PoolParty: Overcome testing capacity with information theory

Overcoming the current testing capacity with an information theoretical approach. How to opimize pool testing with respect efficiency and reliability.

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Quick introduction

COVID-19 has been taking over the world like a wildfire and the economies of countries have succumbed to it. One of the main reasons being inefficient testing strategies. Not only does a country need high testing capacity but also faster testing capability. With a faster and efficient testing strategy, a country knows where it stands amidst this pandemic. For instance, South Korea in the first week of its critical period had already conducted 300,000 tests, establishing 600 test centers across the country. It had halved the number of infections in just a week [1]. On the other end of the spectrum, Italy and USA has/is failing to cope up with the pandemic. So much so, Italy could not keep up with the pandemic because they did not know where they stood in the curve because of the inefficiency in testing samples. The delay in testing has and is still costing lives [1-3]. The take-home message from these scenarios is that high capacity testing provides more information upon which a country could take a standpoint and act in preparedness. So, we propose a strategy for efficient testing of SARS-COV-2 by sample pooling.

Practical Mumbo-jumbo

RT-qPCR is a diagnostic technique used for the detection of COVID-19. It is powerful, sensitive and efficient. But the detection strategy is not efficient enough. A single pack of commercially available testing kits can be used for 200 such tests. With high demand, the manufacturers have a hard time dispensing sufficient kits and the laboratories have a hard time testing multiple samples. The sensitivity and specificity of the technique is 94% and 96% respectively [4]. The current strategy till now is to use single reactions per each sample (200 tests per kit). Our strategy is to pool multiple samples together for better data obtainment.

Some data on the infection

The severity of the viral infection is characterized by its number of RNA copies in the patient sample. Clinical samples have shown around 1.6 x 106 RNA copies/ml [5]. The minimum amount required to analyze the sample is 2000 RNA copies/ml [6]. This would technically mean that we could dilute a sample 800 times and still be able to detect it. Pooling multiple samples would dilute the samples, but within a certain range would still be detectable. A test concluded that an interpretable signal was obtainable by pooling 31 samples together [7]. For a good strategy design, we would require as many trustable data we could get. The sensitivity & specificity, False Positive and negative rates of RT-qPCR, real-time statistics of the infection in a country, and we hope to also design a strategy based on the symptoms. Currently 90% of the patients display symptoms of fever, 75% cough, 40 – 70% fatigue and 20 – 25% fluid secretion (sputum) [8].

Inspiration

Faster detection of the infection could save lives. Recently, a study done in New York City stated that they could have saved 50-80% of the lives if they had acted a week faster [9].

What it does

Evaluates performance of many pooling strategies based on RT-qPCR model test simulations.

When is it used?

Our strategies seem to be much more efficient when is practiced at the very early and late stages of the infection when the prevalence is low.

How we built it

We took a completely scientific standpoint - took all of data from real-life situations with the least assumptions possible - optimized the strategy by figuring out the error rates in RT-qPCR - GUI approach.

Challenges we ran into

Literature review and missing parentheses, which was pretty annoying.

What's next for PoolParty?

Trying out more strategies and add more parameters (like symptoms. age, etc.) based on the current statistics for better optimization.

References

  1. https://www.globalgovernmentforum.com/lessons-from-the-frontline-how-to-stop-the-spread-of-covid-19/
  2. https://ourworldindata.org/covid-testing
  3. https://www.euronews.com/2020/04/01/covid-19-numbers-stabilising-but-more-testing-is-needed-says-italian-health-ministry
  4. Alvarez-Martínez, M. J., Miró, J. M., Valls, M. E., Moreno, A., Rivas, P. V., Solé, M., ... & Zar, H. J. (2006). Sensitivity and specificity of nested and real-time PCR for the detection of Pneumocystis jiroveci in clinical specimens. Diagnostic microbiology and infectious disease, 56(2), 153-160.
  5. Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020). Detection of SARS-CoV-2 in different types of clinical specimens. Jama.
  6. Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., ... & Mulders, D. G. (2020). Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance, 25(3), 2000045.
  7. Yelin, I., Aharony, N., Shaer-Tamar, E., Argoetti, A., Messer, E., Berenbaum, D., ... & Mandel-Gutfreund, Y. (2020). Evaluation of COVID-19 RT-qPCR test in multi-sample pools. medRxiv.
  8. Thomas-Rüddel, D., Winning, J., Dickmann, P., Ouart, D., Kortgen, A., Janssens, U., & Bauer, M. (2020). Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists March 2020. Der Anaesthesist, 1-10.
  9. https://www.livescience.com/why-covid19-coronavirus-deaths-high-new-york.html

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