COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020

Cevasco, Kevin E; North, Hayley M.; Zeitoun, Sheryne A.; Wofford, Rachel N.; Matulis, Graham; Gregory, Abigail F.; Hassan, Maha H.; Abdo, Aya D.; Farris, David; Roess, Amira; Fricken, Michael E. von

doi:10.1371/journal.pone.0240786

Cited by 10 publications

(15 citation statements)

References 8 publications

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“…The second provides information about country/countries of study and references. Category Country and references Implementation of NPIs Government response trackers: Canada [384] , 195 countries [385] , 180 countries [348] , 56 countries [42] ; Universities response in USA [386] Mobility Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa, USA [387] ; Italy [40] Contacts Belgium, China, Finland, France, Germany, Italy, Hong Kong, Luxembourg, Netherlands, Poland, Peru, Russia, South Africa, UK, Vietnam, Zambia, Zimbabwe [41] …”

Section: Datasetsmentioning

confidence: 99%

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 of authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

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Section: Datasetsmentioning

confidence: 99%

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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“…In this light, we draw important lessons for universities around the world and contribute to our general understanding of the effects of the pandemic on higher education. Although the empirical results are only valid for institutions in the UK, the paper provides a useful research design that can be replicated for data on university responses in other countries [ 53 , 59 ]. In addition, our theoretical framework and selection of covariates, as well as the emphasis on survival analysis and models of policy diffusion, will serve as useful guidelines for further research.…”

Section: Resultsmentioning

confidence: 99%

“…In this section we supplement our analysis of Twitter data with information from university webpages. Universities also used Covid-19 dedicated webpages to raise awareness of the pandemic and provide guidance and advice to students and staff [ 53 ]. We acknowledge that the content of Covid-19 specific webpages is different than Twitter posts–webpages require more careful planning and implementation than tweets, as well as constant updating and maintenance.…”

Section: Methodsmentioning

confidence: 99%

“…Research has also been devoted to the timing and heterogeneity of non-pharmaceutical interventions during the height of the pandemic [ 53 , 54 ]. Closely linked to this strand of work are epidemiological simulations for university campuses that inform university interventions, including contact tracing and quarantining [ 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

“…This is particularly pertinent to risk communication and therefore our aim is to explain why some universities posted social media messages sooner than others . In order to confirm our results, we supplemented our analysis of social media with a study of the introduction of coronavirus-related university webpages, which were also widely used by universities to communicate Covid-19 information to stakeholders [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

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Variation in the timing of Covid-19 communication across universities in the UK

et al. 2021

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During the Covid-19 pandemic, universities in the UK used social media to raise awareness and provide guidance and advice about the disease to students and staff. We explain why some universities used social media to communicate with stakeholders sooner than others. To do so, we identified the date of the first Covid-19 related tweet posted by each university in the country and used survival models to estimate the effect of university-specific characteristics on the timing of these messages. In order to confirm our results, we supplemented our analysis with a study of the introduction of coronavirus-related university webpages. We find that universities with large numbers of students are more likely to use social media and the web to speak about the pandemic sooner than institutions with fewer students. Universities with large financial resources are also more likely to tweet sooner, but they do not introduce Covid-19 webpages faster than other universities. We also find evidence of a strong process of emulation, whereby universities are more likely to post a coronavirus-related tweet or webpage if other universities have already done so.

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Survival Analysis

Flores

2022

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Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.

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COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020

Cited by 10 publications

References 8 publications

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Variation in the timing of Covid-19 communication across universities in the UK

Survival Analysis

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