Experts in the Air Force Research Laboratory (AFRL) are applying explainable machine learning and artificial intelligence approaches to develop thousands of models that could help federal, state and local decision-makers as they make re-opening decisions during the COVID-19 pandemic.

These forecasting models, which represent military installations and counties across the United States, will be available in publicly-accessible dashboards where leaders can interact with different simulations and examine various intervention strategies.

Dr. Ryan Kramer, Explainable Artificial Intelligence lead in AFRL’s 711 HPW, explained that his team paid special attention to the modeling that was being used across the United States for explaining and predicting how to flatten the curve. “Our goal was to complement these approaches by producing high-fidelity models that dramatically reduce the cone of uncertainty,” he said. “By helping to decipher the signal from the noise, we knew we could help commanders identify real-time model divergences and act on them in earlier interventional timeframes.”

The team has more than 30 different transition parameters in their models that all influence how COVID-19 spreads throughout a community. By utilizing actual data that the team collected from multiple open data sources, they can identify which simulations are accurately describing what is happening on the ground.

The challenge, Kramer explained, is defining which sets of parameters are correct for a given county or region, especially given the complexity in the transmission of a novel virus and the inherent biases in how the data is being reported across the country.

“To address this, we created a simulation library that sweeps across multiple ranges within each parameter thereby simulating every possible outcome that could happen within the model. When complete, we will have greater than 20 million models in our library, which allows us the ability to begin learning the unknown parameters that dictate transmission.”

“We utilize actual data to essentially fit county-level virus dynamics to the models within our library. We then utilize other advanced machine learning techniques that can account for social-distancing policy, adherence to policy, seasonal effects and underlying demographics to further refine the forecasts. Forecasts for individual counties can be aggregated for installation-level awareness, and at the state and national levels as well,” Kramer said.

The primary EXAIL team was supported by the team’s prime contractor, KBR. Other partners include the Air Force Institute of Technology as well as the Massachusetts Institute of Technology Lincoln Laboratories. Working across AFRL directorates, the Materials and Manufacturing Hyperthought team brought some timely expertise to help accelerate capabilities. The EXAIL team also reached out to the local industry to enhance transition efforts. Mile2, a local small business startup, has successfully transitioned optimized user interfaces, Kramer said.

“The people that I get to work with every day are immensely talented, and we pushed the limits in reimagining our capabilities to fight the COVID pandemic and enhance commander situational awareness,” Kramer said. “We also quickly realized that we had the capacity to do even more. So we brought in our colleagues at AFIT to assist in statistical verification and validation efforts. Since many of the findings are completely novel as we begin to understand the intricacies in the spread of COVID-19, experts at AFIT are helping us to move this information quicker to the decision-makers. I can’t say enough about our partners in AFRL’s Materials and Manufacturing directorate. Their Hyperthought team was critical to early prototyping and demonstration efforts. Our collaborative network also expands to MIT’s Lincoln Labs where we can interface with thought leaders across disciplines from supercomputing to reinforcement learning.”