Investigating transmission of SARS-CoV-2 using novel face mask sampling: a protocol for an observational prospective study of index cases and their contacts in a congregate setting.

INTRODUCTION: This study aims to measure how transmission of SARS-CoV-2 occurs in communities and to identify conditions that lend to increased transmission focusing on congregate situations. We will measure SARS-CoV-2 in exhaled breath of asymptomatic and symptomatic persons using face mask sampling-a non-invasive method for SARS-CoV-2 detection in exhaled air. We aim to detect transmission clusters and identify risk factors for SARS-CoV-2 transmission in presymptomatic, asymptomatic and symptomatic individuals. METHODS AND ANALYSIS: In this observational prospective study with daily follow-up, index cases and their respective contacts are identified at each participating institution. Contact definitions are based on Centers for Disease Control and Prevention and local health department guidelines. Participants will wear masks with polyvinyl alcohol test strips adhered to the inside for 2 hours daily. The strips are applied to all masks used over at least 7 days. In addition, self-administered nasal swabs and (optional) finger prick blood samples are performed by participants. Samples are tested by standard PCR protocols and by novel antigen tests. ETHICS AND DISSEMINATION: This study was approved by the Colorado Multiple Institutional Review Board and the WHO Ethics Review Committee. From the data generated, we will analyse transmission clusters and risk factors for transmission of SARS-CoV-2 in congregate settings. The kinetics of asymptomatic transmission and the evaluation of non-invasive tools for detection of transmissibility are of crucial importance for the development of more targeted control interventions-and ultimately to assist with keeping congregate settings open that are essential for our social fabric. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (#NCT05145803).

Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

The case for altruism in institutional diagnostic testing.

Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members' close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network.