University of Minnesota and Minnesota Department of Health awarded $17.5 million to help develop national outbreak response network
The University of Minnesota (UMN) School of Public Health (SPH), UMN Medical School, UMN Institute for Health Informatics, Minnesota Electronic Health Records Consortium, and Minnesota Department of Health (MDH) were awarded $17.5 million to help establish an outbreak response network to support decision makers during public health emergencies. As one of 13 funded partners across the U.S., researchers at UMN and MDH will work alongside the CDC’s Center for Forecasting and Outbreak Analytics (CFA) to support the new national network — the Outbreak Analytics and Disease Modeling Network (OADM).
Grants were awarded in three distinct areas of the model’s development — innovation, integration and implementation. The UMN and MDH team will focus on the integration phase, meaning that over the next five years, the team will identify the most promising approaches from the innovation pipeline and pilot test them at the state, local, tribal or territorial level to gauge the success of the technique in practical application by public health decision makers.
“Each of the grantees will help us move the nation forward in our efforts to better prepare and respond to infectious disease outbreaks that threaten our families and our communities,” said Dylan George, director of the Center for Forecasting and Outbreak Analytics. “We are committed to working alongside these outstanding partners to achieve our goal of using data and advanced analytics to support decision-makers at every level of government.”
The grant has three co-principal investigators: Eva Enns, an associate professor at SPH, R. Adams Dudley, a professor in the UMN Medical School, Institute for Health Informatics and SPH, and Kristin Sweet, manager of Infectious Disease Cross-Cutting Epidemiology, Programs and Partnerships at MDH.
The UMN and MDH team will work to address several key issues identified during management of the COVID-19 pandemic. The first is the challenge of accurately predicting how many people might get sick and how many cases a particular intervention might prevent. A key element in predicting the number of cases requires knowing how many people an individual routinely comes into contact with. The Minnesota team will undertake a survey to ask how many people an individual interacts with each day and how that varies by the type of work they do and the time of year. This data will then be incorporated into a response-ready decision support tool to allow policymakers to quickly understand key benefits, costs and tradeoffs between different emergency response choices to the next pandemic.
“The COVID-19 pandemic revealed a need to understand both localized disease patterns as well as the benefits and costs of different policy choices, like whether to close schools or mandate masks,” said Enns. “Creating the analytic tools needed to support these decisions in the middle of a crisis was a challenge. With this project, we have the opportunity to engage in thoughtful planning and development of modeling and analytic methods to generate clear, tailored and actionable evidence to support emergency response decision-making in the face of an infectious threat.”
The team will also address the challenge of accurately counting new cases by creating machine-learning algorithms that will learn which symptom clusters come together at different seasons of the year, like flu symptoms becoming more common in winter. The algorithms will also be ready to recognize anomalies or new patterns of symptoms.
The Minnesota Electronic Health Record Consortium (MNEHRC), a partnership of MDH and 11 of the largest health systems and organizations in Minnesota, will be a key data source, collaborator and implementation partner for the project.
“We fought COVID with our hands tied. We had no test and an incomplete list of symptoms. We needed ways to gather and analyze clinical data quickly to better understand this novel pathogen. With the systems we plan to design and test, we’ll be able to characterize new clusters of symptoms and have a more complete picture of a novel disease as it emerges,” said Dudley.
“The Minnesota Department of Health is very grateful for this support from the CDC and excited to leverage the modeling and analytics expertise of our partners at the University of Minnesota and the Minnesota Electronic Health Record Consortium as we embark on this important initiative together,” said MDH’s Principal Investigator Kristin Sweet. “We are eager to build on our experience during COVID-19 as we refine and test new and innovative analytic tools that we can deploy during future public health emergencies.”
The CDC established the Center for Forecasting and Outbreak Analytics (CFA) to enable timely, effective decision-making to improve outbreak response using data, modeling, and analytics. To do so, CFA produces models and forecasts to characterize the state of an outbreak and its course, inform public health decision makers on potential consequences of deploying control measures, and support innovation to continuously improve the science of outbreak analytics and modeling.