Fig 1.
(A) The base model with single interventions applied. Note that the reduction in infections from “fewer students” is smaller than it appears since there are 50% fewer people on campus in that intervention. (B) The impact of testing latency on a campus with 25% fewer students and testing and quarantine in effect.
Fig 2.
Total infections by room type in the base model and with the gym, library, and dining hall closed.
In an “austere closure”, students spend any extra free time alone. In a “social closure”, students spend half of their free time socializing.
Fig 3.
The total infection counts colored by size for different policy and adherence intensities.
Table 1.
Sample schedules for an on-campus student, an off-campus student, and a faculty member.
Each row is the time of day.
Table 2.
At the top, counts for the number of single and double dorm rooms, the number of seats in classrooms.
In the middle, the number of classrooms in each type of building. On the bottom, the number of each type of building.
Table 3.
The core and leaf capacity and risk multiplier for different buildings.
The quantity x is the number of people assigned to that space.
Table 4.
Parameters.
Fig 4.
Schematic of the network.
Fig 5.
Exposure profiles for 100 agents are arranged in decreasing order then averaged.
A 95% confidence interval is included around the curve. Panel A shows the exposure profile for off-campus students. The larger panel of Panel B shows the exposure profile for on-campus students with the maximum entry (corresponding to a dorm roommate) removed. The smaller subpanel in Panel B shows the exposure profile when the roommate is included. Panel C shows the ordered average exposure profile for 100 faculty.
Fig 6.
Agent states over 100 days in the base model.
Panel A shows a 95% confidence around the mean behavior from 40 trials. Panel B shows the number of active infections over time for each trial.
Fig 7.
The total number of cases (numeric) and the coefficient of variation (standard deviation/mean; colorbars) for different policy and adherence intensity levels.
Table 5.
The intervention parameter choices corresponding to different intensities for administrative policy (left) and student adherence (right).
We describe in words Medium Policy and Medium Student Adherence as an example. Medium Policy screens of the student population weekly with a 3-day latency
.
students are removed from the population. The gym, library, dining hall, and large gatherings are closed. Medium student adherence has half of students wearing facemasks while socializing
. A
proportion of students comply with screening tests. Students spend free time from building closures in their dorm room with probability
for each occurrence in their schedule. Additionally, students socialize less by a factor of
.
Fig 8.
Empirical measurements of R0(s) computed as in (4) with different initial seed sizes s of the on-campus student population infected.
The results from 100 runs are shown for each R0(s).
Fig 9.
The average number of days (y-axis) to go from x/2 to at least x infections.
We omit x = 20 since we initially seed 10 agents in the exposed state and there is latency for infections to begin. We omit x > 320 since for such large x-value the doubling time slows significantly from a herd-immunity effect.
Fig 10.
A sensitivity analysis of the tuning parameter, p.
We fix the student adherence to be medium, and show the total number of cases for each of the three administrative policies.
Fig 11.
A sensitivity analysis of the off-campus multiplier.
We fix the student adherence to be medium, and show the total number of cases for each of the three administrative policies.
Fig 12.
A sensitivity analysis of facemask effectiveness.
Displayed are total number of infections after a semester with (perfect facemask compliance), but no other intervention.