Mathematical Modeling-; which combines mathematics, statistics, computing and data; is an essential tool for public health professionals, who use it to study the spread of disease, predict the future course of epidemics, and evaluate epidemic control strategies.
As the COVID-19 pandemic has driven public health decision-making nationwide, a wide range of disease models have proliferated. Across the country, city, county and state officials worked with university modeling teams to develop custom models to predict what would happen in their jurisdictions. Municipalities that lacked the resources to develop location-specific models were forced to extrapolate data from other models and make decisions based on less than ideal information. Since there was no IT infrastructure to run these models in a standardized way, the confusion caused by the cacophony of inconsistent models most likely eroded public confidence in modeling as a powerful tool.
Assistant Professor Joe Mihaljevic of Northern Arizona University’s School of Informatics, Computing, and Cyber Systems (SICCS) worked with public health partners across the state and country to share computer models mapping the spread of the coronavirus. Mihaljevic, a disease ecologist who applies epidemiological modeling techniques to wildlife and, more recently, human disease, has received more than $3.5 million from the National Institutes for Health to take modeling to the next level with EpiMoRPH (Epidemiological Modeling Resources for Public Health), which will significantly automate and accelerate the development of epidemiological models.
“Throughout the pandemic, we realized we needed models at spatial scales relevant to the needs of specific public health partners,” Mihaljevic said. “Across the country, small municipalities, like cities, have often been forced to inform their decisions based on models developed at larger spatial scales, like county scales or even the scale of the State, when they really needed a custom model for their location. As we reflected on the complex challenges we faced and the things we learned modeling the coronavirus, we asked this question: If a new epidemic or pandemic were to emerge, could we imagine a system that would make it easier for modellers to set up and operate? and to collaborate across groups? And could we use that to develop locally customized models that are better for decision making? decision ? “
“As we developed the EpiMoRPH proposal, we tried to define a manageable part of that answer that we could accomplish within five years, 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,” he said.
The project will use two different viral 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,” Mihaljevic said, “supporting rapid model development and uniform evaluation of models against reference data. Beyond that, however, EpiMoRPH will provide an accessible interface for public health professionals to identify models relevant to their region and then use those models to generate municipality-specific predictions.”
Multi-institutional collaboration to include the Public Health Advisory Council
Mihaljevic’s co-researchers on the project are Professor Eck Doerry of SICCS, who will lead software development and cloud-based computing; Crystal Hepp, associate professor at ISCCS, also at the Translational Genomics Research Institute (TGen), who will lead the acquisition and management of surveillance data on viral cases; and Samantha Sabo, associate professor in the NAU Center for Health Equity Research, who will help engage and liaise with public health partners and lead formal evaluation efforts.
NAU investigators will work with researchers from several other institutions, including Esma Gel of the University of Nebraska, who will participate in optimization theory and algorithm development; Sanjay Mehrotra of Northwestern University, who will lead all work on the development of optimization theory; and Timothy Lant of Arizona State University, who will help mobilize and coordinate a public health advisory board.
The team will form a Public Health Advisory Council (PHAC) comprised of 15 local, regional and national public health and epidemiological modeling stakeholders who will provide critical feedback and assessment on the system as it develops. Collaborators from the Arizona Department of Health Services, with whom Mihaljevic and his team have worked extensively during the COVID-19 pandemic, will be part of this effort.
“PHAC will help us better understand the logistical constraints and drive the development of the user interface so that it reflects the level of detail required by the intended users,” Mihaljevic said. “We will work closely with the advisory board to evaluate and refine our technologies, ensuring that our innovations meet the changing needs of public health partners, while engaging the epidemiological modeler community.”
Additionally, many graduate and undergraduate computer science and computer science students will be involved in efforts to develop web-based cyber-infrastructures, coding automation scripts, and writing technical documentation. Two undergraduate public health researchers will help the team conduct formal technology assessments and develop outreach methods with PHAC.
Could EpiMoRPH help make predicting epidemics as reliable as predicting the weather?
“Once EpiMoRPH is built, a typical user might be someone representing public health in Flagstaff, for example. During the pandemic, that user might have wanted to understand what they should expect with COVID-19 in terms hospitalizations in the next 30 days. Because our model at the time was Coconino County-wide, we could tell them what was happening at the county level, but not specifically for Flagstaff,” Mihaljevic said.
“And so, once EpiMoRPH is in place, if a model hasn’t been built for Flagstaff, a public health official could enter certain characteristics of that particular location, such as population density, geography, etc., and immediately see what patterns are and then the EpiMoRPH system would use those patterns to develop a custom forecast for Flagstaff.
“In the ideal scenario, community modelers could contribute models and public health professionals could also contribute data. Our system would match models and data and pit them against each other and try to determine which models are the best for specific needs Locations.
“Eventually, as models become more and more accurate, predicting epidemics could become as routine and as reliable as predicting the weather,” Mihaljevic said.
Revolutionizing the way modeling is done
“It’s a whole new way of thinking about developing large-scale models,” said co-investigator Doerry, “so that the next time we have a pandemic, we’re ready and can produce consistent, intelligible models and consistent from the start.
“Our ultimate goal is to revolutionize the way modeling is done by defining a uniform conceptual standard with which all current and existing models can be characterized. Automatically test them on thousands of different locales to discover which model is best suited for any set of locale conditions Finally, we’ll add an infinitely scalable cloud computing infrastructure that can bring massive computing power to do All this big work. EpiMoRPH is so powerful precisely because it explores what you could achieve if you took cutting-edge modeling of infectious pathogens and combined it with cutting-edge cloud-based big data computing.”
EpiMoRPH will contribute to the national modeling community
With a greater emphasis on disease modeling, the EpiMoRPH platform could potentially be adopted as a national hub. Academic labs and national organizations across the country are racing to make epidemic modeling more accessible, useful and accurate. For example, the Centers for Disease Control and Prevention (CDC) recently launched its Center for Forecasting and Outbreak Analytics (CFA), which will improve the country’s ability to use data, models and analytics to enable rapid decision-making. and effective in responding to public health threats for the CDC and its public health partners. Mihaljevic hopes that EpiMoRPH could make a strong contribution to national efforts to standardize and automate epidemic modeling, with the aim of creating reliable forecasts for local decision makers.
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