背景:灵活的自适应算法监测测试(FAAST)计划代表了一种改进新的传染病病例检测的创新方法;它在这里被部署用于筛查和诊断SARS-CoV-2。随着COVID-19治疗的出现,发现感染SARS-CoV-2的个体是临床和公共卫生的当务之急。虽然这些类型的贝叶斯搜索算法在其他设置中被广泛使用(例如,寻找被击落的飞机,在潜艇回收中,并协助石油勘探),这是该领域首次将贝叶斯自适应方法用于主动疾病监测.
目的:这项研究的目的是评估贝叶斯搜索算法,以针对社区中SARS-CoV-2传播的热点,目的是随着时间的推移,在哥伦布的多个地点发现大多数病例。俄亥俄州,2021年8月至10月。
方法:用于此项目的直接弹出式SARS-CoV-2测试的算法基于汤普森采样,其中的目的是根据每个测试地点的先验概率分布的采样,最大限度地提高一组测试地点中诊断出的SARS-CoV-2新病例的平均数量。耶鲁大学之间的学术-政府伙伴关系,俄亥俄州立大学,威克森林大学,俄亥俄州卫生部,俄亥俄州国民警卫队,哥伦布大都会图书馆对土匪算法进行了研究,以最大限度地检测2021年俄亥俄州城市的SARS-CoV-2新病例。该计划在哥伦布的13个地点建立了弹出式COVID-19测试站点,包括图书馆分支机构,娱乐和社区中心,电影院,无家可归者收容所,家庭服务中心,和社区活动网站。我们的团队在16个测试事件中进行了0到56个测试,每个事件进行的总体平均值为25.3,移动平均值随时间增加。向那些接近弹出式网站以鼓励他们参与的人提供了小型激励措施,包括礼品卡和带回家的快速抗原测试。
结果:随着时间的推移,正如预期的那样,贝叶斯搜索算法将测试工作定向到新诊断结果较高的位置.令人惊讶的是,该算法的使用还最大限度地提高了服务不足社区的少数民族居民的案件识别能力,尤其是非洲裔美国人,相对于测试地点所在的当地邮政编码的人口统计概况,参与者人数过多。
结论:这项研究表明,使用bandit算法的弹出式测试策略可以在大流行期间在城市环境中可行地部署。这是首次在现实世界中使用这些算法进行疾病监测,并且代表了评估其使用有效性的关键步骤,以最大程度地检测未确诊的SARS-CoV-2和其他感染病例。比如HIV。
The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field.
This study\'s objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021.
The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation.
Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located.
This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.