Optimizing COVID-19 testing strategies on college campuses: evaluation of the health and economic costs

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate health and economic costs, as the virus and our arsenal of medical countermeasures continue to evolve. We developed a decision-support tool to aid in the design of university-based testing strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed the CDCs original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions, university-specific parameters, and institutional goals. Author SummaryAs a part of the COVID-19 response team at a large public university in the US, we performed an analysis that considered together, the potential health and economic costs of different testing policies for the student body. University administrators had to weigh the up-front effort needed to implement wide scale testing against the potential costs of responding to high levels of disease on campus in the Fall of 2021, after vaccines were widely available but vaccination rates among college students were uncertain. The results presented here are applied to this specific instance, but the online tool provided can be tailored to university specific parameters, the epidemiological conditions, and the goals of the university. As we confront newly emerging variants of COVID-19 or novel pathogens, consideration of both the health and economic costs of proactive testing may serve as a politically tractable and cost-effective disease mitigation strategy.

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 16.9% (95% CrI: 16.1-17.8%) infection rate and 34.1% (95% CrI: 32.4-35.8%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (8.0% [95% CrI: 7.5-8.6%] vs 18.1% [95% CrI: 17.2-19.2%]), but more likely to be hospitalized (1,381 per 100,000 vs 319 per 100,000) and have their infections reported (51% [95% CrI: 48-55%] vs 33% [95% CrI: 31-35%]). Children under 18, who make up 20.3% of the local population, accounted for only 5.5% (95% CrI: 3.8-7.7%) of all infections between March 1 and May 1, 2020 compared with 20.4% (95% CrI: 17.3-23.9%) between December 1, 2020 and February 1, 2021. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 61%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. For example, the ratio in infection rates between the more and less vulnerable communities declined from 12.3 (95% CrI: 8.8-17.1) to 4.0 (95% CrI: 3.0-5.3) to 2.7 (95% CrI: 2.0-3.6), from April to August to December of 2020, respectively. Our results suggest that public health efforts to mitigate COVID-19 disparities were only partially effective and that the CDCs social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Comparative Effectiveness of Dexamethasone in Treatment of Hospitalized COVID-19 Patients during the First Year of the Pandemic: The N3C Data Repository

BackgroundDexamethasone, a widely available glucocorticoid, was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial; however, evidence is still needed to support its real-world effectiveness in patients with a wide range of comorbidities and in diverse care settings. ObjectivesTo conduct a comparative effectiveness analysis of dexamethasone use with and without remdesivir in hospitalized COVID-19 patients using electronic health record data. MethodsWe conducted a retrospective real-world effectiveness analysis using the harmonized, highly granular electronic health record data of the National COVID Cohort Collaborative (N3C) Data Enclave. Analysis was restricted to COVID-19 patients in an inpatient setting, prior to vaccine availability. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome as defined by use of ECMO or mechanical ventilation during stay. Missing data were imputed with single imputation. Matching of dexamethasone-treated patients to non-dexamethasone-treated controls was accomplished using propensity score (PS) matching, stratified by remdesivir treatment and based on demographics, baseline laboratory values, and comorbidities. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. ResultsRegression analysis revealed a statistically significant association between dexamethasone use and reduced risk of in-hospital mortality for those not receiving remdesivir (OR=0.77, 95% CI: 0.62 to 0.95, p=0.017), and a borderline statistically significant risk for those receiving remdesivir (OR=0.74, 95% CI: 0.53 to 1.02, p=0.054). Treatment also showed secondary outcome benefit. In sensitivity analyses, treatment effect size generally remained similar with some heterogeneity of benefit across strata of PS. ConclusionsWe add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

Early estimates of SARS-CoV-2 B.1.1.7 variant emergence in a university setting

Recent identification of the highly transmissible novel SARS-CoV-2 variant in the United Kingdom (B.1.1.7) has raised concerns for renewed pandemic surges worldwide 1,2. B.1.1.7 was first identified in the US on December 29, 2020 and may become dominant by March 2021 3. However, the regional prevalence of B.1.1.7 is largely unknown because of limited molecular surveillance for SARS-CoV-2 4. Quantitative PCR data from a surveillance testing program on a large university campus with roughly 30,000 students provides local situational awareness at a pivotal moment in the COVID-19 pandemic.

In the long shadow of our best intentions: model-based assessment of the consequences of school reopening during the COVID-19 pandemic

As the United States grapples with the ongoing COVID-19 pandemic, a particularly thorny set of questions surrounds the reopening of K-12 schools and universities. The benefits of in-person learning are numerous, in terms of education quality, mental health, emotional well-being, equity and access to food and shelter. Early reports suggested that children might have reduced susceptibility to COVID-19, and children have been shown to experience fewer complications than older adults. Over the past few months, our understanding of COVID-19 has been further shaped by emerging data, and it looks increasingly likely that children are as susceptible to infection as adults and have a similar viral load during infection. While the higher prevalence of asymptomatic disease among children makes symptom-based isolation strategies ineffective, asymptomatic patients do not in fact carry a reduced viral load. Using assumptions consistent with the emerging understanding of the disease, we conducted epidemiological modeling to explore the feasibility and consequences of school reopening in the face of differing rates of COVID-19 prevalence and transmission. Our findings indicate that, regardless of the initial prevalence of the disease, and in the absence of systematic surveillance testing, most schools in the United States can expect 20-60 days without a major cluster emerging. Without testing or contact tracing, the true extent of these disease clusters may not be apparent, and our research suggests that the case count will underestimate the true size of the clusters by a large margin. These disease clusters, in turn, can be expected to propagate silently through the community, with potentially hundreds to thousands of additional cases resulting from each individual school cluster. Thus, our findings suggest that the debate between the risks to student safety and benefits of in-person learning frames a false dual choice. Given the current circumstances in the United States, the most likely outcome in the late fall is that students will be deprived of the benefits of in-person learning while having incurred a significant risk to themselves and their communities.

This time is different: model-based evaluation of the implications of SARS-CoV-2 infection kinetics for disease control

As the ongoing COVID-19 pandemic passes from an acute to a chronic situation, countries and territories are grappling with the issue of how to reopen safely. The unique kinetics of infectivity of SARS-CoV-2, with its significant presymptomatic transmission, presents an unprecedented challenge to our intuitions. In this context, a generalizable quantitative understanding of the impact of SARS-CoV-2 infectivity on disease control strategies is vital. We used a previously published time-dependent model of SARS-CoV-2 infectivity (He et al., 2020) to parameterize an epidemiological model of transmission, which was then used to explore the effect of various disease control measures. Our analysis suggests that using symptom-based isolation alone as a control strategy is ineffective in limiting the spread of COVID-19, in contrast to its effectiveness in other diseases, such as SARS and influenza. Additionally, timeliness of testing and tracing strategies to reduce time to isolation, along with widespread adoption of measures to limit transmission are critical for any containment strategy. Our findings suggest that for symptom-based isolation and testing strategies to be effective, reduced transmission is required, reinforcing the importance of measures to limit transmission. From a public health strategy perspective, our findings lend support to the idea that symptomatic isolation should not form the primary basis for COVID-19 disease control.