Screening multi‐dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID‐19

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 d...

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Bibliographic Details
Published in:Naval research logistics Vol. 69; no. 1; pp. 3 - 20
Main Authors: El Hajj, Hussein, Bish, Douglas R., Bish, Ebru K., Aprahamian, Hrayer
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2022
Wiley Subscription Services, Inc
Subjects:
Algorithms
Contact
Contact tracing
COVID-19
COVID‐19 testing
Design
Design optimization
Dimensions
Disease outbreaks
Heterogeneity
heterogeneous population
Infectious diseases
Information management
Medical screening
Mitigation
Pandemics
partition problem
pooled testing
Public health
resource allocation under limited resources
Resources
Signs and symptoms
Symptoms
Testing
Viral diseases
pooled testing
resource allocation under limited resources
heterogeneous population
COVID‐19 testing
partition problem
ISSN:0894-069X, 1520-6750, 1520-6750
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Summary: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.
Bibliography:Funding information
National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation, 1761842
History
Accepted by Sanjay Mehrotra, healthcare management
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HistoryAccepted by Sanjay Mehrotra, healthcare management
Funding information National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation, 1761842
ISSN:0894-069X
1520-6750
1520-6750
DOI:10.1002/nav.21985