Cost-Effectiveness of Workplace Covid-19 Self-Testing: A Mathematical Modeling Study

Background: As COVID-19 cases persist, one potential intervention to reduce absenteeism in the workplace due to COVID-19 is to use rapid antigen diagnostics to mitigate the spread of SARS-CoV-2. Furthermore, routine testing in the workplace offers an avenue to reaching a large proportion of the population which could lead to a greater community impact beyond solely mitigating transmission events that occur in the workplace. We sought to identify the most cost-effective workplace testing strategies at the community level and within individual workplaces. Method(s): We used two models to understand how SARS-CoV-2 AgRDTs could best be implemented within the workplace to mitigate the spread of COVID-19. In our community-level dynamic transmission model, PATAT, we evaluated the impact of symptomatic testing and asymptomatic testing of a fixed proportion of the formally employed workforce on broader community transmission. We stratified runs by asymptomatic testing frequency, vaccine coverage, vaccine effectiveness, and Rt. Simulations were informed using demographic data from Georgia, Brazil, and the Netherlands. We conducted a cost-effectiveness analysis using the results from each country and assumed a $2.50 total cost per test. Result(s): We observed a substantial decrease in the number of infections occurring in both the workplace and community when a SARS-CoV-2 AgRDTs strategy was implemented. Under all conditions, mandatory symptomatic testing and related quarantine from the workplace averted up to 72%, 79%, and 74% of community infections in Brazil, Georgia, and the Netherlands respectively. Limiting tests to symptomatic workers was always on the cost-effectiveness frontier, regardless of the vaccine coverage, efficacy, or Rt of the virus (Figure 1), at $2-$4 per workplace infection prevented. While asymptomatic testing was on the cost-effectiveness frontier, it would cost an additional $500-$6700 to prevent one additional workplace infection. The added benefit of routine asymptomatic testing was minimal until 100% of the workforce was reached. Conclusion(s): We found self-testing with AgRDTs for the formally employed workforce is both efficient at reducing workplace and community infections as well as cost-effective when targeting symptomatic individuals. Willingness to pay to avoid workplace absenteeism may differ by country, individual workplaces, and the perceived economic value of several workdays missed. If there is a higher willingness to pay, routine asymptomatic screening may be considered.

USE OF Ag-RDTs TO REDUCE SARS-CoV-2 INFECTIONS AT RELIGIOUS GATHERINGS IN LICS & MICS

Background: Throughout the COVID-19 pandemic, it was evident that many SARS-CoV-2 infections occurred at mass gathering events. In many LMICs and LICs, places of worship serve as a venue for mass gatherings, and therefore a potential source of large-scale transmission events. Mass gatherings at places of worship also serve as an opportunity to distribute Ag-RDTs to a significant proportion of the community at regular intervals, disrupting transmission within the event and potentially impacting community spread of SARS-CoV-2. Method(s): We used an agent-based community assessment model, Propelling Action for Testing and Treatment, to estimate how various strategies of asymptomatic Ag-RDT self-testing of a fixed percentage of persons attending large religious gatherings (10%, 20%, 40%, 100%), in addition to the general underlying level of ongoing symptomatic testing in the population, would impact community transmission of SARS-CoV-2 in 3 contexts (Brazil, Georgia, Zambia). These testing strategies were analyzed with bi-weekly and weekly asymptomatic self-testing in a population with varying levels of vaccine efficacy (low/high), vaccine coverage (10%, 50%, 80%), and reproductive numbers (0.9, 1.2, 1.5, and 2.0) to simulate varying stages of the COVID-19 pandemic. We then performed an economical evaluation of the results from the model to understand the impact and cost-effectiveness of each self-testing strategy at places of worship. Result(s): In each of the epidemic conditions modeled, testing of symptomatic persons at weekly and biweekly frequencies can avert 2%-16% of Brazilian community infections and 31%-45% of infections occurring in places of worship in Brazil. The same is true in Georgia (1%-6% of total infections and 28%-45% place of worship-related infections) and Zambia (2%-21% of total infections and 29%-45% of place of worship related infections) despite differences in the proportion of populations regularly attending places of worship in the 3 countries. Asymptomatic self-testing in 100% of places of worship in a country result in the greatest percent of infections averted and consistently lands on the cost-effectiveness frontier yet requires a budget 520- 1550x greater than that of symptomatic testing alone. Conclusion(s): Testing of symptomatic persons attending regular religious gatherings have a significant impact on the spread of SARS-CoV-2 in places of worship and can significantly reduce community spread in contexts where population level attendance at religious gatherings is high. Cost-effectiveness analysis from Brazil, Georgia and Zambia modelling results with infections averted within places of worship and total community infections averted assuming a total cost per self-test of $2.50 USD.

Impact and Cost-Effectiveness of Covid-19 Rapid Self-Testing Strategies in Schools

Background: Despite widespread vaccination and increasing population immunity from previous infections, community transmission of COVID-19 continues, and testing may continue to be an important component of our response particularly with the proliferation of new variants of concern. Strategic deployment of SARS-CoV-2 antigen-detection rapid diagnostic (AgRDT) self-tests to settings with increased transmission potential can reduce the viral burden within the specific settings, such as in K-12 schools, and may have spillover benefits for broader community transmission. Method(s): Using a previously developed agent-based simulation model, parameterized to three distinct country archetypes (Brazil, Georgia, Zambia), we analyzed 11 different self-testing strategies within the school-going population at three testing frequencies under 24 different epidemic conditions (Rt, vaccination coverage/effectiveness), comprising a total of 696 scenarios per country. Strategies included symptomatic testing, and in addition, asymptomatic testing at 5, 20, 40 or 100% of schools, or asymptomatic contact testing. These were all targeted to either only teachers or teachers and students. Then, with the cost to offer a COVID-19 self-test in schools at USD 2.50, we performed an economic analysis with all scenarios to identify the most costeffective strategies by country. Result(s): Routine asymptomatic testing of teachers and students at 100% of schools reduced the greatest number of infections across contexts, but at the greatest cost. However, with respect to both the reduction in infections and total cost, symptomatic testing of all teachers and students appears to be the most efficient strategy. Symptomatic testing can prevent up to 69.3%, 64.5%, and 75.5% of school infections in Brazil, Georgia, and Zambia, across all epidemic conditions. Additionally, it can prevent up to 77,200, 80,900, 107,800 symptomatic days per 100,000 teachers and students in each country, respectively, over the course of a 90-day epidemic wave. The incremental cost-effectiveness ratios for strategies that consistently appeared on the costeffectiveness frontier across countries and epidemic conditions are shown in Figure 1 for an Rt of 1.2. Conclusion(s): If financial resources are limited, symptomatic testing of teachers and students has the potential to be cost-effective while reducing a substantial number of infections and the amount of time lost from the classroom, making it a feasible strategy for implementation in a variety of settings.

Extraversion, Technology Proclivity, and Participation in Technology-Mediated, Sharing Economy Markets: An Abstract

Beyond the archetypical Airbnb and Uber brands, thousands of sharing economy platforms now operate globally. In these two-sided markets, governing algorithms bring together participants as consumers and as service providers across many business sectors. These platform's success relies on attracting a critical mass of participants on both sides of the market. Hence, alongside the emergence of the sharing economy is a growing body of research investigating the predictors of participation in the sharing economy. This study investigates whether an individual's disposition toward others and toward technology affect participation in these markets. Sharing economy markets exhibit a high level of consociality, defined as human interaction that is either physical or virtual or both (Perren & Kozinets, 2018), so we assess the role that an individual's natural propensity to engage with others—their degree of extraversion—plays on participation in the sharing economy. Sharing economy platforms leverage technological intermediation, so we examine the role that an individual's technology proclivity plays in participation in the sharing economy. We define technology proclivity as an individual's level of optimism about technology and the perceived proficiency with technology (Parasuraman, 2000;Ratchford & Barnhart, 2012). Further, we examine the relationship between extraversion and technology proclivity. Extraverts are generally described as more optimistic (Marshall et al., 1992;Williams, 1992), and optimists are more generally confident in their coping ability in novel and challenging settings (Scholz et al., 2002), including those that involve technology use. We augment Davis's (1989) technology acceptance model (TAM) to test whether and how extraversion and technology proclivity affect the likelihood of participating as a consumer or as a service provider in two sharing economy applications: ride sharing and in-home gig services. A structural equation model was tested using data from an online survey of 292 individuals. The findings reveal that extraverts have higher technology proclivity and that extraversion is directly related to the intention to use sharing economy applications. Additionally, extraversion is related indirectly to likelihood to use these technologies and to engage as a provider of such services, through technology proclivity and through the technology's perceived usefulness. Extraversion thus emerges as an important personality antecedent of participation in the sharing economy. As the world returns to normalcy, following the unprecedented global crisis and social isolation caused by the COVID-19 pandemic, continued expansion of the sharing economy offers the potential to foster a more collaborative society. A novel finding in our study is the role of extraversion as an antecedent of technology proclivity. Extraverts appear to hold more optimistic beliefs about technology and greater desire to engage with technology. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Yield of Adding chest CT to Abdominal CT to Detect COVID-19 in Patients Presenting With Acute Gastrointestinal Symptoms (SCOUT-3): Multicenter Study.

OBJECTIVE: To determine the incremental yield of standardized addition of chest CT to abdominal CT to detect COVID-19 in patients presenting with primarily acute gastrointestinal symptoms requiring abdominal imaging. Summary Background Data: Around 20% of patients with COVID-19 present with gastrointestinal symptoms. COVID-19 might be neglected in these patients, as the focus could be on finding abdominal pathology. During the COVID-19 pandemic, several centers have routinely added chest CT to abdominal CT to detect possible COVID-19 in patients presenting with gastrointestinal symptoms. However, the incremental yield of this strategy is unknown. METHODS: This multicenter study in 6 Dutch centers included consecutive adult patients presenting with acute nontraumatic gastrointestinal symptoms, who underwent standardized combined abdominal and chest CT between March 15, 2020 and April 30, 2020. All CT scans were read for signs of COVID-19 related pulmonary sequelae using the СО-RADS score. The primary outcome was the yield of high COVID-19 suspicion (СО-RADS 4-5) based on chest CT. RESULTS: A total of 392 patients were included. Radiologic suspicion for COVID-19 (СО-RADS 4-5) was present in 17 (4.3%) patients, eleven of which were diagnosed with COVID-19. Only 5 patients with СО-RADS 4-5 presented without any respiratory symptoms and were diagnosed with COVID-19. No relation with community prevalence could be detected. CONCLUSION: The yield of adding chest CT to abdominal CT to detect COVID-19 in patients presenting with acute gastrointestinal symptoms is extremely low with an additional detection rate of around 1%.

SARS-CoV-2 RAPID ANTIGEN DIAGNOSTICS: COMBINED ANALYSIS of 8 MATHEMATICAL MODELS

Background: Antigen-detecting rapid diagnostic tests (Ag-RDT) for SARS-CoV-2 are an inexpensive diagnostic tool with fast turnaround times. Ag-RDTs in combination with measures to reduce contact rates after a positive test result can reduce the spread of SARS-CoV-2. Understanding when and in what settings Ag-RDTs can best be utilized to reduce transmission is critical for resource allocation. Here, we used a suite of mathematical models to quantify the impact of SARS-CoV-2 Ag-RDT testing strategies on COVID-19 outcomes in a variety of use-cases. Methods: Our analysis synthesized the results from eight mathematical models from different modeling groups to assess the potential impact of Ag-RDT testing for SARS-CoV-2 infection across multiple use cases: (a) community testing, (b) mass gatherings, (c) K-12 schools (kindergarten to 12th grade/high school, or primary/secondary education), (d) universities, (e) border crossings, and (f) testing to exit quarantine. We calculated two outcomes relative to the status quo in each use case: (1) impact: the percent and number of infections averted and (2) efficiency: the number of tests required to avert one infection. We investigated the impact of different epidemic conditions including effective reproductive number (Rt) and COVID-19 prevalence, and the frequency of testing (for community testing, K-12 schools, and universities). Results: Different use cases require varying testing strategies to reduce infections most efficiently and effectively across a range of epidemic conditions, with some global trends. Overall, there were tradeoffs with impact and efficiency. Across use cases, increasing test frequency (and/or more testing) was associated with greater percentage of infections averted. However, lower testing frequency was generally more efficient. In the community testing and university use cases, testing was most effective and efficient when Rt and/or infection prevalence was low but for border crossings testing was most effective and efficient when Rt and/or infection prevalence were high (Table 1). Conclusion: The optimal timing of the intervention depends on whether one is trying to maximize effectiveness or efficiency, and on the use case itself. For a robust understanding of total community-level impact and cost-effectiveness, future work should aim to assess the combined impact of interventions through a single model that can consider all use-cases.