Relative role of community transmission and campus contagion in driving the spread of SARS-CoV-2: Lessons from Princeton University

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Abstract

Mathematical models have played a crucial role in exploring and guiding pandemic responses. University campuses present a particularly well-documented case for institutional outbreaks, thereby providing a unique opportunity to understand detailed patterns of pathogen spread. Here, we present descriptive and modeling analyses of SARS-CoV-2 transmission on the Princeton University (PU) campus—this model was used throughout the pandemic to inform policy decisions and operational guidelines for the university campus. Epidemic patterns between the university campus and surrounding communities exhibit strong spatiotemporal correlations. Mathematical modeling analysis further suggests that the amount of on-campus transmission was likely limited during much of the wider pandemic until the end of 2021. Finally, we find that a superspreading event likely played a major role in driving the Omicron variant outbreak on the PU campus during the spring semester of the 2021–2022 academic year. Despite large numbers of cases on campus in this period, case levels in surrounding communities remained low, suggesting that there was little spillover transmission from campus to the local community.

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Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Seth Flaxman, Swapnil Mishra, Axel Gandy … (2020)

Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.

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Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Stephen M. Kissler, Christine Tedijanto, Edward Goldstein … (2020)

It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.

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Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

Joel Hellewell, Sam Abbott, Amy Gimma … (2020)

Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

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Author and article information

Contributors

Sang Woo Park:

ORCID: https://orcid.org/0000-0003-2202-3361

Irini Daskalaki

Robin M Izzo

Irina Aranovich

Aartjan J W te Velthuis:

ORCID: https://orcid.org/0000-0002-5129-3953

Daniel A Notterman

C Jessica E Metcalf:

ORCID: https://orcid.org/0000-0003-3166-7521

Bryan T Grenfell

I Wilson: Role: Editor

Journal

Journal ID (nlm-ta): PNAS Nexus

Journal ID (iso-abbrev): PNAS Nexus

Journal ID (publisher-id): pnasnexus

Title: PNAS Nexus

Publisher: Oxford University Press (US )

ISSN (Electronic): 2752-6542

Publication date Collection: July 2023

Publication date (Electronic): 03 July 2023

Publication date PMC-release: 03 July 2023

Volume: 2

Issue: 7

Electronic Location Identifier: pgad201

Affiliations

Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA

University Health Services, Princeton University , Princeton, NJ 08544, USA

Environmental Health and Safety, Princeton University , Princeton, NJ 08544, USA

Princeton University Clinical Laboratory, Princeton University , Princeton, NJ 08544, USA

Department of Molecular Biology, Princeton University , Princeton, NJ 08544, USA

Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA

Princeton School of Public and International Affairs, Princeton University , Princeton, NJ 08544, USA

Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA

Princeton School of Public and International Affairs, Princeton University , Princeton, NJ 08544, USA

Author notes

To whom correspondence should be addressed. Email: swp2@ 123456princeton.edu

Competing Interest: The authors declare no competing interest.

Author information

Sang Woo Park https://orcid.org/0000-0003-2202-3361

Aartjan J W te Velthuis https://orcid.org/0000-0002-5129-3953

C Jessica E Metcalf https://orcid.org/0000-0003-3166-7521

Article

Publisher ID: pgad201

DOI: 10.1093/pnasnexus/pgad201

PMC ID: 10338902

SO-VID: 4248cd08-ffa3-4436-89b8-e5cd029d2e6f

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 15 August 2022

Date revision received : 03 May 2023

Date accepted : 22 May 2023

Date: 13 July 2023

Page count

Pages: 12

Relative role of community transmission and campus contagion in driving the spread of SARS-CoV-2: Lessons from Princeton University

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Novel Coronavirus Disease COVID-19

Most cited references 38

Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

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