ABSTRACT
A cohort of laboratorians with positive SARS-CoV2 test results were uncovered during asymptomatic COVID-19 screening programs at six universities. Follow-up PCR and antibody tests showed that most of these cases were not true COVID-19 infection but instead arose from reverse-transcribed and amplified viral sequences (amplicons) that are generated during research. Environmental testing showed widespread contamination of amplicons in lab spaces including notebooks, keyboards, glasses, and doorknobs. Minimizing instances of amplicon contamination and developing protocols for handling suspected cases are critical to propel research efforts and to avoid diverting university and healthcare resources from patients with COVID-19. Removal of these individuals from the standard testing protocol, per CDC guidelines for positive cases, risks the spread of true infection. We discuss potential prevention and mitigation strategies.
ABSTRACT
College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model allows for stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. However, one needs to be careful about the assumptions implicit in the model to ensure that the estimated parameters have a reasonable interpretation. This modeling approach could be applied to data from other outbreaks in college campuses and similar small-population settings.
