Dataset Information

Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community.

ABSTRACT: Prior studies suggest that population heterogeneity in SARS-CoV-2 (COVID-19) transmission plays an important role in epidemic dynamics. During the fall of 2020, many US universities and the surrounding communities experienced an increase in reported incidence of SARS-CoV-2 infections, with a high disease burden among students. We explore the transmission dynamics of an outbreak of SARS-CoV-2 among university students, how it impacted the non-student population via cross-transmission, and how it could influence pandemic planning and response. Using surveillance data of reported SARS-CoV-2 cases, we developed a two-population SEIR model to estimate transmission parameters and evaluate how these subpopulations interacted during the 2020 Fall semester. We estimated the transmission rate among the university students (β_U) and community residents (β_C), as well as the rate of cross-transmission between the two subpopulations (β_M) using particle Markov Chain Monte Carlo (pMCMC) simulation-based methods. We found that both populations were more likely to interact with others in their population and that cross-transmission was minimal. The cross-transmission estimate (β_M) was considerably smaller [0.04 × 10^-5 (95% CI: 0.00 × 10^-5, 0.15 × 10^-5)] compared to the community estimate (β_C) at 2.09 × 10^-5 (95% CI: 1.12 × 10^-5, 2.90 × 10^-5) and university estimate (β_U) at 27.92 × 10^-5 (95% CI: 19.97 × 10^-5, 39.15 × 10^-5). The higher within population transmission rates among the university and the community (698 and 52 times higher, respectively) when compared to the cross-transmission rate, suggests that these two populations did not transmit between each other heavily, despite their geographic overlap. During the first wave of the pandemic, two distinct epidemics occurred among two subpopulations within a relatively small US county population where university students accounted for roughly 41% of the total population. Transmission parameter estimates varied substantially with minimal or no cross-transmission between the subpopulations. Assumptions that county-level and other small populations are well-mixed during a respiratory viral pandemic should be reconsidered. More granular models reflecting overlapping subpopulations may assist with better-targeted interventions for local public health and healthcare facilities.

SUBMITTER: Mietchen MS

PROVIDER: S-EPMC10802636 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Unexpected Transmission Dynamics in a University Town: Lessons from COVID-19.

Clancey Erin E Mietchen Matthew S MS McMichael Corrin C Lofgren Eric T ET

medRxiv : the preprint server for health sciences 20241114

Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to re-open during the second wave of COVID-19 in the fall of 2020, which was controversial because incidence in young adults was on the rise. The migration of students back to campuses worried many that transmission within student populations would spread into surrounding communities. In light of this, many colleges and universities implemented mitigation strategies, wi ...[more]

PMID: 38260547

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Project description:BackgroundSince the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2.MethodsWe examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations.ResultsFor both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community. Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student's t-test p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security. Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave (one SD range [90%,102%]). We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial regression p = 0.015).ConclusionsWe found significant evidence of community/staff/incarcerated population inter-linkage of SARS-COV-2 transmission. Further study is warranted to determine which control measures aimed at the incarcerated population and/or staff are most efficacious at preventing or controlling outbreaks.

Dataset Information

Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community.

Publications

Unexpected Transmission Dynamics in a University Town: Lessons from COVID-19.

Similar Datasets

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