Georgia Clinical & Translational Science Alliance (GaCTSA)

基于机器学习的新冠肺炎考试中心有效分配 自适应采样 摘要 在社区管理和处理新冠肺炎的一项关键任务是进行诊断和/或抗体测试，以 识别患病的个体。这一信息对公共卫生官员估计流行率和传播至关重要， 并有效规划所需资源，如ICU床位、呼吸机、个人防护装备和医疗 工作人员。此外，有关感染者数量的信息可用于开发概率和统计模型 估计疾病的繁殖数量，并预测暴发的可能空间和时间轨迹。 这为规划行动和为社会疏远、学校制定政策和指导方针提供了重要信息 关闭、远程工作、社区封锁等。尽管诊断测试和识别 对于阳性病例，广泛的检测是一项具有挑战性的任务，特别是由于检测试剂盒和资源的数量有限。我们的 提出研究重点是开发基于机器学习的分配策略，确定最优分配策略 新冠肺炎考试中心的选址，包括移动和卫星中心，以最大限度地减少本地和全球预测 不确定性，最大限度地扩大地理覆盖范围，与时空疫情轨迹预测相关，并 提高疾病病例的有效识别能力。

EFFECTIVE ALLOCATION OF TEST CENTERS FOR COVID-19 USING MACHINE LEARNING AND ADAPTIVE SAMPLING ABSTRACT A critical task in managing and dealing with COVID-19 in communities is to perform diagnostic and/or antibody tests to identify diseased individuals. This information is critical to public health officials to estimate prevalence and transmission, and to effectively plan for required resources such as ICU beds, ventilators, personal protective equipment, and medical staff. Additionally, information on the number of infected people can be used to develop probabilistic and statistical models to estimate the reproduction number of the disease, and to predict the likely spatial and temporal trajectories of the outbreak. This provides vital information for planning actions and preparing policies and guidelines for social-distancing, school closures, remote work, community lockdown, etc. Despite the importance of diagnostic testing and identification of the positive cases, broad-scale testing is a challenging task particularly due to the limited number of test kits and resources. Our proposed research focuses on the development machine learning-based allocation strategies for determining the optimal location of COVID-19 test centers, including mobile and satellite centers, to minimize the local and global prediction uncertainties, maximize geographic coverage, associated with projections of spatio-temporal outbreak trajectories, and to improve efficient identification of diseased cases.