Noisy Pooled PCR for Virus Testing
Zhu, J.; Rivera, K.; Baron, D.
Show abstract
Fast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Optimal test allocation strategy during the COVID-19 pandemic and beyond 93%
- How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study 91%
- Using a supervised principal components analysis for variable selection in high-dimensional datasets reduces false discovery rates 91%
Similar papers in this journal
- Reconstruction Algorithms for DNA-Storage Systems 94%
- Estimating the SARS-CoV-2 infected population fraction and the infection-to-fatality ratio: A data-driven case study based on Swedish time series data 94%
- Preponderance of generalized chain functions in reconstructed Boolean models of biological networks 92%
Similar papers in this journal
- Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data 95%
- Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves 94%
- An exact method for quantifying the reliability of end-of-epidemic declarations in real time 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.