of the primal mathematical programming... more
We propose a novel analytic approach for the comparative statics analysis of multiproduct multiresource newsvendor networks under responsive pricing. Our approach involves exploiting the properties of the primal mathematical programming formulation and of the dual variables and linking those properties to the concept of convex orders and to properties of the underlying demand function. The use of convex orders allows us to establish our main results without restriction to a specific demand distribution. A major strength of our approach is that it is “scalable,” i.e., it applies to newsvendor networks with any number of “nonindependent” (i.e., demand or resource sharing) products and resources, without an exponential increase in effort as problem size increases. This is unlike the current approaches commonly used in the operations management literature, which typically involve a parametric analysis of the recourse problem, followed by the use of Jacobians and the implicit function th...
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Abstract Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via... more
Abstract Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via a single test. The pool design, i.e., the number and sizes of testing pools, has a substantial impact on estimation accuracy. Determining an optimal pool design is challenging, especially for emerging or seasonal diseases for which information on the status of the disease is unreliable or unavailable prior to testing. We develop novel optimization models for testing pool design under uncertainty and limited resources, and characterize structural properties of optimal pool designs. We apply our models to estimate the prevalence of West Nile virus in mosquitoes (the main vector of transmission to humans). Our findings suggest that estimation accuracy can be substantially improved over the status quo through the proposed optimal pool designs.
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We study an important problem faced by Blood Centers, of selecting screening tests for donated blood to reduce the risk of “transfusion-transmitted infectious diseases” (TTIs), including the human immunodeficiency virus (HIV), hepatitis... more
We study an important problem faced by Blood Centers, of selecting screening tests for donated blood to reduce the risk of “transfusion-transmitted infectious diseases” (TTIs), including the human immunodeficiency virus (HIV), hepatitis viruses, human T-cell lymphotropic virus, syphilis, West Nile Virus, and Chagas’ Disease. This decision has a significant impact on health care quality in both developed and developing countries.
Research Interests: Decision Making, Resource Allocation, Multidisciplinary, Developing Country, Infectious Disease, and 10 moreHealth Care Quality, Specification Tests, Constructive Mathematics, Chagas disease, West nile virus, Human immunodeficiency virus, Decision Problem, Decision Maker, Sensitivity and Specificity, and Objective function
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Group testing (i.e., testing multiple subjects simultaneously with a single test) is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease). We study... more
Group testing (i.e., testing multiple subjects simultaneously with a single test) is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease). We study optimal group testing designs under subject-specific risk characteristics and imperfect tests, considering classification accuracy-, efficiency- and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties allow us to model the testing design problems as partitioning problems, develop efficient algorithms, and derive insights on equity versus accuracy trade-off. One of our models reduces to a constrained shortest path problem, for a special case of which we develop a polynomial-time algorithm. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. We demonstrate the value of optimal ris...
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To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful... more
To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.
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An accurate estimation of the residual risk of transfusion-transmittable infections (TTIs), which includes the human immunodeficiency virus (HIV), hepatitis B and C viruses (HBV, HCV), among others, is essential, as it provides the basis... more
An accurate estimation of the residual risk of transfusion-transmittable infections (TTIs), which includes the human immunodeficiency virus (HIV), hepatitis B and C viruses (HBV, HCV), among others, is essential, as it provides the basis for blood screening assay selection. While the highly sensitive nucleic acid testing (NAT) technology has recently become available, it is highly costly. As a result, in most countries, including the United States, the current practice for human immunodeficiency virus, hepatitis B virus, hepatitis C virus screening in donated blood is to use pooled NAT. Pooling substantially reduces the number of tests required, especially for TTIs with low prevalence rates. However, pooling also reduces the test's sensitivity, because the viral load of an infected sample might be diluted by the other samples in the pool to the point that it is not detectable by NAT, leading to potential TTIs. Infection-free blood may also be falsely discarded, resulting in wasted blood. We derive expressions for the residual risk, expected number of tests, and expected amount of blood wasted for various two-stage pooled testing schemes, including Dorfman-type and array-based testing, considering infection progression, infectivity of the blood unit, and imperfect tests under the dilution effect and measurement errors. We then calibrate our model using published data and perform a case study. Our study offers key insights on how pooled NAT, used within different testing schemes, contributes to the safety and cost of blood. Copyright © 2016 John Wiley & Sons, Ltd.
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Planning for a bus-based regional evacuation is essential for emergency preparedness, especially for regions threatened by hurricanes that have large numbers of transit-dependent people. While this difficult planning problem is a variant... more
Planning for a bus-based regional evacuation is essential for emergency preparedness, especially for regions threatened by hurricanes that have large numbers of transit-dependent people. While this difficult planning problem is a variant of the vehicle routing problem, it differs in some key aspects, including the objective and the network structure (e.g., capacitated shelters). This problem is not well studied. In this paper we introduce a model specifically designed for bus-based evacuation planning, along with two mathematical programming formulations, which are used to develop a heuristic algorithm. Using these models, we analyze the differences in the structural properties of optimal solutions between this problem and traditional vehicle routing problems.
Research Interests: Applied Mathematics, Mathematical Programming, Disaster Management, Business and Management, Vehicle routing, and 8 moreVehicle Routing Problem, Emergency Preparedness, Mixed Integer Programming, Structural Properties, Numerical Analysis and Computational Mathematics, Network structure, Heuristic algorithm, and Optimal Solution
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Babesia microti causes transfusion-transmitted babesiosis (TTB); currently, blood donor screening assays are unlicensed but used investigationally. We developed a decision tree model assessing the comparative- and cost-effectiveness of B.... more
Babesia microti causes transfusion-transmitted babesiosis (TTB); currently, blood donor screening assays are unlicensed but used investigationally. We developed a decision tree model assessing the comparative- and cost-effectiveness of B. microti blood donation screening strategies in endemic areas compared to the status quo (question regarding a history of babesiosis), including testing by: 1) universal antibody (Ab), 2) universal polymerase chain reaction (PCR), 3) universal Ab/PCR, and 4) recipient risk-targeted Ab/PCR. The model predicted the number of TTB cases, complicated TTB cases, cases averted, and quality-adjusted life years (QALYs). Economic outcomes included each strategy's per-donation cost, waste (number of infection-free units incorrectly discarded), and waste index (number wasted units/number true positives). Sensitivity analyses examined uncertainty in transmission probabilities, prevalence rates, and other key model inputs. Universal PCR in four endemic states would prevent 24 to 31 TTB cases/100,000 units transfused (pht) at an incremental cost-effectiveness ratio (ICER) of $26,000 to $44,000/QALY (transmission probability dependent) and waste index of zero. Universal Ab/PCR would prevent 33 to 42 TTB cases pht at an ICER of $54,000 to $83,000/QALY and waste index of 0.05. The questionnaire is most wasteful (99.62 units wasted pht; 208.62 waste index), followed by the risk-targeted strategy (76.27 units wasted pht; 0.68 waste index). The model predicted zero cases of TTB or complicated TTB with universal Ab/PCR (versus [33, 42] and [13, 18] pht, respectively [no screening]). Results are highly sensitive to transmission probabilities. Universal PCR in endemic states is an effective blood donation screening strategy at a threshold of $50,000/QALY. Using a higher cost-effectiveness ratio, universal Ab/PCR is the most effective strategy.