基于计算机科学二叉搜索算法的混合样本检测试剂盒优化利用

P. R. Negi
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引用次数: 3

摘要

在新型冠状病毒(COVID-19)全球疫情持续爆发的背景下,世界上大多数国家都在努力最大限度地提高检测能力,以应对COVID-19。对于像印度这样的发展中国家来说,这项COVID-19检测能力建设活动似乎是一项艰巨的任务,特别是由于全球检测试剂盒短缺以及国内生产检测试剂盒的初级阶段。世界各地的研究团体和政府组织正试图设计一种方法来解决全球检测工具短缺的问题。汇集样本测试是最近出现的一种这样的方法,它在阳性率低的情况下最佳地利用了测试试剂盒。为了优化检测试剂盒的使用,本文对计算机科学、数据结构二叉搜索算法在COVID-19混合样本检测方法中的应用进行了理论研究。文章解释了如何使用新方法进一步减少鉴定个别阳性标本所需的检测试剂盒数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimum utilization of COVID-19 Testing Kits using the Principles of Computer Science Binary Search Algorithm in Pooled Sample Testing
Amid the ongoing global outbreak of Novel Corona Virus (COVID-19) epidemic, most countries of the world are working to maximize their testing capacity to fight against COVID-19. For developing countries like India, this COVID-19 testing capacity building activity seems a monstrous task, specifically due to a global shortage of testing kits and naive phase of domestic productions of testing kits. The research communities and government organizations across the world are trying to devise a methodology to grapple with this global shortage of testing kits. Pooled Sample Testing is one such methodology that has emerged recently which utilizes the testing kits optimally, where the positivity rate is low. For optimal utilization of testing kits, this article provides the theoretical study of using Computer Science, Data Structure Binary Search Algorithm in Pooled Sample testing methodology of COVID-19. The article explains how using new methodology there can be a further reduction in the number of testing kits required for identifying individual positive specimens.
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