Researchers develop real-time method to project COVID-19 quarantine housing needs


It’s hard to plan ahead when SARS-CoV-2, the virus that causes COVID-19, is so unpredictable. But, now there is a simple method to predict one of the resources needed to slow the spread of COVID-19 in communities. Researchers at Boston University (BU) have developed a real-time method to project COVID-19 quarantine needs in collective housing ten days in advance.

Eric Kolaczyk, director of the Hariri Institute and professor of mathematics and statistics, Laura White, professor of biostatistics at the BU School of Public Health, and Wenrui Li, a former doctoral student in mathematics and statistics, joined forces to create a model simple statistic that incorporates readily available data, including daily case counts and contact tracing details, and is informed by experiments and sound judgments about human behavior. The team’s findings were recently published in The American Journal of Public Health (AJPH).

As has been the case for universities around the world, the initial wave of COVID-19 cases that shut down Massachusetts has raised some concerns about returning students to BU’s campus. So, over the summer of 2020, BU leadership supporting the COVID-19 response tasked faculty experts to determine the effectiveness of testing, contact tracing, and quarantine measures in bringing students safely in the fall. “There was a lot of collaboration between different departments and parts of the university, as well as the university management who were collecting and storing the data,” White said, “That’s a really big feature of BU’s response. to COVID as far as we’ve done, which has proven to be a very effective response.”

However, some of the initial predictions about quarantine and isolation were wrong. Fortunately, BU has reserved hundreds more beds than needed. “We found ourselves in uncharted waters determining the number of quarantine and isolation beds,” said Peter Smokowski, vice president of ancillary services, “However, the modeling done by Eric’s team was very useful for establishing a reference number.” The researchers’ original model was intended to provide guidance for getting students back to campus safely, rather than specific estimates on the number of beds needed.

Modeling experts Kolaczyk, White and Li continued to work together in the fall to develop a more accurate model for predicting quarantine needs. The team’s new model incorporates data on the daily number of positive cases for students and information from contact tracing on how off- and on-campus student populations interact. The model also takes into account dates when COVID-19 could spread faster, such as long weekends or holidays. The methods the team used to create their model are effective and fairly simple. “The software is only five lines of code,” Kolaczyk said, “yet it’s based on a very principled method, based on standard notions of arrival of infected individuals and local transmission.” Li remains pleasantly surprised by the simplicity and efficiency of the model. “Our model is simple, but it works well,” Li said.

A different variant of SARS-CoV-2 or a new disease could emerge in the future, and figuring out how to allocate resources like quarantine housing could make a big difference in how quickly it spreads in communal settings. The researchers’ model can be applied to similar contexts where nearby people interact with outside groups. Correctional facilities, retirement homes, or military housing could use this model to forecast quarantine needs and allocate housing resources appropriately.

The driving force for us in transitioning from a BU project to a release is the realization that the need for quarantine space optimization is ubiquitous across the globe. Our model can be used as a predictive tool to allocate resources from a relatively softer baseline…rather than being reactive.”

Eric Kolaczyk, Director, Hariri Institute and Professor of Mathematics and Statistics


Journal reference:

Li, W. et al. (2022) Projection of the use of quarantine during a pandemic. American Journal of Public Health.


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