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Abstract BackgroundPathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. ResultsTo address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. DynamicCtthreshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative rate. ConclusionIn conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, offers a promising solution for efficient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale.
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Active Sensing for Epidemic State Estimation Using ABM-guided Machine Learning
During an epidemic, it can be difficult to get an estimate of the actual number of people infected at any given time. This is due to multiple reasons, including some cases being asymptomatic and sick people not seeking healthcare for mild symptoms, among others. Large scale random sampling of the population for testing can be expensive, especially in the early stages of an epidemic, when tests are scarce. Here we show how an adaptive prevalence testing method can be developed to obtain a good estimate of the disease burden by learning to intelligently allocate a small number of tests for random testing of the population. Our approach uses a combination of an agent-based simulation and deep learning in an active sensing paradigm. We show that it is possible to get a good state estimate with relatively minimal prevalence testing, and that the trained system adapts quickly and performs well even if the disease parameters change.
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- Award ID(s):
- 1918656
- PAR ID:
- 10496154
- Publisher / Repository:
- AAMAS
- Date Published:
- Journal Name:
- Proceedings of the Multi-agent-based Simulation (MABS) Workshop
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
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