Global and local mobility as a barometer for COVID-19 dynamics

Linka, Kevin; Goriely, Alain; Kuhl, Ellen

doi:10.1007/s10237-020-01408-2

Cited by 41 publications

(25 citation statements)

References 44 publications

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“…The response included a rapid and dramatic reduction in mobility, which has now been documented with cellphone data in a large number of papers (Brinkman and Mangum, 2020;Couture et al, 2020;. The reduction in mobility also occurred outside the U.S., and it often preceded formal legal rules (Abu-Rayash and Dincer, 2020;Cintia et al, 2020;Huang et al, 2020;Linka et al, 2020;Soucy et al, 2020). These declines in mobility echo centuries of people responding to plagues by either sheltering in place or taking flight.…”

mentioning

confidence: 96%

Determinants of Small Business Reopening Decisions After COVID Restrictions Were Lifted

Balla-Elliott¹,

Cullen

Glaeser

et al. 2020

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mentioning

confidence: 96%

Determinants of Small Business Reopening Decisions After COVID Restrictions Were Lifted

Balla-Elliott¹,

Cullen

Glaeser

et al. 2020

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show abstract

“…One of the main risks associated with campus reopening is bringing students from high prevalence regions to campus and allowing them to mix with other students, possibly from low prevalence regions [8,15]. Despite various mitigating strategies including testing, quarantine, contact tracing, facemask usage, and dedensification [16,28], the risk of creating an uncontrollable outbreak and spreading the disease to a large part of the campus population remains exceptionally high as demonstrated by the cautious reopening followed by the rapid closing of the University of North Carolina at Chapel Hill in August 2020 [27].…”

Section: Motivationmentioning

confidence: 99%

Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics. A campus reopening study

Linka

Peirlinck

Schäfer

et al. 2021

Preprint

Self Cite

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The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under base-line reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

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“…One of the main risks associated with campus reopening is bringing students from high prevalence regions to campus and allowing them to mix with other students, possibly from low prevalence regions [ 9 , 17 ]. Despite various mitigating strategies including testing, quarantine, contact tracing, facemask usage, and disinfection [ 18 , 30 ], the risk of creating an uncontrollable outbreak and spreading the disease to a large part of the campus population remains exceptionally high as demonstrated by the cautious reopening followed by the rapid closing of the University of North Carolina at Chapel Hill in August 2020 [ 29 ].…”

Section: Motivationmentioning

confidence: 99%

Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study

Linka

Peirlinck

Schäfer

et al. 2021

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

show abstract

Global and local mobility as a barometer for COVID-19 dynamics

Cited by 41 publications

References 44 publications

Determinants of Small Business Reopening Decisions After COVID Restrictions Were Lifted

Determinants of Small Business Reopening Decisions After COVID Restrictions Were Lifted

Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics. A campus reopening study

Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study

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