Assessment of SARS-CoV-2 Screening Strategies to Permit the Safe Reopening of College Campuses in the United States | Infectious Diseases | JAMA Network Open | JAMA Network
[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.  Cumulative Infections as a Function of Test Sensitivity and Frequency
Cumulative Infections as a Function of Test Sensitivity and Frequency

During an 80-day horizon, for the base case (Rt of 2.5, test specificity of 98%, and 10 exogenous infections per week) (A), worst case (Rt of 3.5, test specificity of 98%, and 25 exogenous infections per week) (B), and best case (Rt of 1.5, test specificity of 99.7%, and 5 exogenous infections per week) (C), these panels report cumulative infections for tests with sensitivity ranging from 70% to 99%.

Figure 2.  Projecting the Required Size of the Isolation Dormitory
Projecting the Required Size of the Isolation Dormitory

An isolation dormitory needs to be large enough to house students with false-positive results, students with symptoms, and students without symptoms who have received true-positive results. During the 80-day horizon, these panels depict the number of students in the isolation dormitory using a 70% sensitive, 98% specific test under the base case scenario (ie, Rt of 2.5). The effect of exogenous shocks (10 per week) is visible in the scalloped borders with daily screening and screening every 2 days (A, B); this is less evident with less frequent testing and symptom-based screening (C, D), in which the number of true-positive cases masks the comparatively small effect of exogenous shocks.

Table 1.  Model Input Parameters and Scenarios
Model Input Parameters and Scenarios
Table 2.  Results of the Incremental Cost-effectiveness Analysis in the Base-Case, Worst-Case, and Best-Case Scenarios
Results of the Incremental Cost-effectiveness Analysis in the Base-Case, Worst-Case, and Best-Case Scenarios
Table 3.  Per-Student Costs for Optimal Policies During an 80-Day Horizon Under Base-Case, Worst-Case, and Best-Case Scenarios
Per-Student Costs for Optimal Policies During an 80-Day Horizon Under Base-Case, Worst-Case, and Best-Case Scenarios
1.
Centers for Disease Control and Prevention. Interim guidance for administrators of US institutions of higher education: plan, prepare, and respond to coronavirus disease 2019. Updated March 18, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/community/guidance-ihe-response.html
2.
Hubler  S,. College calendars in the pandemic: no fall break and home by Thanksgiving. New York Times. Published May 19, 2020. Accessed July 7, 2020. https://www.nytimes.com/2020/05/19/us/coronavirus-college-fall-semester.html
3.
National Governors Association. Memorandum: reopening institutions of higher education. Published May 27, 2020. Accessed July 7, 2020. http://www.nga.org/wp-content/uploads/2020/05/State-Higher-Ed-reopening-final.pdf
4.
US Food and Drug Administration. Coronavirus (COVID-19) update: daily roundup May 21, 2020. Published May 21, 2020. Accessed July 7, 2020. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-daily-roundup-may-21-2020
5.
Walensky  RP, Del Rio  C.  From mitigation to containment of the COVID-19 pandemic: putting the SARS-CoV-2 genie back in the bottle.   JAMA. 2020;323(19):1889-1890. doi:10.1001/jama.2020.6572PubMedGoogle ScholarCrossref
6.
McLaughlin  K., The coronavirus could force smaller liberal arts and state colleges to close forever. Insider. Published April 26, 2020. Accessed July 7, 2020. https://www.insider.com/smaller-colleges-may-never-reopen-because-of-the-coronavirus-2020-4
7.
US Centers for Disease Control and Prevention. COVID-19 pandemic planning scenarios. Updated May 20, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
8.
He  X, Lau  EHY, Wu  P,  et al.  Temporal dynamics in viral shedding and transmissibility of COVID-19.   Nat Med. 2020;26(5):672-675. doi:10.1038/s41591-020-0869-5PubMedGoogle ScholarCrossref
9.
Davies  NG, Klepac  P, Liu  Y, Prem  K, Jit  M, Eggo  RM; CMMID COVID-19 working group.  Age-dependent effects in the transmission and control of COVID-19 epidemics.   Nat Med. 2020. doi:10.1038/s41591-020-0962-9 PubMedGoogle Scholar
10.
Lauer  SA, Grantz  KH, Bi  Q,  et al.  The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application.   Ann Intern Med. 2020;172(9):577-582. doi:10.7326/M20-0504PubMedGoogle ScholarCrossref
11.
US Centers for Disease Control and Prevention. Quarantine if you might be sick. Updated July 3, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine-isolation.html
12.
Day  M.  COVID-19: four-fifths of cases are asymptomatic, China figures indicate.   BMJ. 2020;369:m1375. doi:10.1136/bmj.m1375PubMedGoogle ScholarCrossref
13.
Yang  R, Gui  X, Xiong  Y.  Comparison of clinical characteristics of patients with asymptomatic vs symptomatic coronavirus disease 2019 in Wuhan, China.   JAMA Netw Open. 2020;3(5):e2010182. doi:10.1001/jamanetworkopen.2020.10182PubMedGoogle Scholar
14.
Ing  AJ, Cocks  C, Green  JP.  COVID-19: in the footsteps of Ernest Shackleton.   Thorax. Published online May 27, 2020;thoraxjnl-2020-215091. doi:10.1136/thoraxjnl-2020-215091PubMedGoogle Scholar
15.
Pitzer  VE, Chitwood  M, Havumaki  J,  et al. The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States. medRxiv. Preprint posted online April 24, 2020. doi:10.1101/2020.04.20.20073338
16.
Li  Q, Guan  X, Wu  P,  et al.  Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia.   N Engl J Med. 2020;382(13):1199-1207. doi:10.1056/NEJMoa2001316PubMedGoogle ScholarCrossref
17.
Lieberman  JA, Pepper  G, Naccache  SN, Huang  ML, Jerome  KR, Greninger  AL.  Comparison of commercially available and laboratory developed assays for in vitro detection of SARS-CoV-2 in clinical laboratories.   J Clin Microbiol. 2020;JCM.00821-20. doi:10.1128/JCM.00821-20 PubMedGoogle Scholar
18.
Zhen  W, Manji  R, Smith  E, Berry  GJ.  Comparison of four molecular in vitro diagnostic assays for the detection of SARS-CoV-2 in nasopharyngeal specimens.   J Clin Microbiol. 2020;JCM.00743-20. doi:10.1128/JCM.00743-20 PubMedGoogle Scholar
19.
US News and World Report. 10 colleges with the most undergraduates. Published October 1, 2019. Accessed July 7, 2020. https://www.usnews.com/education/best-colleges/the-short-list-college/articles/colleges-with-the-most-undergraduates
20.
National Center for Education Statistics. Characteristics of postsecondary students. Updated April 2020. Accessed July 7, 2020. https://nces.ed.gov/programs/coe/indicator_csb.asp
21.
US Department of Health and Human Services. United States Life Tables, 2016. Published May 7, 2019. Accessed July 7, 2020. https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_04-508.pdf
22.
Neumann  PJ, Cohen  JT, Weinstein  MC.  Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold.   N Engl J Med. 2014;371(9):796-797. doi:10.1056/NEJMp1405158PubMedGoogle ScholarCrossref
23.
US Food and Drug Administration. EUAs for coronavirus disease 2019 (COVID-19). Updated July 1, 2020. Accessed July 7, 2020. https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations#coronavirus2019
24.
Hogan  CA, Sahoo  MK, Pinsky  BA.  Sample pooling as a strategy to detect community transmission of SARS-CoV-2.   JAMA. 2020;323(19):1967-1969. doi:10.1001/jama.2020.5445PubMedGoogle ScholarCrossref
25.
Sanders  GD, Neumann  PJ, Basu  A,  et al.  Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine.   JAMA. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    1 Comment for this article
    EXPAND ALL
    Table 2- data on Worst Case Scenario
    Earl Carlos, Family Medicine | Indian Health Service
    I do not understand why they say in the article that under the worst-case scenario, daily screening strategies were the only undominated choices for WTP values, when for the 70 % test every 2 days shows that it is undominated, in fact, has an Incremental Cost-Effectiveness ratio of only $600 compared to $4400 with the daily test. Am I missing something?

    Also, I was trying to calculate these values on my own, but I was not able to by using the Cost value and dividing that with the Total of Infection Averted, but I was not able to mimic
    their values. Can someone show me this?

    Thank you.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    Public Health
    July 31, 2020

    Assessment of SARS-CoV-2 Screening Strategies to Permit the Safe Reopening of College Campuses in the United States

    Author Affiliations
    • 1Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut
    • 2Harvard Medical School, Boston, Massachusetts
    • 3Medical Practice Evaluation Center, Division of Infectious Diseases, Massachusetts General Hospital, Boston
    JAMA Netw Open. 2020;3(7):e2016818. doi:10.1001/jamanetworkopen.2020.16818
    Key Points español 中文 (chinese)

    Question  What screening and isolation programs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will keep students at US residential colleges safe and permit the reopening of campuses?

    Findings  This analytic modeling study of a hypothetical cohort of 4990 college-age students without SARS-CoV-2 infection and 10 students with undetected asymptomatic cases of SARS-CoV-2 infection suggested that frequent screening (every 2 days) of all students with a low-sensitivity, high-specificity test might be required to control outbreaks with manageable isolation dormitory utilization at a justifiable cost.

    Meaning  In this modeling study, symptom-based screening alone was not sufficient to contain an outbreak, and the safe reopening of campuses in fall 2020 may require screening every 2 days, uncompromising vigilance, and continuous attention to good prevention practices.

    Abstract

    Importance  The coronavirus disease 2019 (COVID-19) pandemic poses an existential threat to many US residential colleges; either they open their doors to students in September or they risk serious financial consequences.

    Objective  To define severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) screening performance standards that would permit the safe return of students to US residential college campuses for the fall 2020 semester.

    Design, Setting, and Participants  This analytic modeling study included a hypothetical cohort of 4990 students without SARS-CoV-2 infection and 10 with undetected, asymptomatic SARS-CoV-2 infection at the start of the semester. The decision and cost-effectiveness analyses were linked to a compartmental epidemic model to evaluate symptom-based screening and tests of varying frequency (ie, every 1, 2, 3, and 7 days), sensitivity (ie, 70%-99%), specificity (ie, 98%-99.7%), and cost (ie, $10/test-$50/test). Reproductive numbers (Rt) were 1.5, 2.5, and 3.5, defining 3 epidemic scenarios, with additional infections imported via exogenous shocks. The model assumed a symptomatic case fatality risk of 0.05% and a 30% probability that infection would eventually lead to observable COVID-19–defining symptoms in the cohort. Model projections were for an 80-day, abbreviated fall 2020 semester. This study adhered to US government guidance for parameterization data.

    Main Outcomes and Measures  Cumulative tests, infections, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact.

    Results  At the start of the semester, the hypothetical cohort of 5000 students included 4990 (99.8%) with no SARS-CoV-2 infection and 10 (0.2%) with SARS-CoV-2 infection. Assuming an Rt of 2.5 and daily screening with 70% sensitivity, a test with 98% specificity yielded 162 cumulative student infections and a mean isolation dormitory daily census of 116, with 21 students (18%) with true-positive results. Screening every 2 days resulted in 243 cumulative infections and a mean daily isolation census of 76, with 28 students (37%) with true-positive results. Screening every 7 days resulted in 1840 cumulative infections and a mean daily isolation census of 121 students, with 108 students (90%) with true-positive results. Across all scenarios, test frequency was more strongly associated with cumulative infection than test sensitivity. This model did not identify symptom-based screening alone as sufficient to contain an outbreak under any of the scenarios we considered. Cost-effectiveness analysis selected screening with a test with 70% sensitivity every 2, 1, or 7 days as the preferred strategy for an Rt of 2.5, 3.5, or 1.5, respectively, implying screening costs of $470, $910, or $120, respectively, per student per semester.

    Conclusions and Relevance  In this analytic modeling study, screening every 2 days using a rapid, inexpensive, and even poorly sensitive (>70%) test, coupled with strict behavioral interventions to keep Rt less than 2.5, is estimated to maintain a controllable number of COVID-19 infections and permit the safe return of students to campus.

    Introduction

    Universities across the United States are struggling with the question of whether and how to reopen for the fall 2020 semester.1,2 Residential colleges, with communal living arrangements, shared dining spaces, intimate classrooms, and a population of young adults anxious to socialize, pose a particular challenge. In the absence of an effective vaccine, a proven therapy, and/or sufficient herd immunity, the best hope for reopening campuses in the fall is likely to be a robust strategy of behavior-based prevention combined with regular monitoring to rapidly detect, isolate, and contain new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections when they occur.3

    Evidence on the available monitoring technologies and their performance is limited and rapidly evolving. The US Food and Drug Administration is currently evaluating more than 100 candidate tests that screen for the presence of SARS-CoV-2 infection or antibodies.4 There are many uncertainties, including the logistics of deployment; the ease and comfort of sample collection; and the accuracy, scalability, turnaround time, and cost of test kits. After a new coronavirus disease 2019 (COVID-19) case is detected, further questions emerge regarding how to conduct subsequent tracing; how to isolate detected cases in the context of congregate housing arrangements; and how to protect other at-risk populations, including faculty, staff, and members of the surrounding community.5 These uncertainties underscore the pressing need for both a generalized assessment of population-wide screening for SARS-CoV-2 and a comprehensive plan for reopening universities.

    For many US colleges, COVID-19 poses an existential threat: either they open their doors to students in September or they suffer severe financial consequences.6 University administrators struggling with this dilemma must nevertheless keep in mind that their first priority is the safety of the students in their care. We offer specific recommendations on the design of a virologic monitoring program that will keep students safe at an affordable cost. Our specific research objectives were, first, to define the minimum performance attributes of a SARS-CoV-2 monitoring program (eg, frequency, sensitivity, specificity, and cost) that could ensure that college students are kept safe; second, to understand how those minimum performance standards might change under varying assumptions about the severity of the epidemic and the success of behavioral and social distancing interventions; third, to suggest what isolation and treatment capacity would need to be in place; and fourth, to forecast what testing might cost and to help decision-makers understand that information to address the question of a screening and monitoring program’s value.

    Methods
    Study Design

    We adapted a simple compartmental epidemic model to capture the essential features of the situation facing university decision-makers that included the epidemiology of SARS-CoV-2; the natural history of COVID-19 illness; and regular mass screening to detect, isolate, and contain the presence of SARS-CoV-2 in a residential college setting (eFigure 1 in the Supplement). A spreadsheet implementation of the model permitted us to vary critical epidemic parameters and to examine how different test performance attributes (ie, frequency, sensitivity, specificity, and cost) would translate to outcomes. Model input data (Table 1)7-19 were obtained from a variety of published sources, adhering whenever possible to the data guidance for modelers recently issued by the US Centers for Disease Control and Prevention and the Office of the Assistant Secretary for Preparedness and Response. We defined 3 increasingly pessimistic epidemic scenarios and estimated both cumulative outcomes (eg, tests administered, number of true-positive and false-positive results, number of new infections, and person-days requiring isolation) and economic performance (eg, cost, incremental cost-effectiveness, and budget impact) during an abbreviated 80-day semester, running from Labor Day through Thanksgiving.2 We assumed a medium-sized college setting with a target population of 5000 students, all of them younger than 30 years and nonimmune, living in a congregate setting.19,20 We seeded this population with 10 undetected, asymptomatic cases of SARS-CoV-2 infection. A publicly accessible version of the model implementation is available online.

    This analysis adheres to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline, where applicable. Because this study used only aggregate, published data, the institutional review boards of both the Massachusetts General Hospital and the Yale School of Medicine determined that this research did not involve human participants and did not require their review or approval.

    Compartmental Model

    To the basic susceptible-exposed-infected-removed compartmental modeling framework, we added the following: the availability of regular, repeated screening with a test of imperfect sensitivity and specificity; the creation of a new compartment for uninfected persons receiving a false-positive test result; separation of the infected compartment to distinguish between asymptomatic patients with undetected infection, asymptomatic patients with detected infection (ie, true-positives), and observed symptomatic patients; and the importation of additional new infections via exogenous shocks (eg, infections transmitted to students by university employees or members of the surrounding community or during superspreader events, such as parties).

    We defined 3 epidemic severity scenarios: a base case with a reproduction number (Rt) of 2.5, test specificity of 98%, and the exogenous introduction of 10 new, undetected infections to the susceptible population each week; a worst case with an Rt of 3.5, test specificity of 98%, and 25 exogenous new infections every week; and a best case with an Rt of 1.5, test specificity of 99.7%, and 5 exogenous new infections each week.

    Isolation

    We assumed that after a lag of 8 hours, individuals receiving a positive test result (true or false) and those exhibiting COVID-19 symptoms would be moved from the general population to an isolation dormitory, where their infection would be confirmed, where they would receive supportive care, and from which no further transmissions would occur. The lag reflected both test turnaround delays and the time required to locate and isolate identified cases. Students with confirmed (ie, true-positive) results would remain in the isolation dormitory a mean of 14 days to ensure they were not infectious before proceeding to a recovered or immune state.10,11 Students with false-positive results would remain isolated for 24 hours, reflecting our assumption that a highly specific confirmatory test could overturn the original diagnosis, permitting them to return to the campus population.

    We assumed a mean time from exposure to both infectiousness and screening detectability of 3 days, a symptomatic case fatality risk of 0.05%, and a 30% probability that infection would eventually lead to observable COVID-19–defining symptoms in this young cohort.7-9,12-14

    Screening

    We sought to evaluate both existing SARS-CoV-2 detection methods and newer technologies that could plausibly be available in the near future. Accordingly, we considered a range of different test sensitivities (ie, 70%-99%), specificities (ie, 98%-99.7%),17,18 and per-test costs (ie, $10-$50). For each combination of these test characteristics, we considered both symptom-based screening and routine testing every 1, 2, 3, and 7 days. We assumed that a confirmatory test with 100% specificity could distinguish false-positive from true-positive results at a cost of $100.

    Cost-effectiveness

    Next, we estimated incremental cost-effectiveness ratios, denominated in screening costs per infection averted. This measure of return on investment in screening was compared with a crude benchmark of value estimated using the following 4 terms: (1) COVID-related mortality (0.05% in persons of college age; 0.4% overall)7; (2) survival loss of 60 years per college-age fatality; 20 years overall21; (3) societal willingness-to-pay (WTP) threshold of $100 000 per year of life gained22; and (4) Rt + 1, assuming that each infection averted prevents half the Rt secondary infections among college-age students and half among other adult members of the campus community.7,15,16 This method yielded a maximum WTP to avert 1 infection of $5500 in the best case, $8500 in the base case, and $11 600 in the worst case.

    Cost-effectiveness analysis identified a preferred screening strategy from among 13 possibilities—3 test sensitivities (70%, 80%, and 90%) and 4 frequencies (1, 2, 3, and 7 times per week) in addition to symptom-based screening—under each epidemic scenario (base, worst, and best cases) already described. We also considered the more restricted case, in which the only available test cost $25 and had a sensitivity of 80%. Finally, to help decision-makers understand the fiscal consequences of pursuing these preferred strategies, we conducted a budget impact assessment, reporting the cumulative costs for the semester on a per-student basis.

    Statistical Analysis

    The model was implemented as a spreadsheet. All analyses were conducted in Microsoft Excel. Because no statistical tests were run, no prespecified level of statistical significance was set.

    Results
    Test Frequency and Sensitivity

    At the start of the semester, the hypothetical cohort of 5000 students included 4990 (99.8%) with no SARS-CoV-2 infection and 10 (0.2%) with SARS-CoV-2 infection. During an 80-day semester in the base case (ie, Rt of 2.5 and 10 exogenous infections each week), screening every 1, 2, 3, or 7 days with a 70% sensitive, 98% specific test resulted in 162, 243, 379, and 1840 cumulative infections, respectively. Symptom-based screening yielded 4970 infections. Raising the sensitivity of the test from 70% to 90% reduced total infections (eg, from 162 to 149 for daily screening and from 1840 to 1118 for weekly screening). Figure 1 shows cumulative infections as a function of test sensitivity and test frequency for the 3 epidemic severity scenarios.

    Isolation Dormitory Occupancy

    In the base case, daily screening with a 70% sensitive, 98% specific test resulted in a mean isolation dormitory census of 116 occupants, of whom 21 (18%) had true-positive results (Figure 2A). With screening every 2 days, mean daily census was reduced to 76, as fewer tests were performed and fewer false-positive results were obtained; however, less frequent testing was also associated with greater transmission of infection and a higher mean proportion of students with true-positive results in isolation (28 students [37%]) (Figure 2B). Weekly and symptom-based screening were associated with large increases in the infected occupancy of the isolation dormitory (Figure 2C and Figure 2D). For example, screening every 7 days resulted in a mean daily isolation census of 121 students, with 108 (90%) with true-positive results. Sensitivity analysis revealed that the trends evident in Figure 2 extended beyond the 80-day planning horizon (data not shown). Varying the initial number of asymptomatic infections between 0 and 100 did not materially change our findings.

    The number of students with false-positive results and the isolation capacity required to accommodate them were reduced in the presence of a more specific test. For example, with daily screening in the base case, increasing the test specificity from 98% to 99.7% was associated with a decrease in the mean daily census of students with false-positive results in isolation from 95 to 15.

    Under worst-case assumptions (ie, Rt of 3.5 with 25 exogenous infections every week), daily screening yielded mean isolation dormitory census of 152 students, of whom 60 (39%) had true infections (eFigure 2A in the Supplement). Screening every 2 days produced similar census (151) but a higher proportion (106 [70%]) of true infections (eFigure 2B in the Supplement). With weekly screening or symptom-based screening, nearly the entire student population would be infected before the conclusion of the 80-day semester (eFigure 2C and eFigure 2D in the Supplement).

    In the best case (ie, Rt of 1.5 with 5 exogenous shocks each week and a test with 99.7% specificity), mean occupancy of the isolation dormitory was 18 (16 with infection; 2 with false-positive results) with weekly screening and 24 (all true infections) with symptom-based screening (eFigure 3 in the Supplement).

    Cost-effectiveness and Budget Impact Assessment

    In the base case, screening with a less expensive, less sensitive test dominated screening with more expensive, more accurate tests (ie, it cost less and averted greater numbers of infection) for all plausible WTP values. At the benchmark maximum WTP ($8500 per infection averted), screening every 2 days with a 70% sensitive test was the preferred strategy. For WTP exceeding $28 400 per infection averted, daily screening with this same test was optimal (Table 2). Under worst-case assumptions, daily screening strategies were the only undominated choices for WTP values exceeding $4400 per infection averted; at the benchmark maximum WTP ($11 600 per infection averted), daily screening with the least sensitive (ie, 70%) test was the preferred choice. Under best-case assumptions (with a WTP maximum of $5500 per infection averted), weekly screening with a 70% sensitive test was optimal. If the only available test cost $25 and had a sensitivity of 80%, the optimal frequency of screening would be every 7, 3, and 2 days in the best, base, and worst case scenarios, respectively (eAppendix and eTable 1 in the Supplement). If the probability of progressing from infection to symptoms rose from 30% to 65%, screening every day would be optimal in the base case scenario (eTable 2 in the Supplement). During the 80-day semester, the per-student costs of implementing the preferred screening strategy were $120, $470, and $910 in the best, base, and worst case scenarios, respectively (Table 3).

    Discussion

    The safe return of students to residential colleges demands an effective SARS-CoV-2 monitoring strategy. Results from this modelling study suggest that a highly specific screening test that can easily be administered to each student every 1 to 7 days—and that reports results quickly enough to permit newly detected cases to be isolated within hours—would be required to blunt the further transmission of infection and to control outbreaks at a justifiable cost. We identified no circumstance in this modelling study under which symptom-based screening alone would be sufficient to contain an outbreak.

    Of the many uncertain variables driving our assessment of the required frequency of screening, we highlight Rt. This uncertain measure of the transmission potential of infection will depend in part on factors that are within the control of students and university administrators. Strict adherence to handwashing, mandated indoor masking, elimination of buffet dining, limited bathroom sharing with frequent cleaning, dedensifying campuses and classrooms, and other best practices could reduce Rt to best-case levels, rendering containment possible with weekly testing. However, any relaxation of these measures in the residential college setting could easily increase Rt to worst-case levels, requiring daily screening. All members of the university community must understand the fragility of the situation and the ease with which inattention to behavior may propagate infections and precipitate the need once again to shut down campus.

    Much depends on the judicious management of positive test results, both true and false. Rapid detection, confirmation, isolation, and treatment of true-positive cases is, of course, essential. We found that frequent screening with a test of modest sensitivity and a turnaround time of 8 hours would be required for this purpose. The greater difficulty lies in managing the overwhelming number of false-positives that will inevitably result from repeated screening for low-prevalence conditions. False-positive results threaten to overwhelm isolation housing capacity, a danger whose gravity increases with screening frequency. The specificity of the initial test will matter far more than its sensitivity. Many current virologic tests report a 99.8% to 100% specificity in the context of use to date for symptomatic testing23; we examined a value of 99.7% in the best case but used a lower value of 98% in the base-case and worst-case scenarios, given that most virologic tests have yet to be used for the kind of large-scale surveillance described in this model.

    Even with a 98% specific screening test, false-positive results will present a challenge. Until a confirmatory test result is obtained, anyone receiving a positive test result will be presumed to be infectious and need to be separated from other students. Setting aside the logistic challenges and financial costs, administrators must anticipate the anxiety such separations may provoke among both students and their families. Excessive numbers of false-positive results may fuel panic and undermine confidence in the reliability of the monitoring program. It may be possible to work with test manufacturers to tune test kits under development for use in this setting, sacrificing some small measure of sensitivity in favor of higher specificity.

    Obtaining an adequate supply of testing equipment will be a challenge. On a college campus with 5000 enrollees, screening students alone every 2 days will require more than 195 000 test kits during the abbreviated semester. Our analysis assumed per-test costs (including equipment and associated personnel costs) ranging from $10 to $50. Lower-cost, self-administered testing modalities may soon be available and could make screening more affordable. Pooling could also facilitate more efficient, higher-volume screening.24 However, pooling introduces its own logistic challenges and could increase the time to definitively identifying and isolating a positive case, resulting in further transmission and provoking anxiety among the many uninfected students notified that they are among the members of an initially positive pool.

    We have tried to help decision-makers make sense of the value question by conducting a cost-effectiveness analysis and by comparing our findings with a rough estimate of the societal WTP per infection averted.25 While we have adhered to the broad outlines of recommended practice for the conduct of economic evaluations,25 we urge readers to interpret our results with caution. Most of our assumptions are conservative, ie, they understate the value of more frequent testing. For example, we ignored the clinical harms and attributable costs of COVID-19–related morbidity and treatment. We also ignored the value of infections averted beyond the student population. However, a few assumptions (eg, our failure to account for the economic and quality-of-life effects of false-positive results) may pull in the direction of less testing.

    Reopening college campuses imposes risks that extend beyond students to the faculty who teach them, the many university employees (administrative and facilities staff) who come into close daily contact with them, and the countless other members of the surrounding community with whom students come into contact. University presidents have a duty to consider the downstream effect of their reopening decisions on these constituencies. However, their first responsibility is to the safety of the students in their care. While we certainly do not intend to minimize the broader effects of the reopening decision, we have quite deliberately excluded from consideration any transmissions exported off campus.

    Limitations

    The simple model underlying this analysis has notable limitations. We assumed homogenous mixing without age-dependent transmission. We did not explicitly include the effect of screening on faculty and staff, although these and other nonstudent members of the college community include a higher proportion of older, more medically vulnerable individuals. We assumed that no students arrive on campus with immunity to COVID-19. We excluded the effects of contact tracing. Given its implementation challenges, this is a noteworthy omission. However, our results suggest that with frequent enough screening, contact tracing would not be necessary for epidemic control. While this analysis offers guidance on the frequency of screening, it does not speak to the logistic challenges of deploying testing strategies on large college campuses. Such challenges include the acquisition of supplies; the orchestration of screening at scale; the monitoring of adherence; the development of a strategy for rapid result return, contact, and isolation; and the availability and maintenance of an isolation dormitory with all single rooms and bathrooms.

    Conclusions

    We believe that there is a safe way for students to return to college in fall 2020. In this study, screening every 2 days using a rapid, inexpensive, and even poorly sensitive (>70%) test, coupled with strict interventions that keep Rt less than 2.5, was estimated to yield a modest number of containable infections and to be cost-effective. This sets a very high bar—logistically, financially, and behaviorally—that may be beyond the reach of many university administrators and the students in their care.

    Back to top
    Article Information

    Accepted for Publication: July 2, 2020.

    Published: July 31, 2020. doi:10.1001/jamanetworkopen.2020.16818

    Correction: This article was corrected on August 18, 2020, to fix a broken link in the eAppendix of the Supplement.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Paltiel AD et al. JAMA Network Open.

    Corresponding Author: A. David Paltiel, PhD, Public Health Modeling Unit, Yale School of Public Health, 60 College St, New Haven, CT 06510 (david.paltiel@yale.edu).

    Author Contributions: Drs Paltiel and Walensky had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: All authors.

    Critical revision of the manuscript for important intellectual content: Paltiel, Walensky.

    Statistical analysis: Walensky.

    Obtained funding: Paltiel, Walensky.

    Administrative, technical, or material support: All authors.

    Supervision: Paltiel.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Dr Paltiel was supported by grant R37 DA015612 from the National Institute on Drug Abuse of the National Institutes of Health. Dr Walensky was supported by the Steve and Deborah Gorlin Research Scholars Award from the Massachusetts General Hospital Executive Committee on Research.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Massachusetts General Hospital Executive Committee on Research.

    Additional Contributions: The authors thank the Massachusetts university presidents of the COVID-19 Testing Group for motivating this research. Helpful conversations with these and other college presidents shaped and refined our analysis, strategies, and assumptions.

    References
    1.
    Centers for Disease Control and Prevention. Interim guidance for administrators of US institutions of higher education: plan, prepare, and respond to coronavirus disease 2019. Updated March 18, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/community/guidance-ihe-response.html
    2.
    Hubler  S,. College calendars in the pandemic: no fall break and home by Thanksgiving. New York Times. Published May 19, 2020. Accessed July 7, 2020. https://www.nytimes.com/2020/05/19/us/coronavirus-college-fall-semester.html
    3.
    National Governors Association. Memorandum: reopening institutions of higher education. Published May 27, 2020. Accessed July 7, 2020. http://www.nga.org/wp-content/uploads/2020/05/State-Higher-Ed-reopening-final.pdf
    4.
    US Food and Drug Administration. Coronavirus (COVID-19) update: daily roundup May 21, 2020. Published May 21, 2020. Accessed July 7, 2020. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-daily-roundup-may-21-2020
    5.
    Walensky  RP, Del Rio  C.  From mitigation to containment of the COVID-19 pandemic: putting the SARS-CoV-2 genie back in the bottle.   JAMA. 2020;323(19):1889-1890. doi:10.1001/jama.2020.6572PubMedGoogle ScholarCrossref
    6.
    McLaughlin  K., The coronavirus could force smaller liberal arts and state colleges to close forever. Insider. Published April 26, 2020. Accessed July 7, 2020. https://www.insider.com/smaller-colleges-may-never-reopen-because-of-the-coronavirus-2020-4
    7.
    US Centers for Disease Control and Prevention. COVID-19 pandemic planning scenarios. Updated May 20, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
    8.
    He  X, Lau  EHY, Wu  P,  et al.  Temporal dynamics in viral shedding and transmissibility of COVID-19.   Nat Med. 2020;26(5):672-675. doi:10.1038/s41591-020-0869-5PubMedGoogle ScholarCrossref
    9.
    Davies  NG, Klepac  P, Liu  Y, Prem  K, Jit  M, Eggo  RM; CMMID COVID-19 working group.  Age-dependent effects in the transmission and control of COVID-19 epidemics.   Nat Med. 2020. doi:10.1038/s41591-020-0962-9 PubMedGoogle Scholar
    10.
    Lauer  SA, Grantz  KH, Bi  Q,  et al.  The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application.   Ann Intern Med. 2020;172(9):577-582. doi:10.7326/M20-0504PubMedGoogle ScholarCrossref
    11.
    US Centers for Disease Control and Prevention. Quarantine if you might be sick. Updated July 3, 2020. Accessed July 7, 2020. https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine-isolation.html
    12.
    Day  M.  COVID-19: four-fifths of cases are asymptomatic, China figures indicate.   BMJ. 2020;369:m1375. doi:10.1136/bmj.m1375PubMedGoogle ScholarCrossref
    13.
    Yang  R, Gui  X, Xiong  Y.  Comparison of clinical characteristics of patients with asymptomatic vs symptomatic coronavirus disease 2019 in Wuhan, China.   JAMA Netw Open. 2020;3(5):e2010182. doi:10.1001/jamanetworkopen.2020.10182PubMedGoogle Scholar
    14.
    Ing  AJ, Cocks  C, Green  JP.  COVID-19: in the footsteps of Ernest Shackleton.   Thorax. Published online May 27, 2020;thoraxjnl-2020-215091. doi:10.1136/thoraxjnl-2020-215091PubMedGoogle Scholar
    15.
    Pitzer  VE, Chitwood  M, Havumaki  J,  et al. The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States. medRxiv. Preprint posted online April 24, 2020. doi:10.1101/2020.04.20.20073338
    16.
    Li  Q, Guan  X, Wu  P,  et al.  Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia.   N Engl J Med. 2020;382(13):1199-1207. doi:10.1056/NEJMoa2001316PubMedGoogle ScholarCrossref
    17.
    Lieberman  JA, Pepper  G, Naccache  SN, Huang  ML, Jerome  KR, Greninger  AL.  Comparison of commercially available and laboratory developed assays for in vitro detection of SARS-CoV-2 in clinical laboratories.   J Clin Microbiol. 2020;JCM.00821-20. doi:10.1128/JCM.00821-20 PubMedGoogle Scholar
    18.
    Zhen  W, Manji  R, Smith  E, Berry  GJ.  Comparison of four molecular in vitro diagnostic assays for the detection of SARS-CoV-2 in nasopharyngeal specimens.   J Clin Microbiol. 2020;JCM.00743-20. doi:10.1128/JCM.00743-20 PubMedGoogle Scholar
    19.
    US News and World Report. 10 colleges with the most undergraduates. Published October 1, 2019. Accessed July 7, 2020. https://www.usnews.com/education/best-colleges/the-short-list-college/articles/colleges-with-the-most-undergraduates
    20.
    National Center for Education Statistics. Characteristics of postsecondary students. Updated April 2020. Accessed July 7, 2020. https://nces.ed.gov/programs/coe/indicator_csb.asp
    21.
    US Department of Health and Human Services. United States Life Tables, 2016. Published May 7, 2019. Accessed July 7, 2020. https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_04-508.pdf
    22.
    Neumann  PJ, Cohen  JT, Weinstein  MC.  Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold.   N Engl J Med. 2014;371(9):796-797. doi:10.1056/NEJMp1405158PubMedGoogle ScholarCrossref
    23.
    US Food and Drug Administration. EUAs for coronavirus disease 2019 (COVID-19). Updated July 1, 2020. Accessed July 7, 2020. https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations#coronavirus2019
    24.
    Hogan  CA, Sahoo  MK, Pinsky  BA.  Sample pooling as a strategy to detect community transmission of SARS-CoV-2.   JAMA. 2020;323(19):1967-1969. doi:10.1001/jama.2020.5445PubMedGoogle ScholarCrossref
    25.
    Sanders  GD, Neumann  PJ, Basu  A,  et al.  Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine.   JAMA. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195PubMedGoogle ScholarCrossref
    ×