论文标题
优化在社交网络上的SARS-COV-2合并测试策略，以用于低资源设置
Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
论文作者
论文摘要
控制COVID-19大流行是一项紧迫的全球挑战。 SARS-COV-2的快速地理传播直接反映了社会结构。在有效的疫苗和治疗得到广泛使用之前，我们必须依靠替代性的非药物干预措施，包括频率测试，接触跟踪，社交距离，戴口罩和洗手，作为公共卫生实践，以减慢疾病的传播。无论频繁测试是没有其他选择的关键。我们提出了一种网络方法，以确定最佳的低资源设定面向池测试策略，该策略以少量测试和几轮测试识别感染者，在病毒的较低患病率下。我们模拟了隔离区下社会的随机感染曲线。允许进行一些社交互动是可以使COVID-19曲线保持平坦的可能性。但是，可以从策略上获得类似的结果，以搜索和隔离感染者，以维护更健康的社会结构。在这里，我们分析哪些是遏制病毒的最佳策略，该病毒应用算法结合样品并分组进行测试[1]。使用该算法保持感染曲线平坦的相关参数是在报告高感染率的区域测试的乳制频率。另一方面，算法效率较低，用于随机搜索感染者。
Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequency testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a small number of tests and few rounds of testing, at low prevalence of the virus. We simulate stochastic infection curves on societies under quarantine. Allowing some social interaction is possible to keep the COVID-19 curve flat. However, similar results can be strategically obtained searching and isolating infected persons to preserve a healthier social structure. Here, we analyze which are the best strategies to contain the virus applying an algorithm that combine samples and testing them in groups [1]. A relevant parameter to keep infection curves flat using this algorithm is the dairy frequency of testing at zones where a high infection rate is reported. On the other hand, the algorithm efficiency is low for random search of infected people.
