A pair of federal grants totaling more than $2.6 million will help researchers at Washington State University better understand and predict how infectious diseases behave and spread in health care settings and other small populations.
“A lot of different groups in small, structured populations — like hospitals, universities, jails — wanted to make informed decisions about the trajectory of the COVID‑19 pandemic, but many of the techniques we use for modeling diseases are built for large populations, at a city, state or national level, and they don’t necessarily work well for small populations,” said Eric Lofgren, the principal investigator for the grants.
To help address those shortcomings, the National Institutes of Health awarded Lofgren, a professor in the Paul G. Allen School for Global Health at WSU, a 5‑year, $1.8 million grant to develop methods and tools for modeling disease spread and dynamics in these populations. Lofgren was also awarded a 3‑year, $877,000 grant from the Centers for Disease Control and Prevention to support three new fellowships focused on modeling infectious diseases specifically in health care settings.
Lofgren said the phenomena of emerging infectious diseases accelerating once they reach the medical system — known as nosocomial amplification — has been well documented in severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) epidemics, as well as Ebola outbreaks. One of the earliest major transmission events for COVID‑19 in the United States also took place in a health care facility.
“This phenomenon is especially problematic as it impacts the health of the health care workforce, creating a feedback loop where sickened health care workers both accelerate the epidemic and are unable to care for patients when they are most needed,” Lofgren said.
“This phenomenon is especially problematic as it impacts the health of the health care workforce…where sickened health care workers both accelerate the epidemic and are unable to care for patients when they are most needed.”Eric Lofgren, WSU professor
Paul G. Allen School for Global Health
Many current emerging epidemic models view the health care system simplistically and don’t reflect the layers of complexities. Lofgren’s team will work to create a more granular model capable of better understanding transmission, evaluating intervention and control strategies to prevent infections within the facility and into the general population.
“People aren’t mixing randomly in these settings,” Lofgren said. “In hospitals, you have wards; you have nursing stations; your providers are seeing different patients. All these things are constraints on how people mix with each other, but modeling these settings is really difficult.”
The CDC grant will fund three doctoral fellowships focused on a trio of projects involving modeling infectious diseases in health care settings. The positions will be under the umbrella of WSU’s Resistance Epidemiology Modeling Initiative, which was established in 2019 to foster research programs in antimicrobial resistance and promote interdisciplinary research. During the SARS‑CoV‑2 epidemic, the initiative’s scope was extended from antimicrobial-resistant infections to include other emerging infectious diseases.
One of the projects involves nosocomial amplification and constructing models of hospital-community interactions to explore how the resiliency of the health care system to emerging infections might be improved.
Lofgren pointed to a 2015 MERS outbreak sparked after a man who had unknowingly become infected with the virus while on a trip to Saudi Arabia returned home to South Korea where he visited four health care facilities while seeking care.
“Everybody else in that outbreak got the virus in a hospital. We’ve seen the same things in outbreaks of SARS and Ebola and, to a lesser extent, COVID‑19,” Lofgren said. “We want to try to understand some of the mechanisms by which we might be able to prevent that in the future.”
The remaining two projects aim to improve models for rural health care systems and to develop statistical and machine learning models to create simulated health care populations to model the impact of patient movement on healthcare-associated infections.