Back

The Impacts of Testing Cadence, Mode of Instruction, and Student Density on Fall 2020 COVID-19 Rates On Campus

Stubbs, C. W.; Springer, M.; Thomas, T. S.

2020-12-09 epidemiology
10.1101/2020.12.08.20244574 medRxiv
Show abstract

We analyzed the COVID-19 infection rate among undergraduate students at 9 colleges and Universities in the greater Boston area and 4 comparison schools elsewhere, from Fall 2020. We assessed whether the cumulative rate of infection is dependent on the mode of instruction (in-person, hybrid, or remote), on the number and density of dorm-resident undergraduates, and/or on COVID-19 testing cadence. We limited our analysis to institutions that have implemented at least weekly PCR testing of dormitory-resident undergraduates. Our primary conclusions are that (i) the fraction of students that succumbed to a COVID-19 infection up through Nov 22, 2020 shows no correlation with either the total number of students on campus, or the fractional occupancy of dormitories, (ii) remote instruction vs. hybrid instruction has no significant measurable impact on cumulative infection rate, and (iii) there is evidence that testing 2 or 3 times per week is correlated with lower infection rates than weekly testing. These data are consistent with a hypothesis of students predominantly acquiring infection off-campus, with little community transmission within dormitory housing. This implies good student compliance with face mask and social distancing protocols. Significance StatementWe review the incidence of COVID-19 infection among under-graduate students for selected colleges and universities that conducted at least weekly COVID-19 testing during the Fall of 2020. We analyzed the infection-rate dependence on number of students on campus, dormitory residential density, instructional methodology (remote vs. hybrid), and testing cadence. This compilation of outcomes can help inform policy decisions for congregate settings.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.