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Testing provides essential information for managing infectious disease outbreaks, such as the COVID‐19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease‐positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false‐negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data‐driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID‐19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID‐19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.

 

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, thepaneldesign), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.

 

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 .

 

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 DNA designs; develop a methodology that generates a family of optimal designs at different costs; and characterize the conditions under which a 1-panel versus a multipanel design is optimal. This methodology can assist decision-makers to design a screening process, considering the cost versus accuracy trade-off. Our case study, based on published CF NBS data from the state of New York, indicates that the multipanel and pooling aspects of genetic testing work synergistically, and the proposed NBS processes have the potential to substantially improve both the efficiency and accuracy of current practices. This paper was accepted by Stefan Scholtes, healthcare management. 

Cystic fibrosis (CF) is a life-threatening genetic disorder. Early treatment of CF-positive newborns can extend life span, improve quality of life, and reduce healthcare expenditures. As a result, newborns are screened for CF throughout the United States. Genetic testing is costly; therefore, CF screening processes start with a relatively inexpensive but not highly accurate biomarker test. Newborns with elevated biomarker levels are further screened via genetic testing for a panel of variants (types of mutations), selected from among hundreds of CF-causing variants, and newborns with mutations detected are referred for diagnostic testing, which corrects any false-positive screening results. Conversely, a false negative represents a missed CF diagnosis and delayed treatment. Therefore, an important decision is which CF-causing variants to include in the genetic testing panel so as to reduce the probability of a false negative under a testing budget that limits the number of variants in the panel. We develop novel deterministic and robust optimization models and identify key structural properties of optimal genetic testing panels. These properties lead to efficient, exact algorithms and key insights. Our case study underscores the value of our optimization-based approaches for CF newborn screening compared with current practices. Our findings have important implications for public policy. 

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 pooling, we study COVID-19 screening using testing data from Iceland for the period, February-28-2020 to June-14-2020, for subjects stratified into high- and low-risk groups. We implement the robust pooling strategy within a sequential framework, which updates pool sizes each week, for each risk group, based on prior week’s testing data. Robust pooling reduces the number of tests, over individual testing, by 88.5% to 90.2%, and 54.2% to 61.9%, respectively, for the low-risk and high-risk groups (based on test sensitivity values in the range [0.71, 0.98] as reported in the literature). This results in much shorter times, on average, to get the test results compared to individual testing (due to the higher testing throughput), and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening. 

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 insights: (1) Risk-based designs are shown to be less expensive, more accurate, and more robust than current practices. (2) The performance of static risk-based schemes comprised of only two group sizes is comparable to those comprised of many group sizes. (3) Static risk-based schemes are an effective alternative to more complicated dynamic schemes. (4) An expectation-based formulation captures almost all benefits of a static risk-based scheme.