Northeastern Researchers Develop Real-Time Tools for Disease Outbreak Forecasting

Nearly three years after the official end of the COVID-19 pandemic, researchers at Northeastern University, led by network scientist Alessandro Vespignani, are continuing to refine and expand their disease forecasting capabilities. Their work focuses on maintaining preparedness for future outbreaks, recognizing that the lessons learned during the pandemic must not be abandoned.

From Pandemic Response to Proactive Preparedness

During the early stages of the COVID-19 pandemic in 2020, Vespignani’s team developed transmission models that accurately predicted pandemic-level disease spread by March of that year. Now, they have created post-pandemic datasets and mobility maps, representing the first comprehensive study of American interaction and movement patterns in the post-COVID era. This includes a real-time dashboard for population movement and software – Epydemix – enabling scientists and public health experts to model and compare disease transmission scenarios.

“During COVID, we built tools while using them,” said Vespignani, Sternberg Family Distinguished University Professor and director of Northeastern’s Network Science Institute. He likened the scientific effort to model disease transmission during the pandemic to “building a plane while flying it.”

“Now COVID is over and we are going back to normal,” Vespignani stated. “Many of those technologies, approaches are being abandoned. We cannot do that. We demand to keep those tools sharp and ready for the next time,” even flu season.

Enhancing Situational Awareness for Public Health Officials

The technology developed by Vespignani’s team aims to provide the situational awareness necessary for public health officials to make informed decisions regarding interventions, such as vaccination campaigns, school closures, and hospital staffing. The team openly shares data with partners and makes it publicly available on their websites.

This open-science project is part of the mission of EPISTORM, a Center for Forecasting and Outbreak Analytics (CFA) funded by the CDC and led by Vespignani since 2024. EPISTORM is focused on improving early detection and preparedness for infectious disease outbreaks in the U.S.

Matteo Chinazzi, research associate professor at Northeastern’s Roux Institute, explained that the mobility platform dashboard helps “modulate transmission.”

Epydemix: Lowering Barriers to Epidemic Modeling

The Epydemix software is designed to “lower the barriers between the people who actually need the tools and the practical implementation of epidemic models,” according to Nicolo Gozzi, a research scientist collaborating with the Northeastern Network Science Institute.

The Importance of Population Movement Data

The post-pandemic behavior data utilizes anonymized GPS movement data from over a million mobile devices to understand how people interact and travel, and to assess infection risk. Vespignani refers to these interactions as the “wiring” that drives outbreaks, noting that this “wiring” has not fully returned to pre-COVID patterns.

Specifically, people now have fewer contacts in the workforce. The ongoing data updates from EPISTORM can be integrated into different infection scenarios to determine the necessity of interventions like school closures or mandated policies, or to assess whether public behavior is already changing in response to disease awareness.

“If we keep using old contact assumptions, we will misread transmission risk and mis-time preparedness,” Vespignani warned.

A Real-Time Dashboard on Mobility

The U.S. Mobility platform, developed under EPISTORM, provides real-time measurements updated monthly, with plans to increase frequency to weekly updates. This data was not readily available at the start of the COVID-19 pandemic, requiring researchers to build it from scratch. While companies like Apple and Google initially released similar products, they discontinued them after the pandemic subsided.

“What we’re trying to do is fill that gap and have (the mobility data) always live and ready,” Chinazzi said, preparing for the next flu season or emergent outbreak.

The mobility data measures the distance devices move from a central location, such as a home, throughout the day and during special events. It also tracks contacts, measuring the number, duration, and average duration of close proximity between devices.

Accessible Software for Broader Apply

Epydemix provides accessible epidemic modeling capabilities traditionally limited to specialized research teams, making them available to public health officials and smaller research departments. It’s a “no code” open-source toolkit, originally developed in Python, but accessible through user-friendly dashboards even for those without coding experience.

“Imagine a website where you don’t have to write any code. You just define your model,” Gozzi explained, allowing users to run scenarios using real-world population and epidemiological data.

Scenarios can explore the impact of keeping workplaces or schools open or closed.

Shoba Nair, director of epidemiology and evaluation for the Boston Public Health Commission, whose staff received EPISTORM training on Epydemix, believes the platform will be valuable for forecasting the impact of factors like vaccination levels on infectious disease outbreaks. She added that the commission is looking forward to collaborating with EPISTORM partners to adapt the package for city-level and local use.

Vespignani emphasized that COVID-19 created an urgent need for contact tracing and mobility data. Now, EPISTORM is building infrastructure, providing tools and data, and enhancing the capacity of policymakers.