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Modeling for COVID-19 college reopening decisions: Cornell, a case study.

ABSTRACT: We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

SUBMITTER: Frazier PI

PROVIDER: S-EPMC8764692 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Modeling for COVID-19 college reopening decisions: Cornell, a case study.

Frazier Peter I PI Cashore J Massey JM Duan Ning N Henderson Shane G SG Janmohamed Alyf A Liu Brian B Shmoys David B DB Wan Jiayue J Zhang Yujia Y

Proceedings of the National Academy of Sciences of the United States of America 20220101 2

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the g ...[more]

PMID: 34969678

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Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21st, 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21st until May 4th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic.MethodsThis study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model.ResultsThe proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities.ConclusionsOur model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

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:To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

Project description:IntroductionCOVID-19, a continuously emerging human-to-human infectious disease, has exerted a significant impact on the mental health of college students. However, little is known regarding the variations in the mental health issues experienced by college students during the peak versus reopening stages of the COVID-19 epidemic in China.MethodsTo assess these issues, an online longitudinal survey was conducted via a WeChat applet. Undergraduates (n = 300) were recruited from 26 universities throughout Jinan in February 2020 (T1 - the epidemic peak stage) and in January 2021 (T2 - the society reopening stage). Their mental status was determined using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 item, and the Insomnia Severity Index.ResultsOf the original 300 college students recruited for this survey, 294 responses at T1 and 285 at T2 were analyzed. Compared with responses obtained at T1, college students at T2 showed a greater prevalence of depression (65.3 vs. 51.0%; p = 0.001) and anxiety (47.7 vs. 38.1%, p = 0.019), and experienced more severe depression (p < 0.001) and anxiety (p < 0.001). Both males (p = 0.03) and females (p < 0.01) showed higher levels of depression at T2 versus T1, while no differences were obtained with regard to anxiety and insomnia. At T1, Grade 4 students showed greater levels of depression (p = 0.005) and anxiety (p = 0.008) than that of Grade 1 students. While at T2, only greater levels of depression (p = 0.004) were present when compared with that of Grade 1 students. Additionally, Grade 4 college students demonstrated a greater prevalence of depression at T2 versus T1 (p = 0.03), but no statistically differences were present for anxiety and insomnia. No statistically significant differences were obtained among the 4 grades of college students for insomnia at either the T1 or T2.ConclusionWith progression of the COVID-19 epidemic, college students showed increasing levels of depression and anxiety, with Grade 4 college students being most seriously affected. It is imperative that intervention strategies be implemented to mitigate against these mental health issues resulting from the COVID-19 epidemic.

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Modeling for COVID-19 college reopening decisions: Cornell, a case study.

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Modeling for COVID-19 college reopening decisions: Cornell, a case study.

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