Barreira, Paul J.
Sabeti, Pardis C.
UMass Chan AffiliationsDepartment of Microbiology and Physiological Systems
Program in Bioinformatics and Integrative Biology
Document TypeJournal Article
public health intervention
stochastic susceptible exposed infectious recovered model
MetadataShow full item record
AbstractCollege campuses are vulnerable to infectious disease outbreaks, and there is an urgent need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world adapt 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 contain a mumps outbreak at Harvard University in 2016. We used our model to determine which containment interventions were most effective and study alternative scenarios without and with earlier interventions. This model allows for stochastic variation in small populations, missing or unobserved case data and changes in disease transmission rates post-intervention. The results suggest that control measures implemented by the University's Health Services, including rapid isolation of suspected cases, were very effective at containing the outbreak. Without those measures, the outbreak could have been four times larger. More generally, we conclude that universities should apply (i) diagnostic protocols that address false negatives from molecular tests and (ii) strict quarantine policies to contain the spread of easily transmissible infectious diseases such as mumps among their students. This modelling approach could be applied to data from other outbreaks in college campuses and similar small population settings.
Shah M, Ferra G, Fitzgerald S, Barreira PJ, Sabeti PC, Colubri A. Containing the spread of mumps on college campuses. R Soc Open Sci. 2022 Jan 26;9(1):210948. doi: 10.1098/rsos.210948. PMID: 35116142; PMCID: PMC8790351. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/27563
Rights© 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.