The year 2030 serves as the resolution to the United Nation’s Agenda for Sustainable Development. The agenda, adopted in 2015 by all UN member states including the United States, mobilizes global efforts to protect the planet, end poverty, foster peace, and safeguard the rights of all people. Nine years out from the target date, the sustainable development goals of the agenda still remain ambitious, and as relevant as ever.

MIT Lincoln Laboratory has been growing its efforts to provide technology solutions in support of such goals. “We need to discuss innovative ways that advanced technology can address some of these most pressing humanitarian, climate, and health challenges,” says Jon Pitts, who leads Lincoln Laboratory’s Humanitarian Assistance and Disaster Relief Systems Group.

To help foster these discussions, Pitts and Mischa Shattuck, who serves as the senior humanitarian advisor at Lincoln Laboratory, recently launched a new lecture series, called the Future of Humanitarian Technology.

In the inaugural session on April 28, Lincoln Laboratory researchers presented three topics inherently linked to each other — those of climate change, disaster response, and global health. The webinar was free and open to the public.

Accelerating sustainable technology

Deb Campbell, a senior staff member in the HADR Systems Group, started the session with a discussion of how to accelerate the national and global response to climate change.

“Because the timeline is so short and challenges so complex, it is essential to make good, evidence-based decisions on how to get to where we need to go,” she said. “We call this approach systems analysis and architecture, and by taking this approach we can create a national climate change resilience roadmap.”

This roadmap implements more of what we already know how to do, for example utilizing wind and solar energy, and identifies gaps where research and development are needed to reach specific goals. One example is the transition to a fully zero-emission vehicle (ZEV) fleet in the United States in the coming decades; California has already directed that all of the state’s new car sales be ZEV by 2035. Systems analysis indicates that achieving this “fleet turnover” will require improved electric grid infrastructure, more charging stations, batteries with higher capacity and faster charging, and greener fuels as the transition is made from combustion engines.

Campbell also stressed the importance of using regional proving grounds to accelerate the transition of new technologies across the country and globe. These proving grounds refer to areas where climate-related prototypes can be evaluated under the pressures of real-world conditions. For example, the Northeast has older, stressed energy infrastructure that needs upgrading to meet future demand, and is the most natural place to begin implementing and testing new systems. The Southwest, which faces water shortages, can test technologies for even more efficient use of water resources and ways to harvest water from air. Today, Campbell and her team are conducting a study to investigate a regional proving ground concept in Massachusetts.

“We will need to continuously asses technology development and drive investments to meet these aggressive timelines,” Campbell added.

Improving disaster response

The United States experiences more natural disasters than any other country in the world and has spent $800 billion in last 10 years on recovery, which on average takes seven years.

“At the core of disaster support is information,” said Chad Council, also a researcher in the HADR Systems Group. “Knowing where impacts are and the severity of those impact drives decisions on the quantity and type of support. This can lay the ground work for a successful recovery … We know that the current approach is too slow and costly for years to come.”

By 2030, Council contends that the government could save lives and reduce costs by leveraging a national remote sensing platform for disaster response. It would use an open architecture that integrates advanced sensor data, field data, modeling, and analytics driven by artificial intelligence to deliver critical information in a standard way to emergency managers across the country. This platform could allow for highly accurate virtual site inspections, wide area search-and-rescue, determination of road damage at city-wide scales, and debris quantifications.

“To be clear, there’s no one-size-fits-all sensor platform. Some systems are good for a large-scale disaster, but for a small disaster, it might be faster for local transportation department to fly a small drone to image damage,” Council said. “The key is if this national platform is developed to produce the same data as local governments are used to, then this platform will be familiar and trustworthy when that level of disaster response is needed.”

Over the next two years, the team plans to continue to work with the Federal Emergency Management Agency, the U.S. National Guard, national laboratories, and academia on this open architecture. In parallel, a prototype remote sensing asset will be shared across state and local governments to gain enthusiasm and trust. According to Council, a national remote sensing strategy for disaster response could be employed by the end of 2029.

Predicting disease outbreaks

Kajal Claypool, a senior staff member in the Biological and Chemical Technologies Group, concluded with a discussion on using artificial intelligence to predict and mitigate the spread of disease.

She asks us to fast-forward nine years, and imagine we have convergence of three global health disasters: a new variant of Covid-30 spreading across globe, vector-borne diseases spreading in central and south America, and the first carrier with Ebola has flown into Atlanta. “Well, what if we were able to bring together data from existing surveillance systems, social media, environmental conditions, weather, political unrest, and migration, and use AI analytics to predict an outbreak down to a geolocation, and that first carrier never gets on the airplane?” she asked. “None of these are a far stretch.”

Artificial intelligence has been used to tackle some of these ideas, but the solutions are one-offs and siloed, Claypool said. One of the greatest impediments to using AI tools to solve global health challenges is harmonizing data, the process of bringing together data of varying semantics and file formats and transforming it into one cohesive dataset.

“We believe the right solution is to build a federated, open, and secure data platform where data can be shared across stakeholders and nations without loss of control at the nation, state, or stakeholder level,” Claypool said. “These siloes must be broken down and capabilities available for low- and middle-income nations.”

Over next few years, the laboratory team aims to develop this global health AI platform, building it one disease and one region as a time. The proof of concept will start with malaria, which kills 1.2 million people annually. While there are a number of interventions available today to fight malaria outbreaks, including vaccines, Claypool said that the prediction of hot spots and the decision support needed to intervene is essential. The next major milestone would be to provide data-driven diagnostics and interventions across the globe for other disease conditions.

“It’s an ambitious but achievable vision. It needs the right partnerships, trust, and vision to make this a reality, and reduce transmission of disease and save lives globally,” she said.

Addressing humanitarian challenges is a growing R&D focus at Lincoln Laboratory. Last fall, the organization established a new research division, Biotechnology and Human Systems, to further explore global issues around climate change, health, and humanitarian assistance. 

“Our goal is to build collaboration and communication with a broader community around all of these topics. They are all terribly important and complex and require significant global effort to make a difference,” Pitts says.

The next event in this series will take place in September.