The National Institutes of Health awarded more than $3.5 million to a disease ecologist who applies epidemiological modeling techniques to wildlife and, more recently, to human diseases. The scientist intends to use the grant to take modeling to the next level with EpiMoRPH (Epidemiological Modeling Resources for Public Health), which will be focused on automating and expediting the development of epidemiological models.

Joe Mihaljevic, PhD, assistant professor in Northern Arizona’s School of Informatics, Computing, and Cyber Systems (SICCS), the recipient of the award, has been working with public health partners across the state and the country to share computer models mapping the spread of the coronavirus. Mathematical modeling, which combines math, statistics, computing, and data—is a critical tool for public health professionals, who use it to study how diseases spread, predict the future course of outbreaks, and evaluate strategies for controlling epidemics.

The EpiMoRPH resource will rely heavily on mathematical modeling.[Spicy Truffel/Getty Images]

“Throughout the pandemic, we realized we needed models that were at spatial scales relevant to the needs of specific public health partners,” Mihaljevic said. “Across the country smaller municipalities, like cities, were often forced to inform their decisions based on models that were developed at larger spatial scales, like county scales or even statewide scales, when what they really needed was a customized model for their location.


“As we thought about the complex challenges we faced and the things we learned modeling the coronavirus, we posed this question: if a new epidemic or pandemic were to emerge, could we envision a system that would make things much easier for modelers to get up and running and to collaborate across groups? And could we use this to develop locally customized models that are better for decision-making?

“As we developed the proposal for EpiMoRPH, we tried to define a manageable piece of that answer that we could accomplish in a five-year timeframe, to develop a good proof of concept modeling system for what we envision as the ‘next generation’ of epidemiological modeling that increases automation, promotes sharing and collaboration, accelerates discovery and rapidly advances our understanding of epidemics.

The project will use two different virus-based diseases as case studies: COVID-19 and SLEV (St. Louis Encephalitis Virus), but EpiMoRPH will work with any transmissible pathogen affecting humans, animals, or even plants.

“EpiMoRPH will provide a framework for characterizing meta-population disease models,” continued Mihaljevic, “supporting rapid model development and uniform evaluation of models against data benchmarks. Beyond that, however, EpiMoRPH will provide an accessible interface for public health professionals to identify models relevant to their locale and to then use these models to generate municipality-specific forecasts.”

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