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. 2021 May 26;16(5):e0251296. doi: 10.1371/journal.pone.0251296

Launching a saliva-based SARS-CoV-2 surveillance testing program on a university campus

Alexander J Ehrenberg 1,2, Erica A Moehle 1,2, Cara E Brook 1,2, Andrew H Doudna Cate 1, Lea B Witkowsky 1,2, Rohan Sachdeva 1,2, Ariana Hirsh 1,2, Kerrie Barry 3, Jennifer R Hamilton 1,2, Enrique Lin-Shiao 1,2, Shana McDevitt 1,2, Luis Valentin-Alvarado 1,2, Kaitlyn N Letourneau 1, Lauren Hunter 1, Amanda Keller 2, Kathleen Pestal 1, Phillip A Frankino 1, Andrew Murley 1, Divya Nandakumar 1,2, Elizabeth C Stahl 1,2, Connor A Tsuchida 1,2, Holly K Gildea 1, Andrew G Murdock 1,2, Megan L Hochstrasser 2, Elizabeth O’Brien 1,2, Alison Ciling 1,2, Alexandra Tsitsiklis 1, Kurtresha Worden 1,2, Claire Dugast-Darzacq 1, Stephanie G Hays 1, Colin C Barber 1, Riley McGarrigle 1,2, Emily K Lam 1, David C Ensminger 1, Lucie Bardet 2, Carolyn Sherry 2, Anna Harte 1,4, Guy Nicolette 1,4, Petros Giannikopoulos 2, Dirk Hockemeyer 1,2,5, Maya Petersen 1, Fyodor D Urnov 1,2, Bradley R Ringeisen 1,2, Mike Boots 1, Jennifer A Doudna 1,2,6,*; on behalf of the IGI SARS-CoV-2 Testing Consortium
Editor: Ruslan Kalendar7
PMCID: PMC8153421  PMID: 34038425

Abstract

Regular surveillance testing of asymptomatic individuals for SARS-CoV-2 has been center to SARS-CoV-2 outbreak prevention on college and university campuses. Here we describe the voluntary saliva testing program instituted at the University of California, Berkeley during an early period of the SARS-CoV-2 pandemic in 2020. The program was administered as a research study ahead of clinical implementation, enabling us to launch surveillance testing while continuing to optimize the assay. Results of both the testing protocol itself and the study participants’ experience show how the program succeeded in providing routine, robust testing capable of contributing to outbreak prevention within a campus community and offer strategies for encouraging participation and a sense of civic responsibility.

Introduction

Routine testing of individuals for the presence of viral genetic material is a central component of pandemic control when the pandemic features asymptomatic or presymptomatic infectious individuals. At the beginning of the SARS-CoV-2 outbreak in the United States, many colleges and universities sought to implement testing procedures for campus communities to detect infectious individuals not presenting COVID-19 symptoms. In March 2020, the Innovative Genomics Institute (IGI) at the University of California, Berkeley launched a high-complexity, Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory to perform clinical SARS-CoV-2 testing for both the campus and surrounding local communities [1]. Initially, the lab developed and validated an automated, mid-turbinate-oropharyngeal swab-based RT-qPCR clinical assay to detect SARS-CoV-2. This laboratory-developed test (LDT) has provided high-throughput clinical testing that supports patients utilizing the campus University Health Services (UHS) and multiple external community healthcare organizations.

In parallel with this effort, the IGI SARS-CoV-2 Testing Consortium designed and implemented an asymptomatic surveillance testing program in summer 2020 to serve the essential facilities and infrastructure staff, and researchers working on the University of California, Berkeley campus, as well as undergraduate students who would return to the campus in the Fall. The program was administered as a research study, enabling us to launch surveillance testing ahead of clinical implementation while continuing to optimize our assay. Here, we provide an account of the steps taken to develop, launch, and optimize the IGI Free Asymptomatic Saliva Testing (FAST) study. We describe our operational successes and limitations, as well as feedback from participants. Together with a companion methodology paper [2] describing the development and validation of the saliva test used for IGI FAST, we provide a roadmap to launching an asymptomatic surveillance program on a university campus.

Methods

Enrollment and participation

Recruitment, enrollment, consent, and participation for IGI FAST was approved by the Office for Protection of Human Subjects at the University of California, Berkeley under IRB #2020-05-13336. Informed consent and enrollment were completed on the IGI FAST web application instead of in writing, as a COVID-19 protocol to minimize the need for physical interaction. This web-based consenting step was approved by the IRB. Participants were recruited via email, social media posts, flyers on the University of California, Berkeley campus, word of mouth, campus website postings, and announcements connected to the required campus symptom screening tool. Participants could enroll at any point between June 19, 2020 and October 20, 2020. Enrollment criteria included being at least 18 years of age and affiliation to the University of California, Berkeley campus. Initially, participation was limited to individuals formally approved to work on campus or buildings affiliated with the University of California, Berkeley (e.g., Lawrence Berkeley National Laboratory) as essential workers, including individuals such as visiting scholars, contractors, or regulatory officials who are not formally employees of the University of California, Berkeley but regularly conducted business on campus. This requirement was relaxed in August to allow any individual affiliation to the University of California, Berkeley to enroll, including undergraduate students living off-campus and employees working remotely. Informed consent, flyers, and the study information sheet were available in English and Spanish.

A total of 4,825 participants enrolled in the study; however, 992 did not complete any appointments. A total of 12,602 tests were collected through IGI FAST. From weeks 11–13 of the study (August 31-September 20), there was a pause in study sample collection due to a supply chain shortage of liquid handler pipette tips [2]. Six hundred thirty-one samples were collected during week 11 before the appointment cancellations. Because the majority (n = 586, 93%) of the samples collected during week 11 were affected by the shortage and were unable to be tested, all requisitions from this week are excluded from the analyses we present. These exclusion criteria for our analysis leave a final total of 11,971 tests coming from a cohort of 3,653 participants (Table 1) with at least one test in weeks 1–10 and 14–19 of the study.

Table 1. Demographics of study participants.

Inactive participantsa Week 11 participantsb Final cohort
n 992 180 3,653
Age (mean, SD) 25.2, 9.9 years 22.5, 6.8 years 30.0, 12.2 years
Sex (n, %) Female: 561, 56.6% Female: 96, 53.3% Female: 1,964, 53.8%
Male: 422, 42.5% Male: 83, 46.1% Male: 1,668, 45.7%
Other: 4, 0.4% Other: 0, 0.0% Other: 10, 0.3%
Unspecified: 5, 0.5% Unspecified: 1, 0.6% Unspecified: 11, 0.3%
Number of tests (n, %) 0 appointments: 992, 100% 1 appointment: 180, 100% 1 appointment: 1,163, 31.8%
2–4 appointments: 1,505, 41.2%
5–8 appointments: 886, 24.3%
>8 appointments: 99, 2.7%

aInactive participants are those who signed up for IGI FAST but, despite not taking any tests, may have taken the exit survey.

bWeek 11 participants only participated during week 11 when samples were primarily rejected due to supply-chain issues.

Exit survey

All individuals enrolled in the study, except for 13 that withdrew from communications, were requested to take a 15-minute-long exit survey in Qualtrics. The invitation to complete the survey was included in the announcement that IGI FAST would be closing on October 20, 2020 and was available through November 2, 2020. This survey did not solicit identifiable information and was uncoupled from participants’ test results. Participants were instructed to skip any questions they did not wish to answer; however, of the total 903 (100%) (Tables 2 and 3) participants that answered at least one question, 847 (94%) completed the whole survey. Because participants could skip questions, and many questions were shown conditionally, we provide sample size on a per-question basis throughout our results here. A text version of the survey is available in S1 Appendix.

Table 2. Characteristics of exit survey respondents (n = 903).

Survey respondents
Age (n, %) 18–24 years: 236, 26%
25–34 years: 343, 38%
35–44 years: 118, 13%
45–54 years: 101, 11%
55–64 years: 72, 8%
65+ years: 32, 4%
Unspecified: 1, <1%
Gender (n, %) Woman: 534, 59.1%
Man: 341, 37.8%
Non-binary: 20, 2.2%
Other: 1, 0.1%
Unspecified: 7, 0.8%
University role (n, %) Undergraduate student: 152, 16.8%
Graduate/professional student: 273, 30.2%
Postdoctoral scholar: 95, 10.5%
Non-academic staff: 195, 21.6%
Academic faculty/staff: 186, 20.6%
NA: 2, 0.2%
Number of tests (n, %) 0 appointments: 4, 0.4%
1 appointment: 84, 9.3%
2–4 appointments: 337, 37.3%
5–8 appointments: 346, 38.3%
>8 appointments: 100, 11.1%
NA: 32, 3.5%

Table 3. Race and ethnicity of exit survey respondents (n = 903).

Race Total respondents (n, % of total) Respondents reporting multiple races (n, % of group) Hispanic or Latina/o ethnicity (n, % of group)
American Indian or Alaska Native 7, 0.7% 7, 100% 3, 42.9%
Asian 201, 22.3% 41, 20.4% 11, 5.5%
Black or African American 21, 2.3% 11, 52.4% 2, 9.5%
Native Hawaiian or Other Pacific Islander 3, 0.3% 0, 0% 0, 0%
White 634, 70.2% 56, 8.8% 42, 6.6%
Not reported 93, 10.3% -- 45, 48.4%
Total 903, 100% 59, 6.5% 95, 10.5%

Analysis of testing rates

We sought to compare the results of IGI FAST to expected frequencies based on the estimated prevalence of SARS-CoV-2 infection in the City of Berkeley. To estimate the City of Berkeley’s background SARS-CoV-2 prevalence across the duration of IGI FAST, we relied on the ’covidestim’ R package developed by Chitwood et al. [3]. We used this package to analyze daily reported COVID-19 cases, deaths, and test positivity rates across the duration of the epidemic for the City of Berkeley, CA (https://data.cityofberkeley.info); the package uses a Bayesian statistical approach and an underlying SIR mechanistic model to infer true infection rates (including asymptomatic) from those reported. The model outputs the estimated true daily infections per 100,000 persons with an upper and lower confidence interval. We then inferred prevalence from these estimated incidence rates as the daily incidence rate multiplied by the duration of infection in days. While an individual infected with SARS-CoV-2 may test positive for more than 21 days [46], we used 14 days for this estimate of prevalence, reflective of the pathogen’s infectious period [7]. This calculation yielded the estimated true prevalence of infectious cases of SARS-CoV-2 infection per day in our community. We assumed that 40% of this estimated prevalence was composed of asymptomatic or presymptomatic individuals [8, 9].

Given that symptomatic individuals were instructed to seek testing at a clinic, we then multiplied the estimated true prevalence (and corresponding confidence intervals) by 40% to yield a daily prevalence of asymptomatic/presymptomatic infections. Finally, we multiplied this asymptomatic/presymptomatic prevalence by the total number of tests collected by IGI FAST each day to determine the expected number of positives per day (S1 Table). To compute the estimated asymptomatic and presymptomatic prevalence and expected number of positives across the study duration, we summed the expected number of true infections per day and divided these infections by a 700,000-person scaling factor to compute mean incidence. We then multiplied this incidence rate by the 14-day duration of infection and the 40% asymptomatic/presymptomatic proportion to derive the mean weekly prevalence of asymptomatic/presymptomatic infection. We replicated this approach for the upper and lower confidence interval estimates of infection. To derive mean asymptomatic/presymptomatic prevalence across the study’s duration, we applied the same approach but summed estimated cases across the entire study period, divided by 100,000 persons per day multiplied by the total number of days in the study.

Results and discussion

Saliva sample choice and study design

We reasoned that voluntary asymptomatic testing would be most effective if the sample collection was simple, tolerable, inexpensive, did not require physical contact with healthcare workers, and could be tested rapidly and robustly. Saliva presented an attractive solution that could meet these criteria. While the exact sensitivity and specificity of saliva-based PCR tests for diagnosis of SARS-CoV-2 infection remain unclear, there was emerging evidence that saliva testing held a comparable performance to nasopharyngeal swabs [1015] when we began designing the diagnostic test and cognate research study at the end of spring 2020.

We selected saliva as the sampling medium due to the ability to collect specimens amid a shortage of nasopharyngeal swabs with minimal demand for trained personnel and personal protective equipment. We designed and implemented a saliva specimen collection pipeline that minimized exposure risk and maximized ease of use for participants. In parallel, we developed a high-throughput automated qPCR-based laboratory procedure for testing saliva for the presence of SARS-CoV-2 genetic material [2].

A stochastic branching process model [16] guided our asymptomatic surveillance parameters, including testing frequency and turn-around-time (TAT). Since SARS-CoV-2 tests were a limited resource in the San Francisco Bay Area, we sought to identify a surveillance regime that could effectively mitigate asymptomatic spread while retaining an adequate capacity for more vulnerable populations or medically indicated uses. Though there is an obvious association between higher testing frequency and increased outbreak prevention, our model suggested that when viral prevalence is <1% in the participating population, testing participants on alternating weeks with a TAT of no more than five days could limit campus outbreaks. These parameters afforded the IGI Diagnostics Lab the ability to continue allocating sufficient testing resources to highly vulnerable populations in the local community while suppressing asymptomatic transmission within the campus community.

To test the operational feasibility of this model, optimize our assay, and bring surveillance testing to our campus, we established a research study, known as IGI FAST. Of those enrolled, 47% were recruited through a direct email invitation, 42% through word-of-mouth via a friend/coworker, and 35% through invitations included in the clearance messages sent to campus personnel who completed a daily online symptom screener required for on-site work (Fig 1). Interested individuals were directed to a custom-built online web application, where they provided informed consent as well as demographic and contact information to facilitate communication and follow-up should they test positive or inconclusive. Participants receiving a positive or inconclusive (only one of three SARS-CoV-2 genes detected) result through this research study were called by a clinician and directed to take a follow-up swab-based confirmatory clinical test as soon as possible.

Fig 1. IGI FAST recruitment.

Fig 1

(A) Results from an exit survey question asking respondents to report through which methods they heard about IGI FAST. Respondents (n = 865) could select multiple answers. Abbreviations: IGI, Innovative Genomics Institute; UHS, University Health Services; UCB, University of California, Berkeley. (B) Of the 4,825 participants who enrolled, 992 did not give any samples, leaving 3,833 participants who gave 12,602 samples. A supply-chain shortage in week 11 caused a large number of samples (94% of samples collected in week 11) to go untested. For this reason, all 631 samples collected at the beginning of week 11 are excluded from the analysis of IGI FAST. One hundred eighty individuals only provided a sample during week 11, so they are excluded from the analysis of IGI FAST, leaving a final cohort of 3,653 participants that gave 11,971 samples in the studied weeks of IGI FAST.

IGI FAST study operating procedure

The IGI FAST study operated for a total of 16 weeks between June 23, 2020 and October 29, 2020 and processed 11,971 tests (S2 Table) from a total cohort of 3,653 active participants (Fig 1). For the first eight weeks of the study, one outdoor site operated at a campus location near one cluster of research buildings that were the first to resume operations during the pandemic. For the last eight active weeks of the study, an additional site provided expanded access to the on-campus population. Both sites featured the same workflow (Fig 2) which we detail in S1 Methods.

Fig 2. IGI FAST testing site workflow.

Fig 2

Our two testing sites followed the same workflow, which can be easily scaled to increase the throughput. Participants first checked in at station one, where they were screened for COVID-19 symptoms, potential contacts, and recent food or water consumption. At station 2, an appointment QR code was scanned, and a collection site worker created a test requisition. Participants then went to individually-staffed spitting stations, where they were supervised while giving their sample. At station 4, the tube was re-scanned, and participants confirmed their name and date of birth. At this point, the sample was dropped into a bag and left with the site worker. Parts of figure made using biorender.com.

A custom-built IGI FAST web app allowed participants to schedule their saliva collection appointments. Participants received a QR code that was presented and scanned at the testing site, allowing us to rapidly locate participant records in the app. Participants received an SMS text and email reminder 30 minutes before their appointment. Participants were prompted to schedule appointments at a cadence of every two weeks via email. The app uses TLS based encryption, an industry standard for web security, with a Postgres database on top of an AES-256 encrypted filesystem on the backend. The JavaScript files for the enrollment and scheduling app are available at https://github.com/innovativegenomics/igi-testing-kiosk.

In the text and email reminders, participants were instructed not to eat, drink (including water), smoke, chew gum, or brush their teeth for at least 30 minutes before their appointment slot, consistent with instructions from the saliva collection kit manufacturer (DNA Genotek), and were asked to confirm this upon arrival. Participants were screened verbally for COVID-19 symptoms or known exposure. Any individuals reporting symptoms or exposure were instructed to go to UHS, where they were clinically tested using a respiratory swab outside of the IGI FAST study’s administration. Those who passed the symptom and exposure screener then scanned their appointment QR code at the check-in desk. Here, they were asked to confirm their name and date of birth. They then received a barcoded saliva collection kit (OMNIgene OM-505).

To provide a saliva test specimen, the participant entered the saliva collection area, where they were directed to an available kiosk staffed by "saliva coach" staff or volunteers who advised on the process from behind a plastic divider. Coaching focused on safety and how to generate an optimal specimen. Kiosk workers observed the saliva sample to check for visible food particles, excessive color (thought to be due to substances such as coffee), excessive mucus, or excessive or insufficient volume (S2 Appendix).

At the intake station (Station 4, Fig 2), participants scanned their tube into a Salesforce-platform laboratory information management system built by Thirdwave Analytics [1] and left the tube with the station worker. From here, the samples were brought back to the IGI Diagnostics Lab, where they underwent the procedures outlined in [2].

We produced an instructional video to prepare participants in the test’s ongoing clinical deployment (https://youtu.be/2IFB2Q-zV8g). (Note: This video records an IGI FAST test site worker going through the workflow for demonstrative purposes–not for participation in the study).

To protect participants’ and workers’ safety, we collaborated with University of California, Berkeley’s Environmental Health & Safety department and UHS to implement several safeguards. First, we chose to establish the two collection sites outdoors in covered, well-ventilated areas. Ground markings were put in place to encourage social distancing as participants waited in line, and the spitting stations were each separated by >12 feet to prevent the spread of SARS-CoV-2 for the brief period while participants were not wearing masks. Additionally, participants were scheduled to provide specimens at intervals that would keep the density of people low. We established physical barriers between participants and collection site workers in areas where participants were without masks, and a minimum of six feet of distance elsewhere protected test site workers. Through this attention to safety for both the collection site workers and the participants, we were able to minimize worker demand of personal protective equipment (PPE) while maximizing participant throughput.

On several occasions during the Fall, the Northern California Wildfires created hazardous environmental conditions. If the air quality index, as determined by airnow.gov, was greater than 150 at 8 am on any day, the testing sites were closed, and participants were notified via email and SMS text that no specimens would be collected that day.

Although the IGI FAST test results were not considered protected health information, we nonetheless operated in alignment with HIPAA standards. Results were sent from the berkeley.edu-supported study email address using end-to-end encryption from Virtru. Since these results were from a test yet to be validated under the CLIA framework, we included specific language in the results describing this limitation (S3 Appendix). Participants with positive or inconclusive results were additionally contacted via phone by one of the study clinicians within minutes to several hours following the lab reporting the result to the clinicians. It became critical to quickly recommend isolation to these individuals, follow up with confirmatory testing external to the study, and manage any symptoms that may have emerged.

IGI FAST ended once the assay completed clinical validation as an LDT, obviating the need to administer it as a research study. UHS assumed responsibility for asymptomatic surveillance sample collection to consolidate surveillance sampling resources and personnel on the University of California, Berkeley campus. While we continue to run the saliva test in our laboratory as a clinically orderable test, it is currently deployed in a limited capacity, where its ease of use in a take-home setting better suits low compliance or off-campus student populations. Instead of saliva, a self-administered nasal swab tested on the same PCR-based platform as the IGI FAST test is used for widespread regular asymptomatic surveillance testing at the University of California, Berkeley because these swab-based samples were more easily pooled than the saliva samples.

Testing and participant characteristics

IGI FAST collected a total of 11,971 tests from its final cohort (S3 Table). We identified five positive samples from five different individuals through IGI FAST, within the expected range of 3.6–33.5 positives predicted by the estimated asymptomatic and presymptomatic prevalence of SARS-CoV-2 infection in the City of Berkeley, CA during IGI FAST’s duration (Table 4). While our tested positivity rate falls within the expected range, it is on the lower end. This outcome could be attributable to several factors, including test sensitivity; however, we speculate that it likely reflects a difference in demographics and associated exposure risk between our campus’ study cohort and the broader population of the City of Berkeley. Overall, IGI FAST featured a high number (n = 761, 6.4%) of “specimen insufficient” results, making it a difficult test to further scale through pooled testing [2].

Table 4. Results and estimated community asymptomatic and presymptomatic prevalence by week.

IGI FAST data Estimated community prevalence
Collection Weeka Positive Negative Inconclusive Insufficient Total Asymptomatic/presymptomatic prevalenceb (Percent, 95% CIc)
6/22/2020–6/28/2020 0 367 1 126 494 0.1% (0.04%, 0.35%)
6/29/2020–7/5/2020 0 254 0 115 369 0.14% (0.06%, 0.45%)
7/6/2020–7/12/2020 0 417 2 135 554 0.13% (0.05%, 0.4%)
7/13/2020–7/19/2020 0 480 0 25 505 0.09% (0.04%, 0.3%)
7/20/2020–7/26/2020 0 531 1 42 574 0.07% (0.03%, 0.22%)
7/27/2020–8/2/2020 1 530 0 21 552 0.07% (0.03%, 0.2%)
8/3/2020–8/9/2020 1 599 2 10 612 0.07% (0.03%, 0.22%)
8/10/2020–8/16/2020 0 565 2 42 609 0.08% (0.03%, 0.24%)
8/17/2020–8/23/2020 0 588 4 11 603 0.08% (0.03%, 0.26%)
8/24/2020–8/30/2020 1 629 3 69 701 0.08% (0.04%, 0.26%)
9/21/2020–9/27/2020 2 1650 1 36 1688 0.03% (0.01%, 0.1%)
9/28/2020–10/4/2020 0 462 1 17 480 0.02% (0.01%, 0.08%)
10/5/2020–10/11/2020 0 1497 1 65 1563 0.02% (0.01%, 0.08%)
10/12/2020–10/18/2020 0 1068 2 28 1098 0.03% (0.01%, 0.13%)
10/19/2020–10/25/2020 0 1077 0 4 1081 0.06% (0.02%, 0.21%)
10/26/2020–11/1/2020 0 470 1 15 486 0.1% (0.04%, 0.35%)
Total 5 11184 21 761 11971 0.08% (0.03%, 0.28%)d

aWeeks 11–13 are excluded here due to the supply chain shortage that shut down testing.

bWeekly asymptomatic and presymptomatic prevalence was computed by summing the estimated daily new infections per 100,000 output from the ’covidestim’ package in R across the seven days of each week, dividing by a 700,000 person scaling factor to produce the weekly incidence rate, then multiplying by a 14-day duration of infectiousness to derive prevalence (see Methods). Finally, estimates were scaled by 40% to yield the asymptomatic and presymptomatic prevalence per week for the duration of the study period.

cChitwood et al. fixed the lower bound of their 95% confidence intervals at the reported case positive rate (lagged by delay time to presentation of symptoms). As a result, confidence intervals are not always evenly distributed (upper bounds exceed lower bounds).

dThe asymptomatic and presymptomatic prevalence for the entire study duration (6/23/2020–8/30/2020 and 9/21/2020–10/29/2020) was computed similarly as the weekly asymptomatic and presymptomatic prevalence, but summed the estimated daily new infections across the entire study duration, then followed the same steps.

Early on, the program confronted insufficient specimen rates up to 39% on a given collection day. In the first three weeks of the program, 376 of the 1,417 (27%) total samples received a specimen insufficient result. While aspects of the lab protocol were optimized to decrease the rate of specimen insufficient results, 158 (42%) of the 376 insufficient specimens were due to protocol failure in a step before PCR, such as an inability to pipette the sample.

Several established challenges with saliva collection may have contributed to this rejection rate. Person-to-person variation in salivary flow, pH, and oral hygiene can contribute to notable heterogeneity in specimen quality [17]. Additionally, several commonly used drugs for hypertension, depression, allergies, pain, inflammation, and recreational use are negatively associated with saliva production, which may lead to repeated sample rejection or difficult sampling for select individuals [18]. However, we suspected that factors like postnasal drip, improper sample volumes, or contaminants from food or drink significantly contributed to our high sample rejection rate. Accordingly, we established a communication line between the lab and collection site workers to improve coaching and establish more careful on-site sample screening. Together with optimization of the assay in the lab, the more stringent quality control steps occurring at the collection site decreased the specimen insufficient rate from 27% in the first three weeks to 1.8% in the last three weeks (Fig 3).

Fig 3. Sample characteristics throughout the study duration.

Fig 3

(A) The proportion of samples (n = 11,971) that returned with a specimen insufficient value decreased throughout the study. The teal-colored band represents samples that were rejected in a step before PCR analysis, such as an inability to pipette. (B) The mean (standard deviation) turn-around time decreased from 72.6 (68.8) hours in the first three weeks (n = 1,417) of the study to 45.9 (19.6) hours in the last three weeks (n = 2,665) of the study. Here, the blue bar depicts a 72-hour turn-around. The vertical red bar depicts the supply-chain shortage period that led to a temporary shutdown in testing. (C) A series of laboratory techniques (see Hamilton et al.) were deployed throughout the study to optimize the protocols, improving the sample rejection rate and turn-around time.

These changes also improved the TAT for results. Throughout the study, the mean (standard deviation) TAT decreased from 72.6 (68.8) hours in the first three weeks (n = 1,417) of the study to 45.9 (19.6) hours in the last three weeks (n = 2,665) of the study (Fig 3).

The characteristics of our study population are described in Table 1. Overall, 4,825 participants enrolled, with 992 individuals who never gave a specimen and 180 who completed an appointment only during week 11’s supply chain shortage, which are not included in the final cohort of 3,653 (Fig 1).

IGI FAST participant experience assessed by survey

All individuals enrolled in IGI FAST by October 20, 2020 were invited via email to take a 15-minute anonymous exit survey in Qualtrics (S1 Appendix), including those who never made an appointment. A total of 903 (19%) (Table 2) participants completed at least one question on the survey (S4 Table). Race and ethnicity data (Table 3) were collected on the exit survey, but not during original testing enrollment. This survey collected data on demography, housing characteristics, behaviors associated with SARS-CoV-2 exposure risk, experiences with IGI FAST, and preferences related to surveillance testing. It also solicited general impressions in the form of the question, "If you had the opportunity to give advice to another university setting up SARS-CoV-2 surveillance testing, what are some suggestions you would give?" Selected excerpts from responses to this question are provided in the next section.

Key results from the IGI FAST participant survey

1. Convenience is critical

"I could walk out of my building, do my test, and be back in [the] lab in a matter of 10 minutes."–Graduate/professional student

As a voluntary testing program, we focused heavily on making IGI FAST easy to participate in and widely appealing. Ease of enrollment, quick appointment scheduling, and testing sites’ location were paramount. Appointment scheduling could be completed in well under one minute on the study’s web application. The workflow (Fig 2) was kept as streamlined as possible on-site. While saliva collection duration was variable, we endeavored to supply sufficient kiosks and coaches to keep the site’s total time to under ten minutes. Our locations were chosen, in part, based on their proximity to buildings with high concentrations of on-site personnel– 82% of survey respondents that were approved to work on campus worked within ten minutes of an IGI FAST testing site. Survey responses emphasized the importance of this choice and minimizing travel time, where 58% of exit survey respondents indicated that they would no longer participate if they had to travel longer than ten minutes from their workspace to get to a testing site (Fig 4).

Fig 4. Convenience of IGI FAST testing sites.

Fig 4

IGI FAST aimed to select testing sites on the University of California, Berkeley campus (A) near the buildings with the highest occupancy levels during the pandemic. Here, we represent occupancy based on answers given during enrollment. Residence halls and off-campus buildings are not depicted; neither are participants who did not report any building. Participants who provided a primary campus building were presumed to be approved to work in those buildings during the pandemic in this illustration. Participants who reported multiple campus buildings are counted multiple times in this illustration. (B) Overall, 82% of survey respondents who were approved to work on campus indicated that they worked within 10 minutes of the nearest testing site, excluding those who did not answer this question on the survey (n = 650). (C) When asked how long they would be willing to travel for regular surveillance testing, most survey respondents reported that they would be willing to travel six or more minutes for testing; however, many participants would be lost to travel times greater than ten minutes (n = 635).

Specimen collection procedures in IGI FAST were well-tolerated and viewed as easy by participants (Fig 5). When asked to compare experiences with SARS-CoV-2 tests received elsewhere, 79% (409 out of 515) of those that received a respiratory swab reported that the IGI FAST saliva test was more tolerable (Fig 5). While there is a strong preference for saliva over clinician-administered respiratory swabs, our exit survey indicates that a switch to self-administered nasal swabs would not significantly affect participation rates. As one graduate student participant commented in the exit survey, "…the self-administered nasal swabs are a pain and make me not want to go as much, though [I will] probably put up with [them anyway]." Only 2% of survey respondents indicated that they would not participate in a surveillance program using a self-administered nasal swab, while 86% indicated that they would participate in such a program, and an additional 12% were unsure (Fig 5). Additionally, IGI FAST received high scores for ease and safety (Fig 5). Taking our data together, convenience is the most critical determinant of participation, suggesting that, if made as convenient as FAST, a voluntary nasal swab-based asymptomatic surveillance program is likely to see high participation rates.

Fig 5. Participant reviews of IGI FAST.

Fig 5

IGI FAST was well-reviewed by participants. (A) To exit survey respondents who reported having done a respiratory swab-based SARS-CoV-2 test outside of IGI FAST (n = 515), IGI FAST was superior regarding tolerability and convenience. (B) When exit survey participants were asked how well various words described their experiences with the IGI FAST test, testing sites, or personnel, IGI FAST received favorable responses. (C) When asked whether participants would continue participating in a testing program using a nasal swab instead of IGI FAST, most respondents indicated a willingness to continue participation (n = 839). Of note, this question specified the hypothetical continued program would use nasal swabs instead of nasopharyngeal swabs, clarifying the difference between the two.

2. Saliva presents challenges but should not be ignored as an option

"I really like[d] that IGI FAST used saliva collection instead of a nasal swab and found it much more comfortable than the [self-administered nasal swab] test."–Undergraduate student

Throughout the FAST study, we identified several challenges with saliva-based testing for SARS-CoV-2. First, it requires advanced planning by the participants to ensure that they do not eat, drink, smoke, chew gum, or brush their teeth within 30 minutes before their appointment times. While there is limited research on the subject [19], this guidance is typical of saliva kit manufacturers’ instructions to prevent interference with the abundance of buccal cell DNA or populations of oral viruses and microbes. Second, we suspect that either natural variability [17], drug- or disease-induced xerostomia [18], or participant hydration status affected the viscosity of samples collected, leading to high variability in sample quality. We found saliva to be a challenging matrix for nucleic acid extraction in general and observed that viscous samples were often more likely to fail, leading to a high specimen insufficient rate. These technical issues are further discussed in our companion manuscript [2], and made the possibility of pooling to increase surveillance capacity impractical, a strategy we were able to test by virtue of operating under an IRB rather than as a clinical requisition. Third, 20% of survey respondents indicated that producing a sufficient amount of saliva was either "somewhat difficult" or "extremely difficult." However, 33% of respondents with two or more visits indicated that they developed a saliva sampling strategy, indicating a possible learning curve. The most common strategies included building a "reserve" of saliva while waiting in line by not swallowing (41% of those with strategies), hydrating well before the test (36%), and thinking about food during the test (12%).

Despite these challenges, saliva as a sample type retains certain advantages over respiratory swabs. As spitting is a non-technical procedure, saliva samples are particularly amenable to at-home self-collection. Collecting saliva also presents an alternative for populations who are particularly intolerant to respiratory swabs and circumvents shortages in respiratory swab supply chains. To further explore its potential to better reach low-participation populations, the IGI has partnered with UHS to continue a smaller-scale take-home pilot using the now clinically-validated saliva-based assay developed during IGI FAST. To go along with this pilot, IGI has created resources such as video (https://youtu.be/FRuAcLJm5zk) instructional materials for use at home. Given that saliva tests appear to have comparable performance to nasopharyngeal swab tests for SARS-CoV-2 [20, 21], expanding deployment beyond asymptomatic surveillance into some clinical populations may be warranted. In fact, there is emerging evidence in pre-print literature that SARS-CoV-2 titer in saliva may be a helpful biomarker for risk stratification and prognosis [22].

3. Establish regular testing as a social norm

"Create a climate where an employee sees it as something to do for their coworkers[,] not as a threat to their continued employment…"–Non-academic research staff member

Given that participation was entirely voluntary and no compensation was given, our successful enrollment of 3,653 active participants indicates a demand for, rather than resistance to surveillance testing in general. Indeed, a study at the University of California, Berkeley immediately preceding IGI FAST [23] supports our findings that creating attitudes of civic engagement and camaraderie surrounding surveillance testing may render mandates unnecessary. 648 (17.7%) of the 3,653 active participants participated fully (i.e., took every appointment opportunity offered to them) (Fig 6). The reasons underlying the lack of full participation by 82.3% of participants were not systematically assessed; however, anecdotal accounts by participants cited periods of not conducting on campus work, travel, or self-direction towards clinical testing due to symptoms or exposure as some reasons for missing appointments. Overall, the reasons for joining IGI FAST indicated in the exit survey were typically elective rather than imposed (Fig 6). Of the respondents who were approved to work on the University of California, Berkeley campus at any time during IGI FAST, 90% reported that they either preferred or did not feel safe working unless they and their coworkers got regular viral testing.

Fig 6. IGI FAST enrollment.

Fig 6

(A) There were two waves of enrollment into the study–at the beginning of the study and again in the Fall when undergraduate students returned to the city for the beginning of the academic year. There was a wide variety of participation levels in the program. Here, participation rate represents the number of samples an individual gave divided by the number of possible appointments that individual could have made based on when they joined. The graphs here depict only active participants in the final cohort (n = 3,653). (B) The leading reasons for joining IGI FAST were elective (i.e., participants wanted regular viral testing, wanted to contribute to research, or had encouragement from friends/family/coworkers). 79% of the 865 respondents who completed this question reported more than one reason. (C) The majority (77%) of the total (n = 865) participants reported solely elective reasons for joining IGI FAST. Few (1%) reported imposed or "one-off" reasons (i.e., worry about COVID-19 exposure or symptoms, pressure from others, requirement or pressure from employer/boss/supervisor, the fulfillment of travel requirements). One individual was excluded from this analysis because their response was unclassifiable. (D) Examples of stickers produced to facilitate the normalization of surveillance testing on campus through cultivating a sense of pride.

We endeavored to cultivate a positive culture surrounding surveillance testing through messaging strategies and testing site atmosphere. Training efforts with IGI FAST personnel focused on creating a welcoming, respectful environment for participants (Fig 5). A specific effort to promote positive messaging surrounding surveillance testing featured a series of stickers produced by IGI (Fig 6). While subtle, the stickers provided a mode of civic signaling akin to the "I voted" stickers that increase voter turnout in elections by evoking conformity bias [24]. One undergraduate survey respondent reported that the "…stickers started a movement of sorts within my house and I felt a sense of pride getting tested." Some participants even reported an effort to collect all sticker designs across their visits. Acknowledging that our survey lacks assessment of individuals not choosing to participate in surveillance testing, we conclude that with appropriate messaging and atmosphere, surveillance testing for SARS-CoV-2 can quickly become a social norm and even be perceived as a civic duty.

4. Establish a robust communication system

"Work with a [communications professional to] write, edit and design your communication materials. We are [drowning] in information, which is changing daily and it does not help if we have to wade through […] communication."–Healthcare worker

IGI FAST aimed to communicate regularly and clearly with study participants. By creating a website solely dedicated to testing through IGI FAST, participants had an easily accessible, coherent resource to understand how to participate in the program, access appointments, and view announcements with program updates. Collaboration with UHS enabled individuals who had provided positive or inconclusive samples to promptly receive clinical testing and care. Furthermore, an automated notification system enabled participants to receive prompts to schedule appointments and reminders about upcoming appointments, including reminders to avoid eating or drinking before their appointment via email and SMS text.

Future improvements to the testing program could include faster dissemination of results, both to participants and the campus health service, which could be achieved by enhancing the study web app to include a results portal. Other enhancements to the web app could include centralizing resources for contact tracing, symptom screening, and clear, digestible guidance on best practices for social behavior, mask-wearing, and use of asymptomatic testing results. Universities should aim to centralize all resources related to pandemic response and testing and be transparent regarding policies and procedures to minimize stigmatization surrounding positive results.

5. Risk-stratification is helpful but should be comprehensive

In a limited-resource setting, campuses benefit from developing a risk-stratification paradigm to determine cost-effective test allocation models. Such an allocation model was not deployed in IGI FAST, as the program was initially designed solely for a population of on-campus workers. Many campus surveillance efforts have focused on undergraduate students living in administratively defined congregate housing (i.e., dormitory residents, those living in Greek or co-operative housing) and actively training or competing student-athletes. Exposure and transmission risk-stratification can be further optimized by considering additional factors, including ’off-campus’ housing density and outside work activities. For example, 14% of undergraduate survey respondents (n = 115) live in private households or apartments with greater than five other people, and 5% of graduate student survey respondents who are approved to work on campus live with someone who works or volunteers in healthcare settings or nursing homes. Notably, some participants were concerned about self-isolating should they test positive. Only 50% of graduate/professional students (n = 250) reported that they could "probably" or "definitely" effectively self-isolate in response to a positive test result. A brief questionnaire to collect baseline data such as housing characteristics, risks associated with housemate occupation, childcare/school attendance, ability to self-isolate, or housemate susceptibility to adverse health outcomes could be deployed to allocate resources to the campus population more effectively. Continued brief questionnaires may further refine the model of resource allocation to assess factors such as social behavior, travel, changes in occupational risk, or even emergence of symptoms [23]. Doing so, with guidance from data-driven epidemiological models such as Brook et al [16], would layer adaptive risk-stratification onto a baseline risk-stratification paradigm to ensure optimal resource allocation longitudinally.

Limitations

While we aimed to establish a robust testing program and research study, it had several limitations. To facilitate the approval and establishment of the protocol, we did not solicit any health records from participants. This includes the results of any confirmatory testing or data regarding the subsequent emergence of symptoms. While this did not hinder our goal of identifying and directing asymptomatic individuals infected with SARS-CoV-2 to clinical services, this choice limited the breadth of analyses we could conduct here. Furthermore, our exit survey attempts to study the factors influencing participation in SARS-CoV-2 surveillance testing. Seeing that the exit survey was only sent to participants, we do not capture the attitudes of individuals choosing not to participate in surveillance testing in the survey responses. As such, conclusions drawn from the survey responses should be interpreted with caution.

Conclusion

During the SARS-CoV-2 pandemic, higher education institutions have been faced with difficult choices in their effort to retain some activity while safeguarding their campus and local communities. While many challenges are universal, Universities present some unique risks of spread both within their campus and outside to local communities stemming from student travel between campus and their home locales, engagement in high-contact student athletics, high-density living spaces [25, 26], and a culture of highly social behavior. For these reasons, universities that choose to maintain some in-person activities have an obligation to minimize the spread of SARS-CoV-2 through a robust disease prevention ecosystem. Here we describe a blueprint for a low-barrier, safe, effective, easy, and adaptable program for campus SARS-CoV-2 surveillance using saliva specimens capable of minimizing the number of outbreaks [16] and effectively creating a culture of safety.

Universities have an opportunity to lead with institutional responses to crises. Campus responses to SARS-CoV-2 have had the opportunity to serve as a paragon of effective utilization of academics with expertise in the life sciences, public health, technology, public policy, social psychology, education, and communications. We hope that the outcomes and lessons from IGI FAST will help other institutions implement successful strategies or improve their responses in the face of SARS-CoV-2 and assist with future pandemic preparedness.

Supporting information

S1 Appendix. IGI FAST exit survey.

(PDF)

S2 Appendix. Visual inspection guide for on-site saliva sample screening.

See https://innovativegenomics.org/wp-content/uploads/2021/01/visual-inspection-guide.pdf for the full-quality version.

(DOCX)

S3 Appendix. Generic versions of the results emails used for IGI FAST.

(DOCX)

S4 Appendix. IGI SARS-CoV-2 Testing Consortium membership.

(DOCX)

S1 Methods. Detailed materials and methods for saliva collection sites.

(DOCX)

S1 Table. Daily IGI FAST results and estimates of asymptomatic and presymptomatic SARS-CoV-2 infection.

Estimates derived from the ‘covidestim’ R package calculated from prevalence in the City of Berkeley.

(DOCX)

S2 Table. IGI FAST samples and associated results.

(CSV)

S3 Table. IGI FAST participants.

(CSV)

S4 Table. IGI FAST survey responses.

(CSV)

Acknowledgments

We thank all the IGI FAST participants for their involvement in the study. Additionally, we are grateful to Robert Tijan (UCB/HHMI); Chancellor Carol Christ, Executive Vice Chancellor Paul Alivisatos, Vice Chancellor for Administration Marc Fisher, Assistant Vice Chancellor for Human Resources Eugene Whitlock, and Vice Chancellor for Research Randy Katz (UCB); Melody Heller and Lisa Polley (UCB University Health Services); Chips Hoai (UCB Environmental Health Services); Chief Margot Bennett (UCB Police) and UCPD Community Service Officers; Emily Harden-Antonio and Adrienne Tanner (UCB Office for Protection of Human Subjects); and Patricia Zialcita (City of Berkeley Public Health) for their contributions, support, and guidance. We extend our gratitude to Benton Cheung from IGI for videography assistance. We thank all the testing staff of the IGI FAST surveillance effort for their support of the initiative.

Membership in the IGI SARS-CoV-2 Testing Consortium is provided in S4 Appendix. The IGI SARS-CoV-2 Testing Consortium is led by Jennifer A. Doudna (doudna@berkeley.edu).

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

We thank the Packard Foundation, the Curci Foundation, the Julia Burke Foundation, and other anonymous donors for their support of IGI FAST. We additionally thank the University of California, Berkeley for their financial support of IGI FAST. A.J.E. is a graduate research fellow at the Greater Good Science Center at the University of California, Berkeley (https://greatergood.berkeley.edu/). J.R.H. is a Fellow of The Jane Coffin Childs Memorial Fund for Medical Research (https://www.jccfund.org/). A.M. is a fellow of the Damon Runyon Cancer Research Foundation (https://www.damonrunyon.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Ruslan Kalendar

29 Mar 2021

PONE-D-21-05507

Launching a saliva-based SARS-CoV-2 surveillance testing program on a university campus

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IRB #2020-05-13336

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'We thank the Packard Foundation, the Curci Foundation, the Julia Burke Foundation, and other anonymous donors for their support of IGI FAST. We additionally thank the University of California, Berkeley for their financial support of IGI FAST.

A.J.E. is a graduate research fellow at the Greater Good Science Center at the University of California, Berkeley (https://greatergood.berkeley.edu/). J.R.H. is a Fellow

of The Jane Coffin Childs Memorial Fund for Medical Research

(https://www.jccfund.org/). A.M. is a fellow of the Damon Runyon Cancer Research Foundation (https://www.damonrunyon.org/).

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**********

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5. Review Comments to the Author

Reviewer #1: 

This is a well-written manuscript describing an important, comprehensive approach to wide-scale testing, communication and social norming campaign on a university campus to test for and manage COVID-19. The authors developed a sophisticated, encrypted smartphone app for registering and scheduling sample collection, and models to aid in identifying populations to test. They also developed an important advertising and compliance campaign that greatly facilitates recruiting, compliance and participation in saliva screening on campus. They followed these activities with surveys that aided assessment of their testing program.

However, the authors cite four references from medRXiv, including their own, non-peer-reviewed manuscript detailing the testing protocol. This is completely inappropriate. The authors should either 1)include the testing protocol and data in this current manuscript for peer review or 2)submit the testing protocol manuscript for peer review then, upon acceptance, reference that study in this manuscript. They should also remove references to the other medRxiv manuscripts, as they are not peer reviewed and therefore cannot be used to support this manuscript currently under peer review. I am sorry I cannot be more supportive of this manuscript in its current form, and look forward to a revision that addresses these important concerns.

Reviewer #2: 

Throughout your paper, you state that a testing program such as yours can reduce or prevent outbreaks but you do not provide any evidence that your program did, in fact, reduce or prevent outbreaks. You either need to include evidence from your study that you did reduce/prevent outbreaks, or you should reframe your claims.

Consider rewording the first sentence of the introduction. The mere presence of viral genetic material does not necessarily correlate with infectivity and can lead to loss of productivity and impact to physical and mental health through unnecessary quarantine periods in non-infectious individuals.

Page 5 – what do you mean by “minimize worker use of PPE”?

Page 6 – how quickly were positives/inconclusives contacted by phone? What if they could not be reached?

Page 6 – why is it now deployed in a limited capacity? If it can, as you claim, prevent outbreaks, why has your own university not rolled it out on a larger scale.

What percentage of eligible on-campus staff decided to participate in the study?

What percentage of participants actually completed a test every second week from the time of their enrollment throughout the duration of the study (ie, full compliance)?

Please elaborate on your supposition that the exposure risk of your study cohort is lower than the general population of Berkley.

Only 5 positives were seen during your study. Were these positives asymptomatic or presymptomatic? Were their follow-up confirmatory tests positive or negative? At the time they were contacted by study personnel, had they already taken another COVID test? Did any outbreaks or clusters result from these individuals? How many infected individuals were traced back to these 5 positives, and was that number higher or lower than the Ro in the general Berkley community?

How many outbreaks occurred during this time period from people not undergoing voluntary testing?

You state that the responses to your survey indicate that a sense of civic duty and a cultural norm of frequent testing was present among your participants – is it not likely that those that chose to participate in this voluntary testing program were already individuals with a strong sense of civic duty/desire to undergo regular testing? How do you account for this selection bias in your conclusions?

Did you use risk stratification to guide your testing model? IE, you suggest continued brief surveys to determine risk profiles of the campus population, but did you ever trial or implement this in your study? If not, how do you propose another university apply the results of such continued brief surveys?

**********

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Reviewer #1: Yes: Mark Zabel

Reviewer #2: No

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PLoS One. 2021 May 26;16(5):e0251296. doi: 10.1371/journal.pone.0251296.r002

Author response to Decision Letter 0


19 Apr 2021

Dear Dr. Kalendar,

Thank you very much for the opportunity to resubmit our manuscript, “Launching a saliva-based SARS-CoV-2 surveillance testing program on a university campus” to PLOS ONE. We deeply appreciate the thoughtful responses and recommendations, which have together generated significant improvement to our manuscript. Below, we outline our responses to each point laid out in the reviews we received:

Editor’s comments

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have revised our manuscript’s styling to adhere to the referenced style requirements, including moving the methods section to immediately following the introduction.

2. Please ensure you have discussed any potential limitations of your study in the Discussion.

We now have a section entitled “Limitations” at the end of the “Results and Discussion” section.

3. In the Methods, please clarify that participants provided digital consent. Please also state in the Methods:

- Why written consent could not be obtained

- Whether the Institutional Review Board (IRB) approved use of digital consent

If additional written consent was collected as part of the test sample collection, please include this information.

For more information, please see our guidelines for human subjects research: https://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research

This information has been added to the methods to include:

“Recruitment, enrollment, consent, and participation for IGI FAST was approved by the Office for Protection of Human Subjects at the University of California, Berkeley under IRB #2020-05-13336. Informed consent and enrollment were completed on the IGI FAST web application instead of in writing, as a COVID-19 protocol to minimize the need for physical interaction. This web-based consenting step was approved by the IRB.”

No additional written consent was collected.

4. Thank you for including your ethics statement:

"Office of the Protection of Human Subjects, University of California, Berkeley IRB #2020-05-13336. Written (online) consent was obtained from all participants".

a. Please amend your current ethics statement to confirm that your named institutional review board or ethics committee specifically approved this study.

b. Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

We have updated the Methods section as described in #3 above. This statement is also now listed in the “Ethics Statement” field of the submission form.

5. Thank you for stating in your financial disclosure:

'We thank the Packard Foundation, the Curci Foundation, the Julia Burke Foundation, and other anonymous donors for their support of IGI FAST. We additionally thank the University of California, Berkeley for their financial support of IGI FAST.

A.J.E. is a graduate research fellow at the Greater Good Science Center at the University of California, Berkeley (https://greatergood.berkeley.edu/). J.R.H. is a Fellow of The Jane Coffin Childs Memorial Fund for Medical Research (https://www.jccfund.org/). A.M. is a fellow of the Damon Runyon Cancer Research Foundation (https://www.damonrunyon.org/).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.'

PLOS ONE requires you to include in your manuscript further information about the funder so that any relevant competing interests can be assessed. Please respond to the following questions:

a. Please state whether any of the research costs or authors' salaries were funded, in whole or in part, by a tobacco company (our policy on tobacco funding is at http://journals.plos.org/plosone/s/disclosure-of-funding-sources)

b. Please state whether the donor has any competing interests in relation to this work (see http://journals.plos.org/plosone/s/competing-interests) .

c. Please state whether the identity of the donor might be considered relevant to editors or reviewers’ assessment of the validity of the work.

d. If the donors have no perceived or actual competing interests, please state: “The authors are not aware of any competing interests”.

This information should be included in your cover letter. We will amend your financial disclosure and competing interests on your behalf.

We amend our financial disclosure to the below. This has also been removed from the Acknowledgements section, per the request in #1:

We thank the Packard Foundation, the Curci Foundation, the Julia Burke Foundation, and other anonymous donors for their support of IGI FAST. We additionally thank the University of California, Berkeley for their financial support of IGI FAST. A.J.E. is a graduate research fellow at the Greater Good Science Center at the University of California, Berkeley (https://greatergood.berkeley.edu/). J.R.H. is a Fellow

of The Jane Coffin Childs Memorial Fund for Medical Research

(https://www.jccfund.org/). A.M. is a fellow of the Damon Runyon Cancer Research Foundation (https://www.damonrunyon.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. None of the research costs or authors’ salaries were funded in whole or in part by a tobacco company. The authors are not aware of any competing interests.

6. We note that you have a patent relating to material pertinent to this article.

a. Please provide an amended statement of Competing Interests to declare this patent (with details including name and number), along with any other relevant declarations relating to employment, consultancy, patents, products in development or modified products etc. Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” If there are restrictions on sharing of data and/or materials, please state these.

Please note that we cannot proceed with consideration of your article until this information has been declared.

b. This information should be included in your cover letter; we will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

The patents disclosed in the first submission of this article are unrelated to material pertinent to this article. As such, we are removing any mention of such patents from our submission.

7. One of the noted authors is a consortium; IGI SARS-CoV-2 Testing consortium.

In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript.

Please also indicate clearly a lead author for this group along with a contact email address.

This has been updated

8. Please include a separate caption for each figure in your manuscript.

This has been provided, as outlined in the documents referenced in #1.

9. Please include your tables 1-4 as part of your main manuscript and remove the individual files.

Please note that supplementary tables should remain as separate "supporting information" files.

This has been provided

10. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

These have been added.

11. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

This has been updated accordingly.

Reviewer #1

12. …the authors cite four references from medRXiv, including their own, non-peer-reviewed manuscript detailing the testing protocol. This is completely inappropriate. The authors should either 1)include the testing protocol and data in this current manuscript for peer review or 2)submit the testing protocol manuscript for peer review then, upon acceptance, reference that study in this manuscript. They should also remove references to the other medRxiv manuscripts, as they are not peer reviewed and therefore cannot be used to support this manuscript currently under peer review.

We thank this reviewer for their thoughtful feedback on our manuscript.

In citing the preprint manuscripts, we are following guidelines provided by PLOS “...preprints are a citable part of the scientific record. All preprints are given a permanent DOI, which should be used when adding to the reference list of a manuscript.” This is consistent with other prominent journals. These guidelines indicate that the journal does consider citation of medRxiv articles to be appropriate. With this, we aim to be transparent in our application of the methodologies or conclusions provided by the pre-print.

Of the four medRxiv citations, at least two are presently under consideration at peer-reviewed journals, including one at PLOS ONE. We hope that they will be accepted prior to acceptance of this manuscript, allowing for citation of peer-reviewed versions. Providing further details regarding these manuscripts would be beyond the scope of the present manuscript.

For the citation (Silva et al) where we provide reference to the manuscript’s conclusions, we have added “...in pre-print literature…” to the sentence where we make the citation.

The other citations describe the application of methods or technology as described by the other authors.

Reviewer #2

13. Throughout your paper, you state that a testing program such as yours can reduce or prevent outbreaks but you do not provide any evidence that your program did, in fact, reduce or prevent outbreaks. You either need to include evidence from your study that you did reduce/prevent outbreaks, or you should reframe your claims.

We are grateful to this reviewer for their thoughtful feedback and recommendations for areas of improvement.

Insomuch as this paper lacks a proper comparison of outbreaks in a similar population without regular surveillance testing, we agree with this reviewer’s assessment. As such, we have reframed our claims as follows:

“To test this model, optimize our assay…” is now “To test the operational feasibility of this model, optimize our assay…”

“Here we describe a blueprint for a low-barrier, safe, effective, easy, and adaptable program for campus SARS-CoV-2 surveillance using saliva specimens capable of preventing outbreaks and effectively creating a culture of safety.” now reads ”Here we describe a blueprint for a low-barrier, safe, effective, easy, and adaptable program for campus SARS-CoV-2 surveillance using saliva specimens capable of minimizing the number of outbreaks [9] and effectively creating a culture of safety.” Additionally, it now cites [9], justifying the statement that our paradigm is capable of minimizing the number of outbreaks.

While now reframed, we maintain our reference to outbreak prevention given that we demonstrated our capacity to detect asymptomatic or presymptomatic individuals and asymptomatic or presymptomatic individuals infected with SARS-CoV-2 are capable of transmitting SARS-CoV-2 infection to others

14. Consider rewording the first sentence of the introduction. The mere presence of viral genetic material does not necessarily correlate with infectivity and can lead to loss of productivity and impact to physical and mental health through unnecessary quarantine periods in non-infectious individuals.

We appreciate the point the reviewer is raising. Accordingly, we have amended the first sentence of the introduction to clarify: “...when the pandemic features asymptomatic or presymptomatic infectious individuals.”

15. Page 5 – what do you mean by “minimize worker use of PPE”?

This is meant to illuminate a benefit of self-administered sample collection - the lack of demand of PPE needed for safe collection. Traditional nasopharyngeal swabs require items such as face shields and gowns, which are not required for self-collected saliva.

We have reworded this to “...minimize worker demand of…”

16. Page 6 – how quickly were positives/inconclusives contacted by phone? What if they could not be reached?

We have amended “Participants with positive or inconclusive results were additionally contacted via phone by one of the study clinicians” to include “...within minutes to several hours following the lab reporting the result to the clinicians.” As with normal clinical resulting for COVID-19, the clinicians continued to attempt to contact the participants by phone until the call was received. Additionally, these participants received the result via encrypted email.

17. Page 6 – why is it now deployed in a limited capacity? If it can, as you claim, prevent outbreaks, why has your own university not rolled it out on a larger scale.

As stated in the following paragraph under “Testing and participant characteristics”, “Overall, IGI FAST featured a high number (n=761, 6.4%) of “specimen insufficient” results, making it a difficult test to further scale through pooled testing [2].”

To clarify this point in the location this reviewer refers to, “Instead of saliva, a self-administered nasal swab tested on the same PCR-based platform as the IGI FAST test is used for widespread regular asymptomatic surveillance testing at the University of California, Berkeley because these swab-based samples were more easily pooled than the saliva samples.”

18. What percentage of eligible on-campus staff decided to participate in the study?

Unfortunately, the University’s estimate of the total on-campus population for the duration of this study is highly imprecise. As such, we are unable to estimate the percentage of on-campus staff that were actively participating.

19. What percentage of participants actually completed a test every second week from the time of their enrollment throughout the duration of the study (ie, full compliance)?

We have added:

“648 (17.7%) of the 3,653 active participants participated fully (i.e., took every appointment opportunity offered to them) (Fig 6). The reasons underlying the lack of full participation by 82.3% of participants were not systematically assessed; however, anecdotal accounts by participants cited periods of not conducting on campus work, travel, or self-direction towards clinical testing due to symptoms or exposure as some reasons for missing appointments.”

20. Please elaborate on your supposition that the exposure risk of your study cohort is lower than the general population of Berkley.

We have elaborated the referenced sentence:

“...however, we speculate that it likely reflects a difference in exposure risk between our study cohort and the broader population of the City of Berkeley.“

to

“...however, we speculate that it likely reflects a difference in demographics and associated exposure risk between our campus’ study cohort and the broader population of the City of Berkeley.“

21. Only 5 positives were seen during your study. Were these positives asymptomatic or presymptomatic? Were their follow-up confirmatory tests positive or negative? At the time they were contacted by study personnel, had they already taken another COVID test? Did any outbreaks or clusters result from these individuals? How many infected individuals were traced back to these 5 positives, and was that number higher or lower than the Ro in the general Berkley community?

How many outbreaks occurred during this time period from people not undergoing voluntary testing?

Our IRB did not provide for follow up with participants to gather clinical or contact tracing information. Instead, the purpose of IGI FAST was to establish a program by which asymptomatic or presymptomatic individuals could be routed to clinical services. As such, we are unable to address any of these questions.

We have brought attention to this limitation in a new section of the Results and Discussion section “Limitations”.

22. You state that the responses to your survey indicate that a sense of civic duty and a cultural norm of frequent testing was present among your participants – is it not likely that those that chose to participate in this voluntary testing program were already individuals with a strong sense of civic duty/desire to undergo regular testing? How do you account for this selection bias in your conclusions?

We have included mention of this in the Limitations section. Additionally, we have changed:

“We conclude that with appropriate messaging and atmosphere, surveillance testing for SARS-CoV-2 can quickly become a social norm and even be perceived as a civic duty.”

to

“Acknowledging that our survey lacks assessment of individuals not choosing to participate in surveillance testing, we conclude that with appropriate messaging and atmosphere, surveillance testing for SARS-CoV-2 can quickly become a social norm and even be perceived as a civic duty.”

23. Did you use risk stratification to guide your testing model? IE, you suggest continued brief surveys to determine risk profiles of the campus population, but did you ever trial or implement this in your study? If not, how do you propose another university apply the results of such continued brief surveys?

Towards the beginning of the section discussing risk-stratification, we clarify:

“Such an allocation model was not deployed in IGI FAST, as the program was initially designed solely for a population of on-campus workers.”

We have also amended:

“Doing so would layer adaptive risk-stratification onto a baseline risk-stratification paradigm to ensure adequate resource allocation longitudinally.”

To

“Doing so, with the assistance of data-driven epidemiological models such as Brook et al [9], would layer adaptive risk-stratification onto a baseline risk-stratification paradigm to ensure optimal resource allocation longitudinally.”

Sincerely,

Jennifer Doudna, PhD

Attachment

Submitted filename: PONE-D-21-05507 Response to Reviewers.docx

Decision Letter 1

Ruslan Kalendar

26 Apr 2021

Launching a saliva-based SARS-CoV-2 surveillance testing program on a university campus

PONE-D-21-05507R1

Dear Dr. Doudna,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ruslan Kalendar, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: All of my comments have now been addressed, thank you for your responses and revisions to this manuscript.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mark D Zabel

Reviewer #2: No

Acceptance letter

Ruslan Kalendar

17 May 2021

PONE-D-21-05507R1

Launching a saliva-based SARS-CoV-2 surveillance testing program on a university campus

Dear Dr. Doudna:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Ruslan Kalendar

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix. IGI FAST exit survey.

    (PDF)

    S2 Appendix. Visual inspection guide for on-site saliva sample screening.

    See https://innovativegenomics.org/wp-content/uploads/2021/01/visual-inspection-guide.pdf for the full-quality version.

    (DOCX)

    S3 Appendix. Generic versions of the results emails used for IGI FAST.

    (DOCX)

    S4 Appendix. IGI SARS-CoV-2 Testing Consortium membership.

    (DOCX)

    S1 Methods. Detailed materials and methods for saliva collection sites.

    (DOCX)

    S1 Table. Daily IGI FAST results and estimates of asymptomatic and presymptomatic SARS-CoV-2 infection.

    Estimates derived from the ‘covidestim’ R package calculated from prevalence in the City of Berkeley.

    (DOCX)

    S2 Table. IGI FAST samples and associated results.

    (CSV)

    S3 Table. IGI FAST participants.

    (CSV)

    S4 Table. IGI FAST survey responses.

    (CSV)

    Attachment

    Submitted filename: PONE-D-21-05507 Response to Reviewers.docx

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.


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