Dataset Information

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis.

ABSTRACT:

Purpose

The optimal approach to identify SARS-CoV-2 infection among college students returning to campus is unknown. Recommendations vary from no testing to two tests per student. This research determined the strategy that optimizes the number of true positives and negatives detected and reverse transcription polymerase chain reaction (RT-PCR) tests needed.

Methods

A decision tree analysis evaluated five strategies: (1) classifying students with symptoms as having COVID-19, (2) RT-PCR testing for symptomatic students, (3) RT-PCR testing for all students, (4) RT-PCR testing for all students and retesting symptomatic students with a negative first test, and (5) RT-PCR testing for all students and retesting all students with a negative first test. The number of true positives, true negatives, RT-PCR tests, and RT-PCR tests per true positive (TTP) was calculated.

Results

Strategy 5 detected the most true positives but also required the most tests. The percentage of correctly identified infections was 40.6%, 29.0%, 53.7%, 72.5%, and 86.9% for Strategies 1-5, respectively. All RT-PCR strategies detected more true negatives than the symptom-only strategy. Analysis of TTP demonstrated that the repeat RT-PCR strategies weakly dominated the single RT-PCR strategy and that the thresholds for more intensive RT-PCR testing decreased as the prevalence of infection increased.

Conclusion

Based on TTP, the single RT-PCR strategy is never preferred. If the cost of RT-PCR testing is of concern, a staged approach involving initial testing of all returning students followed by a repeat testing decision based on the measured prevalence of infection might be considered.

SUBMITTER: Van Pelt A

PROVIDER: S-EPMC7606071 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis.

Van Pelt Amelia A Glick Henry A HA Yang Wei W Rubin David D Feldman Michael M Kimmel Stephen E SE

The Journal of adolescent health : official publication of the Society for Adolescent Medicine 20201103 1

<h4>Purpose</h4>The optimal approach to identify SARS-CoV-2 infection among college students returning to campus is unknown. Recommendations vary from no testing to two tests per student. This research determined the strategy that optimizes the number of true positives and negatives detected and reverse transcription polymerase chain reaction (RT-PCR) tests needed.<h4>Methods</h4>A decision tree analysis evaluated five strategies: (1) classifying students with symptoms as having COVID-19, (2) RT ...[more]

PMID: 33153883

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Project description:The 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. To define SARS-CoV-2 screening performance standards that would permit the safe return of students to campus for the Fall 2020 semester. Decision and cost-effectiveness analysis linked to a compartmental epidemic model to evaluate campus screening using tests of varying frequency (daily-weekly), sensitivity (70%-99%), specificity (98%-99.7%), and cost ($10-$50/test). Reproductive numbers Rt = {1.5, 2.5, 3.5} defined three epidemic scenarios, with additional infections imported via exogenous shocks. We generally adhered to US government guidance for parameterization data. A hypothetical cohort of 5000 college-age, uninfected students. Main Outcome(s) and Measure(s): Cumulative tests, infections, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact. All measured over an 80-day, abbreviated semester. With Rt = 2.5, daily screening with a 70% sensitive, 98% specific test produces 85 cumulative student infections and isolation dormitory daily census averaging 108 (88% false positives). Screening every 2 (7) days nets 135 (3662) cumulative infections and daily isolation census 66 (252) with 73% (4%) false positives. Across all scenarios, test frequency exerts more influence on outcomes than test sensitivity. Cost-effectiveness analysis selects screening every {2, 1, 7} days with a 70% sensitive test as the preferred strategy for Rt = {2.5, 3.5, 1.5}, implying a screening cost of {$470, $920, $120} per student per semester. Conclusions & Relevance: Rapid, inexpensive and frequently conducted screening (even if only 70% sensitive) would be cost-effective and produce a modest number of COVID-19 infections. While the optimal screening frequency hinges on the success of behavioral interventions to reduce the base severity of transmission (Rt), this could permit the safe return of student to campus.

Project description:ObjectivesUniversities are exploring strategies to mitigate the spread of COVID-19 prior to reopening their campuses. National guidelines do not currently recommend testing students prior to campus arrival. However, the impact of presemester testing has not been studied.DesignDynamic SARS-CoV-2 transmission models are used to explore the effects of three presemester testing interventions.InterventionsTesting of students 0, 1 and 2 times prior to campus arrival.Primary outcomesNumber of active infections and time until isolation bed capacity is reached.SettingWe set on-campus and off-campus populations to 7500 and 17 500 students, respectively. We assumed 2% prevalence of active cases at the semester start, and that one-third of infected students will be detected and isolated throughout the semester. Isolation bed capacity was set at 500. We varied disease transmission rates (R0=1.5, 2, 3, 4) to represent the effectiveness of mitigation strategies throughout the semester.ResultsWithout presemester screening, peak number of active infections ranged from 4114 under effective mitigation strategies (R0=1.5) to 10 481 under ineffective mitigation strategies (R0=4), and exhausted isolation bed capacity within 10 (R 0 =4) to 25 days (R0=1.5). Mandating at least one test prior to campus arrival delayed the timing and reduced the size of the peak, while delaying the time until isolation bed capacity was reached. Testing twice in conjunction with effective mitigation strategies (R0=1.5) was the only scenario that did not exhaust isolation bed capacity during the semester.ConclusionsPresemester screening is necessary to avert early and large surges of active COVID-19 infections. Therefore, we recommend testing within 1 week prior to and on campus return. While this strategy is sufficient for delaying the timing of the peak outbreak, presemester testing would need to be implemented in conjunction with effective mitigation strategies to significantly reduce outbreak size and preserve isolation bed capacity.

Project description:BackgroundEpidemics of COVID-19 in student populations at universities were a key concern for the 2020-2021 school year. The University of California (UC) System developed a set of recommendations to reduce campus infection rates. SARS-CoV-2 test results are summarized for the ten UC campuses during the Fall 2020 term.MethodsUC mitigation efforts included protocols for the arrival of students living on-campus students, non-pharmaceutical interventions, daily symptom monitoring, symptomatic testing, asymptomatic surveillance testing, isolation and quarantine protocols, student ambassador programs for health education, campus health and safety pledges, and lowered density of on-campus student housing. We used data from UC campuses, the UC Health-California Department of Public Health Data Modeling Consortium, and the U.S. Census to estimate the proportion of each campus' student populations that tested positive for SARS-CoV-2 and compared it to the fraction individuals aged 20-29 years who tested positive in their respective counties.ResultsSARS-CoV-2 cases in campus populations were generally low in September and October 2020, but increased in November and especially December, and were highest in early to mid-January 2021, mirroring case trajectories in their respective counties. Many students were infected during the Thanksgiving and winter holiday recesses and were detected as cases upon returning to campus. The proportion of students who tested positive for SARS-CoV-2 during Fall 2020 ranged from 1.2% to 5.2% for students living on campus and was similar to students living off campus. For most UC campuses the proportion of students testing positive was lower than that for the 20-29-year-old population in which campuses were located.ConclusionsThe layered mitigation approach used on UC campuses, informed by public health science and augmented perhaps by a more compliant population, likely minimized campus transmission and outbreaks and limited transmission to surrounding communities. University policies that include these mitigation efforts in Fall 2020 along with SARS-CoV-2 vaccination, may alleviate some local concerns about college students returning to communities and facilitate resumption of normal campus operations and in-person instruction.

Project description:BackgroundColleges in the United States are determining how to operate safely amid the coronavirus disease 2019 (COVID-19) pandemic.ObjectiveTo examine the clinical outcomes, cost, and cost-effectiveness of COVID-19 mitigation strategies on college campuses.DesignThe Clinical and Economic Analysis of COVID-19 interventions (CEACOV) model, a dynamic microsimulation model, was used to examine alternative mitigation strategies. The CEACOV model tracks infections accrued by students and faculty, accounting for community transmissions.Data sourcesData from published literature were used to obtain parameters related to COVID-19 and contact-hours.Target populationUndergraduate students and faculty at U.S. colleges.Time horizonOne semester (105 days).PerspectiveModified societal.InterventionCOVID-19 mitigation strategies, including social distancing, masks, and routine laboratory screening.Outcome measuresInfections among students and faculty per 5000 students and per 1000 faculty, isolation days, tests, costs, cost per infection prevented, and cost per quality-adjusted life-year (QALY).Results of base-case analysisAmong students, mitigation strategies reduced COVID-19 cases from 3746 with no mitigation to 493 with extensive social distancing and masks, and further to 151 when laboratory testing was added among asymptomatic persons every 3 days. Among faculty, these values were 164, 28, and 25 cases, respectively. Costs ranged from about $0.4 million for minimal social distancing to about $0.9 million to $2.1 million for strategies involving laboratory testing ($10 per test), depending on testing frequency. Extensive social distancing with masks cost $170 per infection prevented ($49 200 per QALY) compared with masks alone. Adding routine laboratory testing increased cost per infection prevented to between $2010 and $17 210 (cost per QALY gained, $811 400 to $2 804 600).Results of sensitivity analysisResults were most sensitive to test costs.LimitationData are from multiple sources.ConclusionExtensive social distancing with a mandatory mask-wearing policy can prevent most COVID-19 cases on college campuses and is very cost-effective. Routine laboratory testing would prevent 96% of infections and require low-cost tests to be economically attractive.Primary funding sourceNational Institutes of Health.

Project description:ImportanceThe 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.ObjectiveTo 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 participantsThis 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 measuresCumulative tests, infections, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact.ResultsAt 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 relevanceIn 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.

Dataset Information

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis.

Purpose

Methods

Results

Conclusion

Publications

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis.

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