Machine Learning Can Predict the Weather — and Human Health

In 2023, with a “tripledemic” of COVID-19, RSV, and flu looming and wildfires threatening to irritate more people’s lungs with particulate matter, hospital leaders across the country braced for a surge of patients with respiratory illnesses. They wondered when cases would peak and whether they would have enough beds to accommodate those in need.

John Brownstein, an HMS professor of pediatrics and chief innovation officer at Boston Children’s Hospital, and colleagues wanted to do better than wonder. They took all the relevant data they could gather — environmental, behavioral, infectious disease — and fed them into a computer model they developed that included a machine-learning algorithm. The result: a detailed forecast for when to expect young patients in the region to flood in with airway issues.

“We could predict to the day when the highest-level capacity needs would be,” and when demand would ebb, says Brownstein, who is also senior vice president of the hospital.

Forecasting health care needs

Machine learning and other forms of artificial intelligence have begun to play a role in protecting well-being on our warming planet by augmenting climate models, deepening understanding of how climate change affects human health, and improving health care systems’ ability to respond effectively. It makes sense: Climate science involves crunching huge amounts of data, and AI excels at interpreting and making predictions from vast, disparate, and incomplete information.

“By helping us pull together huge amounts of noisy and imperfect data with numerous variables, AI can play a substantial role in uncovering and projecting the health impacts of climate change,” says Brownstein.

Generative AI also offers unique opportunities in climate research to extrapolate from heterogeneous data sources, says Francesca Dominici, the Clarence James Gamble Professor of Biostatistics, Population, and Data Science at the Harvard T. H. Chan School of Public Health and director of the Harvard Data Science Initiative.

Some researchers are exploiting AI’s strengths to improve models of climate change and the extreme weather events it drives. The AI model GraphCast by Google DeepMind now delivers more accurate hurricane track predictions and ten-day weather forecasts than traditional models based on mathematical equations of atmospheric and hydrologic physics, which run on supercomputers. Microsoft’s AI model Aurora can calculate global air pollution patterns an unprecedented five days ahead, empowering clinicians and patients to prepare for health consequences. However, it’s harder to validate predictions that extend decades into the future. To rein in potentially outlandish results, scientists are exploring hybrid climate models that incorporate AI components into grounded, physics-based ones.

Other researchers are applying AI to identify and predict climate-related impacts on health. Rather than asking questions piecemeal, such as how heat affects stroke risk, AI can unearth relationships between multiple diseases and environmental factors simultaneously. AI tools helped Brownstein and colleagues reveal in 2018 that rising local temperatures contribute to antibiotic resistance. Other AI tools have facilitated his group’s work by using unconventional data sources such as social media posts to track infectious disease spread in real time.

Efforts in the field include identifying the populations whose health is most at risk from particular aspects of climate change. The results can inform prevention and preparedness. “Very sophisticated algorithms can be trained on massive amounts of data from electronic health records, insurance claims, doctors’ notes, and research on climate stressors to tell you who is more likely to show up at the hospital for what disease a day, a week, or a month after a heat wave,” says Dominici.

Scientists are still in the early stages of exploring AI’s potential to illuminate the connections between climate and health. The authors of a review published in 2024 in PLOS Climate, including two HMS faculty members at Beth Israel Deaconess Medical Center, found only seven English-language studies that used machine learning to predict the health outcomes of climate-driven events.

AI could enrich health care systems’ climate resilience by, for instance, making data more accessible. Satchit Balsari, an HMS associate professor of emergency medicine at Beth Israel Deaconess, co-launched Climateverse in 2023 to integrate and annotate siloed information on climate and health in Southeast Asia. An AI chatbot helps researchers interact with the data and gain insights, such as which communities need the most help withstanding extreme weather events.

Another avenue looks to AI for ideas on decarbonizing health care and other sectors, says Dominici — for example, dynamically optimizing electrical grids and identifying which efforts to lower carbon dioxide emissions work best. Similarly, AI could help clinicians and policymakers analyze which health care interventions work best to protect against climate threats. When a heat wave looms, she says, models could synthesize outcomes from across the country to gauge whether leaders in a specific location should issue a heat warning, open more cooling centers, or send air conditioners to elderly residents.

A mix of sun and clouds

There’s some irony in asking AI how to reduce emissions, since the technologies themselves consume significant electricity, which can contribute to climate change. HMS community members working on environmental sustainability are considering the energy required to run AI systems. While AI models designed to replace traditional ones can save electricity by running faster and on less power-hungry computers, the overall surge in AI use may outweigh any energy gains.

Such considerations factor into larger calls for responsible use of AI as the field hurtles forward. Models can produce unreliable or biased results in climate-related work just as they can when proposing a medical diagnosis or treatment. Leaders at HMS and beyond are advocating for openness and caution in climate AI to ensure that predictions are as accurate as possible, that outputs reflect the populations they’re being applied to, and that people don’t place unearned trust in algorithms.

“This balance of harnessing the good and mitigating the bad of AI is really important for us to embody at Harvard and in medicine when we’re dealing with human lives,” says Dominici.
 

Stephanie Dutchen is editorial director in the HMS Office of Communications and External Relations.