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Mietchen MS, Clancey E, McMichael C, Lofgren ET. Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301116. [PMID: 38260547 PMCID: PMC10802636 DOI: 10.1101/2024.01.10.24301116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
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 (βU) and community residents (βC), as well as the rate of cross-transmission between the two subpopulations (βM) 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 (βM) was considerably smaller [0.04 × 10-5 (95% CI: 0.00 × 10-5, 0.15 × 10-5)] compared to the community estimate (βC) at 2.09 × 10-5 (95% CI: 1.12 × 10-5, 2.90 × 10-5) and university estimate (βU) 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.
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Affiliation(s)
- Matthew S Mietchen
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA
| | - Erin Clancey
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA
| | | | - Eric T Lofgren
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA
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Merling MR, Williams A, Mahfooz NS, Ruane-Foster M, Smith J, Jahnes J, Ayers LW, Bazan JA, Norris A, Norris Turner A, Oglesbee M, Faith SA, Quam MB, Robinson RT. The emergence of SARS-CoV-2 lineages and associated saliva antibody responses among asymptomatic individuals in a large university community. PLoS Pathog 2023; 19:e1011596. [PMID: 37603565 PMCID: PMC10470930 DOI: 10.1371/journal.ppat.1011596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/31/2023] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
SARS-CoV-2 (CoV2) infected, asymptomatic individuals are an important contributor to COVID transmission. CoV2-specific immunoglobulin (Ig)-as generated by the immune system following infection or vaccination-has helped limit CoV2 transmission from asymptomatic individuals to susceptible populations (e.g. elderly). Here, we describe the relationships between COVID incidence and CoV2 lineage, viral load, saliva Ig levels (CoV2-specific IgM, IgA and IgG), and ACE2 binding inhibition capacity in asymptomatic individuals between January 2021 and May 2022. These data were generated as part of a large university COVID monitoring program in Ohio, United States of America, and demonstrate that COVID incidence among asymptomatic individuals occurred in waves which mirrored those in surrounding regions, with saliva CoV2 viral loads becoming progressively higher in our community until vaccine mandates were established. Among the unvaccinated, infection with each CoV2 lineage (pre-Omicron) resulted in saliva Spike-specific IgM, IgA, and IgG responses, the latter increasing significantly post-infection and being more pronounced than N-specific IgG responses. Vaccination resulted in significantly higher Spike-specific IgG levels compared to unvaccinated infected individuals, and uninfected vaccinees' saliva was more capable of inhibiting Spike function. Vaccinees with breakthrough Delta infections had Spike-specific IgG levels comparable to those of uninfected vaccinees; however, their ability to inhibit Spike binding was diminished. These data are consistent with COVID vaccines having achieved hoped-for effects in our community, including the generation of mucosal antibodies that inhibit Spike and lower community viral loads, and suggest breakthrough Delta infections were not due to an absence of vaccine-elicited Ig, but instead limited Spike binding activity in the face of high community viral loads.
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Affiliation(s)
- Marlena R. Merling
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Amanda Williams
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Najmus S. Mahfooz
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Marisa Ruane-Foster
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
| | - Jacob Smith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Jahnes
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Leona W. Ayers
- Department of Pathology, The Ohio State University, Columbus, Ohio, United States of America
| | - Jose A. Bazan
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Alison Norris
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Abigail Norris Turner
- Division of Infectious Disease, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Michael Oglesbee
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Seth A. Faith
- Infectious Disease Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Mikkel B. Quam
- Department of Epidemiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Richard T. Robinson
- Department of Microbial Infection & Immunity, The Ohio State University, Columbus, Ohio, United States of America
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Allen JL, Amick BC, Williams ML, Kennedy JL, Boehme KW, Forrest JC, Primack B, Sides EA, Nembhard WN, Gardner SF, Snowden JN, James LP, Olgaard E, Gandy J. A longitudinal study of SARS-CoV-2 antibody seroprevalence and mitigation behaviors among college students at an Arkansas University. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023:1-10. [PMID: 37289962 DOI: 10.1080/07448481.2023.2217456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 03/29/2023] [Accepted: 05/19/2023] [Indexed: 06/10/2023]
Abstract
Objective: Assess university students' SARS-CoV-2 antibody seroprevalence and mitigation behaviors over time. Participants: Randomly selected college students (N = 344) in a predominantly rural Southern state. Methods: Participants provided blood samples and completed self-administered questionnaires at three timepoints over the academic year. Adjusted odds ratios and 95% confidence intervals were estimated from logistic regression analyses. Results: SARS-CoV-2 antibody seroprevalence was 18.2% in September 2020, 13.1% in December, and 45.5% in March 2021 (21% for those with no vaccination history). SARS-CoV-2 antibody seroprevalence was associated with large social gatherings, staying local during the summer break, symptoms of fatigue or rhinitis, Greek affiliation, attending Greek events, employment, and using social media as the primary COVID-19 information source. In March 2021, seroprevalence was associated with receiving at least one dose of a COVID-19 vaccination. Conclusion: SARS-CoV-2 seroprevalence was higher in this population of college students than previous studies. Results can assist leaders in making informed decisions as new variants threaten college campuses.
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Affiliation(s)
- Jaimi L Allen
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Benjamin C Amick
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Mark L Williams
- Department of Health Behavior and Health Education, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Joshua L Kennedy
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| | - Karl W Boehme
- Department of Microbiology & Immunology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Centre for Microbial Pathogenesis and Host Inflammatory Responses, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - J Craig Forrest
- Department of Microbiology & Immunology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Brian Primack
- Department of Public Health and Medicine, University of Arkansas, Fayetteville, Arkansas, USA
| | - Erica Ashley Sides
- Translational Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Wendy N Nembhard
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Stephanie F Gardner
- College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Jessica N Snowden
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| | - Laura P James
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Translational Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Ericka Olgaard
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Jay Gandy
- Department of Environmental Health, University of Arkansas for Medical Sciences, Fayetteville, Arkansas, USA
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Harris MJ, Cardenas KJ, Mordecai EA. Social divisions and risk perception drive divergent epidemics and large later waves. EVOLUTIONARY HUMAN SCIENCES 2023; 5:e8. [PMID: 37587926 PMCID: PMC10426078 DOI: 10.1017/ehs.2023.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/22/2022] [Accepted: 01/19/2023] [Indexed: 02/25/2023] Open
Abstract
During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves.
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Moreland S, Zviedrite N, Ahmed F, Uzicanin A. COVID-19 prevention at institutions of higher education, United States, 2020-2021: implementation of nonpharmaceutical interventions. BMC Public Health 2023; 23:164. [PMID: 36694136 PMCID: PMC9872740 DOI: 10.1186/s12889-023-15079-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND In early 2020, following the start of the coronavirus disease 2019 (COVID-19) pandemic, institutions of higher education (IHEs) across the United States rapidly pivoted to online learning to reduce the risk of on-campus virus transmission. We explored IHEs' use of this and other nonpharmaceutical interventions (NPIs) during the subsequent pandemic-affected academic year 2020-2021. METHODS From December 2020 to June 2021, we collected publicly available data from official webpages of 847 IHEs, including all public (n = 547) and a stratified random sample of private four-year institutions (n = 300). Abstracted data included NPIs deployed during the academic year such as changes to the calendar, learning environment, housing, common areas, and dining; COVID-19 testing; and facemask protocols. We performed weighted analysis to assess congruence with the October 29, 2020, US Centers for Disease Control and Prevention (CDC) guidance for IHEs. For IHEs offering ≥50% of courses in person, we used weighted multivariable linear regression to explore the association between IHE characteristics and the summated number of implemented NPIs. RESULTS Overall, 20% of IHEs implemented all CDC-recommended NPIs. The most frequently utilized NPI was learning environment changes (91%), practiced as one or more of the following modalities: distance or hybrid learning opportunities (98%), 6-ft spacing (60%), and reduced class sizes (51%). Additionally, 88% of IHEs specified facemask protocols, 78% physically changed common areas, and 67% offered COVID-19 testing. Among the 33% of IHEs offering ≥50% of courses in person, having < 1000 students was associated with having implemented fewer NPIs than IHEs with ≥1000 students. CONCLUSIONS Only 1 in 5 IHEs implemented all CDC recommendations, while a majority implemented a subset, most commonly changes to the classroom, facemask protocols, and COVID-19 testing. IHE enrollment size and location were associated with degree of NPI implementation. Additional research is needed to assess adherence to NPI implementation in IHE settings.
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Affiliation(s)
- Sarah Moreland
- grid.416738.f0000 0001 2163 0069Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA 30329 USA ,grid.410547.30000 0001 1013 9784Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830 USA
| | - Nicole Zviedrite
- Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA, 30329, USA.
| | - Faruque Ahmed
- grid.416738.f0000 0001 2163 0069Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA 30329 USA
| | - Amra Uzicanin
- grid.416738.f0000 0001 2163 0069Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA 30329 USA
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