Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Abstract Background Currently, coronavirus disease 2019 (COVID-19) has become the most severe infectious disease affecting the world, which has spread around the world to more than 200 countries in 2020. Until COVID-19 vaccine technology completely matures, nucleic acid testing is considered as an effective way to screen virus carriers and control the spread of the virus. Considering that the medical resources and infection rates are different across various countries and regions, if all infected areas adopt the traditional individual nucleic acid testing method, the workload will be heavy and time-consuming. Therefore, this will not lead to the control of the pandemic. Methods After Wuhan completed a citywide nucleic acid testing in May 2020, China basically controlled the spread of COVID-19 and entered the post-epidemic period. Since then, although some cities in China, such as Qingdao, Xinjiang, Beijing, Dalian, have experienced a local epidemic resurgence, the pandemic was quickly suppressed through wide-scale pooled nucleic acid testing methods. Combined with the successful experience of mass nucleic acid testing in China, this study introduces two main pooled testing methods used in two cities with a population of more than ten million people, Wuhan’s “five-in-one” and Qingdao's “ten-in-one” rapid pooled testing methods. This study proposes an improved method for optimising the second round of “ten-in-one” pooled testing, known as “the pentagram mini-pooled testing method”, which speeds up the testing process (as a result of reducing the numbers of testing by 40%) and significantly reduces the cost. Findings Qingdao's optimised “ten-in-one” pooled testing method quickly screens out the infections by running fewer testing. This study also mathematically examines the probabilistic principles and applicability conditions for pooled testing of COVID-19. Herein, the study theoretically determines the optimal number of samples that could successfully be combined into a pool under different infection rates. Then, it quantitatively discusses the applicability and principles for choosing the pooled testing instead of individual testing.Interpretation Overall, this research offers a reference for other countries with different infection rates to help them in implementing the mass testing for COVID-19 to reduce the spread of coronavirus.

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Fighting COVID-19 with Flexible Testing: Models and Insights

10.1101/2020.11.17.20233577 ◽

2020 ◽

Author(s):

Jiali Yu ◽

Zuo-Jun (Max) Shen

Keyword(s):

Analytical Method ◽

Empirical Studies ◽

New Wave ◽

Test Results ◽

Testing Strategy ◽

Testing Methods ◽

Reliable Test ◽

Pooled Testing ◽

Epidemic Dynamics ◽

Pool Sizes

AbstractIncreasing testing capacities plays a substantial role in safely reopening the economy and avoiding a new wave of COVID-19. Pooled testing can expand testing capabilities by pooling multiple individual samples, but it also raises accuracy concerns. In this study, we propose a flexible testing strategy that adapts pool sizes to epidemic dynamics. We present an analytical method to calculate the optimal pool size and the prevalence threshold between individual and pooled testing. Incorporating an epidemic model, we show pooled testing is more effective in containing epidemic outbreaks and can generate more reliable test results than individual testing because the reliability of test results is relevant to both testing methods and prevalence. Our study is the first to evaluate the interplay between pooled testing and a rapidly evolving outbreak to the best of our knowledge. Our results allay accuracy concerns about pooled testing and provide theoretical supports to empirical studies.

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