Department of Animal Science, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil.
Anhembi Morumbi University, Piracicaba, Brazil.
Sci Rep. 2022 Jul 13;12(1):11854. doi: 10.1038/s41598-022-14626-8.
The COVID-19 has severely affected economies and health systems around the world. Mass testing could work as a powerful alternative to restrain disease dissemination, but the shortage of reagents is a limiting factor. A solution to optimize test usage relies on 'grouping' or 'pooling' strategies, which combine a set of individuals in a single reaction. To compare different group testing configurations, we developed the poolingr package, which performs an innovative hybrid in silico/in vitro approach to search for optimal testing configurations. We used 6759 viral load values, observed in 2389 positive individuals, to simulate a wide range of scenarios. We found that larger groups (>100) framed into multi-stage setups (up to six stages) could largely boost the power to detect spreaders. Although the boost was dependent on the disease prevalence, our method could point to cheaper grouping schemes to better mitigate COVID-19 dissemination through identification and quarantine recommendation for positive individuals.
COVID-19 疫情严重影响了全球的经济和卫生系统。大规模检测可以作为一种强有力的替代方法来控制疾病传播，但试剂短缺是一个限制因素。优化检测使用的解决方案依赖于“分组”或“混样”策略，即将一组人组合在一个反应中。为了比较不同的分组检测配置，我们开发了 poolingr 包，该包采用了一种创新的混合体内/体外方法来搜索最佳的检测配置。我们使用了 2389 名阳性个体的 6759 个病毒载量值来模拟广泛的场景。我们发现，更大的组（>100 个）可以分为多阶段设置（最多六个阶段），可以大大提高发现传播者的能力。虽然这种提升依赖于疾病的流行程度，但我们的方法可以指出更经济的分组方案，通过对阳性个体进行识别和隔离建议，更好地减轻 COVID-19 的传播。
