modeling and global sensitivity analysis of strategies to mitigate covid 19 transmission CORD-Papers (Version 1)

Title: Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus
Abstract: In response to the COVID-19 pandemic many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to normal in-person operations but it is not clear if -- or for how long -- campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infected surrounding community will maintain cases on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infected individuals.
Published: 2022-04-01
DOI: 10.1101/2022.04.01.22273316
DOI_URL: http://doi.org/10.1101/2022.04.01.22273316
Author Name: Zhao L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhao_l
Author Name: Santiago F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/santiago_f
Author Name: Rutter E M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rutter_e_m
Author Name: Khatri S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/khatri_s
Author Name: Sindi S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sindi_s
sha: fd17dbd508ea67f6b94fdecc440231b27db26e61
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2022.04.01.22273316 http://medrxiv.org/cgi/content/short/2022.04.01.22273316v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: faculty quarantine/isolate COVID-19 UC Merced's prob-335 1−m First-Order class-cap COVID-19 × α faculty 6 V f = V g SARS-CoV-2 green matrix coronavirus UC V u = V d medRxiv preprint Figure 16 Fall 2020 Figure 9 https://doi.org/10.1101/2022.04.01.22273316 doi c matrix C bubble-like Nodes Edges 5(a θ i let's C(t 2 COVID-19 vaccine j + · contact matrix − β fig. 7 θ 1 medRxiv preprint Figure 17 4-5 faculty/staff friends non-influential self-isolates faculty column)-along medRxiv preprint Figure φ medRxiv preprint C Figures c ij = C(i, j left column fig. 16 to fig. 17 · C(t i+1 C. F ≥ C(t i I a f 0 self-isolates 330 fully-online S Ti ≥ S i sections https://github.com/FS-CodeBase/gsa_of_covid19_transmission_on_a_u class-size individuals nodes C (t) ≈ C(t 1 c ij = C(i, j) denotes the contacts higher-educational institutions θ 2 fig. 6 C(t node mask-use t 1 θ ∼i 2,885 Fall M NPIs Figure 7 θ k medRxiv preprint Merced medRxiv subpop-220 self-isolate week-235 c, ω Var θ 1,2,...,k rC(t super-spreaders roommate California Fall 2019 p, m ACI-1429783 DMS-1840265
Extracted Text Content in Record: First 5000 Characters:In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to "normal" in-person operations, but it is not clear if -or for how long -campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infected surrounding community will maintain cases on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infected individuals. In late 2019, a novel coronavirus, SARS-CoV-2, was identified as the cause of a cluster of pneumonia cases [1] . On March 11, 2020 the World Health Organization declared the 2019 novel coronavirus outbreak (COVID-19) a pandemic [2] . Shortly after, nearly every higher-education institute rapidly transitioned all classes to on-line instruction to "flatten the epidemic curve". As of February 8, 2022 the cumulative number of confirmed COVID-19 cases exceeds 400 million [3] . Although the advent of multiple effective vaccines offers the likelihood of a return to normal life, with the advent of booster shots and the emergence of highly-infectious variants means that the return to our pre-COVID existence is not in our immediate future [4] . In Fall 2020 in the United States, many colleges and universities attempted to re-open their class- 10 rooms and dorms to students with mixed-results. Overall, there were substantial increases in the number of new COVID-19 cases after school re-opening [5] . Moreover, even though by age college students are less likely to experience severe complications from COVID-19, the same is not true for their surrounding communities. In Winter 2020, large surges in COVID-19 cases from college students were followed by subsequent infections and deaths in the wider community [6] . In addition, many campuses delayed their in-person start in early 2022 due to emergence of the omicron variant [7] . While there is a strong desire for higher-educational institutions to maintain in-person instruction, it is clear that for the foreseeable future this will require an effective COVID-19 management policy. Nationally, educational institutions need to evaluate how to most effectively plan their 2022-2023 academic years while ensuring their activities do not result in local outbreaks [8, 9, 10] . Although 20 some campuses, such as the University of California (UC) and California State University systems, are mandating the COVID-19 vaccine for all students and employees, these mandates will not be required by all campuses or campus populations [11] . In places where the COVID-19 vaccination is not mandated, the population vaccination levels are likely to vary with local COVID-19 vaccine acceptance patterns [12] . 25 Mathematical models have a proven track record of providing novel insights into the spread and control of epidemics. Dynamic epidemic models have been used to study COVID-19 at many scales [13, 14, 15, 16, 17, 18, 19] . Given the wide-spread campus closures due to COVID-19, models have been developed to study the spread of COVID-19 on college campuses to evaluate reopening strategies [20, 21, 22] . 30 In this study, we develop a structured SEIR model of COVID-19 dynamics on a college campus and investigate the sensitivity of behavior to the vaccinated population on campus and other non-pharmaceutical interventions (NPIs) such as mask-use and social distancing. Our goal is to understand how vaccine hesitancy both within the campus population and the surrounding community will impact disease propagation and which interventions will be the most effective. More specifically, 35 we individually model the various subpopulations at the university, including on-campus undergraduates, off-campus undergraduates, graduate students, and faculty/staff. We connect our campus to the surrounding community where behavior outside the university will impact COVID-19 dynamics within the university. We perform a global sensitivity analysis of model behavior-cumulative number of cases at the end of the semester and case doubling time-and consider the first and total-order effect 40 of epidemic parameters and social contact behavior. In section 2, we first develop our structured epidemic model, then describe the model outputs we will study as well as the variance based sensitivity analysis approach we employ. In section 3, we discuss the campus data we use to parameterize our model. Althou
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