The COVID-19 pandemic has highlighted the need for increased and more dynamic access to healthcare resources. It has also revealed a novel complication to the effective delivery of health resources to communities, which we call the final inch problem. In our recent COVID-19 pop-up testing work with Columbus Public Health and the Ohio National Guard, we have observed that, even when a healthcare-related service is transported directly to community members, it is not a given that they will use that service. We argue that crossing this final inch will require us to reframe public health initiatives through the lens of joint activity: a partnership between healthcare institutions and the public. Our work focuses on three questions. How do we engage with the public and foster common ground between people and our healthcare providers? As part of this, how can we work with the community to determine where to dynamically direct our resources on a given day? Finally, when we show up at the “right” place, will the community join us? Our recent work creating and deploying the Flexible Algorithmic, Adaptive Surveillance Testing (FAAST) has generated promising insights to answer these questions. Throughout our initial tests, we observed a continuous increase in community participation as well as increased positivity through multiple iterations of the program. We consistently overrepresented traditionally underserved minority groups in all testing locations as well. Insights for convincing communities to participate in pop-up testing may yield repeatable, generalizable strategies by which public health officials and healthcare providers may cross the final inch. Through establishing and nurturing reliable community relationships, public health institutions working in partnership with their constituent communities can proactively monitor the health of their communities, thereby facilitating a more resilient response to emerging threats.

Quantitative evaluations of human-machine teams (HMTs) are desperately needed to ensure technological implementations are helpful rather than harmful to overall system performance; however, as machines increasingly behave like active cognitive teammates, traditional evaluation strategies risk overestimating HMT capabilities. Areliable HMT evaluation method should include multiple high-resolution, continuous measures for both system performance and system challenges that can be implemented unobtrusively in real-time operations. In our prior work, we proposed joint activity testing (JAT) as acandidate evaluation framework to satisfy these requirements. Preliminary efforts with asingle dimension of performance and challenge have indicated that the method can identify the additive benefits of joint activity with aspecific technology. In this paper, we explore the operationalization of multi-dimensional JAT by synthesizing our work in two intelligence and two healthcare domains. The patterns observed between domains will guide future JAT, reveal paths towards real-time implementation, and spark future research evaluating resilience.

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

BACKGROUND The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for detecting cases of infectious disease, deployed here to diagnose SARS-CoV-2. OBJECTIVE This study’s objective was to evaluate a Bayesian search algorithm to target hotspots of viral transmission in the community with the objective of detecting the most cases over time across multiple locations in Columbus, Ohio from August to October 2021. METHODS 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 expected value of success in finding new cases of SARS-CoV-2 based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University (OSU), Wake Forest University, the Ohio Department of Health (ODH), the Ohio National Guard (ONG) and the Columbus Metropolitan Libraries (CML) conducted a study of bandit algorithms to maximize the detection of new cases in 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 events. Our team conducted between 0 and 56 tests at the 16 testing sessions, with an overall average of 25.3 tests conducted per session 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. RESULTS 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 under-served communities, particularly African Americans, with the pool of participants over-representing these people relative to the demographic profile of the local ZIP code in which testing sites were located. CONCLUSIONS 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.