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The screening testing is an effective tool to control the early spread of an infectious disease such as COVID‐19. When the total testing capacity is limited, we aim to optimally allocate testing resources amongncounties. We build a (weighted) commute network on counties, with the weight between two counties a decreasing function of their traffic distance. We introduce a network‐based disease model, in which the number of newly confirmed cases of each county depends on the numbers of hidden cases of all counties on the network. Our proposed testing allocation strategy first uses historical data to learn model parameters and then decides the testing rates for all counties by solving an optimization problem. We apply the method on the commute networks of Massachusetts, USA and Hubei, China, and observe its advantages over testing allocation strategies that ignore the network structure. Our approach can also be extended to study the vaccine allocation problem.
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CovIdentify Dataset
This dataset supports the study "A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19" which developed an Intelligent Testing Allocation (ITA) method. The study demonstrated the efficacy of using continuous digital biomarkers like resting heart rate and steps to enhance COVID-19 diagnostic testing positivity rates. The findings suggest significant potential for large-scale, symptom-independent surveillance testing to alleviate diagnostic test shortages. The provided data is from the CovIdentify study launched by Duke's BIG IDEAs Lab in the Biomedical Engineering Department. From April 2nd, 2020 to May 25th, 2021, 2,887 participants connected their smartwatches to the CovIdentify platform, including 1,689 Garmin, 1,091 Fitbit, and 107 Apple smartwatches
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- Award ID(s):
- 2339669
- PAR ID:
- 10587594
- Publisher / Repository:
- PhysioNet
- Date Published:
- Format(s):
- Medium: X
- Location:
- Durham, NC
- Institution:
- Duke University
- Sponsoring Org:
- National Science Foundation
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