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 .
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 .
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.
Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman’s method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman’s method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman’s method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.