A multidisciplinary team from Baylor College of Medicine, Rice University and the University of Texas School of Public Health has received the inaugural Climate + Health Excellence award from the Burroughs Wellcome Fund. The five-year, $10 million award will support FORECAST, an ambitious program designed to investigate how climate change, extreme weather and environmental disruption influence the emergence and spread of infectious diseases. Led by infectious disease researcher Dr. Anthony Maresso, the project will combine wastewater surveillance, metagenomic sequencing, climate science, epidemiology and public education to create an early-warning system for pathogen activity across Texas.

FORECAST will build on Texas’s expanding wastewater-monitoring infrastructure, including the Texas Wastewater Environmental Biomonitoring Network, or TexWEB. The statewide network samples sewage from 15 Texas cities and analyzes biological material shed by communities. Unlike clinical surveillance, which depends on people seeking medical care and receiving diagnostic tests, wastewater epidemiology captures population-level signals from entire communities. Viral genetic fragments, bacterial DNA and other biological markers can appear in sewage before hospitals and clinics register a rise in patients, providing public health officials with valuable time to investigate and respond.

The program will use metagenomic surveillance, a technique that examines genetic material recovered from environmental samples rather than searching only for a predetermined pathogen. In conventional testing, laboratories typically use targeted molecular assays designed to detect a known virus or bacterium. Metagenomic sequencing can survey a much broader range of organisms, including variants and potentially unfamiliar pathogens. Researchers can then compare these signals with clinical data, geographic information and environmental measurements to determine whether changes in temperature, rainfall, flooding, drought or other conditions are associated with shifts in infectious disease activity.

The scientific goal is not simply to identify pathogens after they appear, but to uncover relationships between microbes and the environmental conditions that help them enter or circulate within human populations. Climate change can influence disease transmission through several pathways. Warmer temperatures may expand the geographic range or seasonal activity of mosquitoes and other vectors. Heavy rainfall and flooding can overwhelm sanitation systems and redistribute contaminants, while drought can alter water use and concentrate biological material. Changes in human behavior, animal movement and ecosystems may create additional opportunities for pathogens to cross species boundaries.

Maresso and his colleagues demonstrated during the COVID-19 pandemic that SARS-CoV-2 signals in wastewater could precede increases in clinically diagnosed infections. Viral RNA released in feces entered sewage systems, where researchers tracked changes in concentration over time. Although wastewater measurements cannot determine which individuals are infected, they can reveal whether pathogen activity is increasing or declining within a catchment area. The approach has since been extended to a wider range of targets, including avian influenza, measles, HIV and viruses associated with cancer. FORECAST aims to develop this surveillance capacity into a predictive climate-health platform.

Rice University climate scientist Dr. Sylvia Dee will help connect pathogen observations with weather and climate data. The team plans to examine whether specific environmental patterns consistently occur before increases in particular infectious agents. Such models could eventually function like a public health weather report, indicating when conditions are becoming more favorable for disease transmission. The researchers emphasize that predictive systems will require careful validation because the presence of pathogen genetic material does not necessarily indicate infectious virus, active transmission or a future outbreak. Interpreting the signal will require laboratory studies, clinical evidence and knowledge of local infrastructure.

The project will also investigate diseases transmitted by vectors such as mosquitoes. Dr. Peter Hotez and colleagues at the National School of Tropical Medicine will focus on vector-borne pathogen transmission and the growing health risks associated with changing climate conditions. Texas is particularly relevant for this work because it contains diverse ecological regions and experiences heat waves, hurricanes, flooding and rapid urban growth. These factors can affect mosquito habitats, human exposure and the movement of pathogens across communities. Integrating wastewater findings with vector surveillance could help reveal connections that would remain hidden if either system were used alone.

Education and public communication will form a central part of FORECAST. The team plans to create a “Middle to Medical” climate-health education track extending from middle school through medical training and professional development. Dr. Nancy Moreno, who developed Baylor’s BioEd online program, will lead educational efforts with Rice University’s Dr. Joseph Campana, director of the university’s Center for Environmental Studies and EcoStudio. The curriculum will explain how pathogens spread, how climate conditions alter disease risk and how scientific evidence can guide decisions by students, clinicians, policymakers and communities.

The award is particularly significant because FORECAST was selected as the sole funded project from 161 applications submitted to the new CHEX program. The initiative is intended to strengthen research, education and public engagement at the intersection of climate change and human health. Dr. Eric Boerwinkle, dean of the UT School of Public Health and a project co-investigator, will contribute access to large clinical datasets that can be used to evaluate wastewater and environmental signals against reported illnesses. The investigators say the work could improve outbreak preparedness while advancing the still-developing field of wastewater science.

By combining environmental sequencing with climate modeling and health education, FORECAST seeks to shift infectious disease surveillance from a largely reactive system toward earlier detection and prevention. Its findings may help officials recognize emerging threats before they produce widespread clinical disease, while also clarifying how environmental changes shape viral evolution, transmission and geographic spread. The researchers describe wastewater as an unusually candid public health resource: it aggregates biological information from a population without requiring individual testing. As climate pressures intensify, they argue, that collective signal could become an essential tool for protecting communities and preparing the next generation of scientists.

Subject of Research:
Climate-driven infectious disease emergence, wastewater epidemiology, metagenomic pathogen surveillance and climate-health education.

Article Title:
Texas Researchers Launch FORECAST Program to Link Climate Change With Emerging Viral Threats

Web References:
Baylor College of Medicine; Rice University; University of Texas School of Public Health; Burroughs Wellcome Fund; Texas Wastewater Environmental Biomonitoring Network (TexWEB).

References:
Information provided in the source news release concerning the FORECAST project, the Climate + Health Excellence award and TexWEB wastewater surveillance activities.

Keywords:
Wastewater epidemiology, viral surveillance, metagenomic sequencing, climate change, infectious diseases, pathogen emergence, public health, Texas, SARS-CoV-2, avian influenza, measles, vector-borne disease, climate health, epidemiology, wastewater science

PHILADELPHIA — Public resistance to new data centers has risen sharply across the United States, even as Americans’ overall views of artificial intelligence remain largely unchanged, according to a nationally representative survey from the Annenberg Public Policy Center at the University of Pennsylvania. The findings reveal a growing divide between public attitudes toward AI as a technology and attitudes toward the physical infrastructure required to operate it. While many people continue to see potential benefits from AI, particularly in medical research, opposition increases substantially when the technology is associated with a large industrial facility being built near their homes.

The survey found that 61% of U.S. adults somewhat or strongly oppose the construction of new data centers in their communities. That figure is 12 percentage points higher than the 49% recorded in an earlier APPC survey conducted in February and March. Support fell during the same period, from 21% to 14%. The change comes as communities across the country debate the environmental, economic, and social consequences of rapidly expanding computing infrastructure used to train and operate advanced AI systems.

Data centers are specialized facilities filled with servers, networking equipment, cooling systems, backup power supplies, and security infrastructure. AI applications can require enormous amounts of computational power because they process vast datasets and perform calculations across billions of model parameters. Training a large AI model may require thousands of processors operating simultaneously, while everyday use also depends on data centers to generate responses, analyze images, translate languages, and carry out other tasks. These facilities can consume substantial amounts of electricity and water, making their construction a highly visible local issue even when the software they support is used remotely.

Opposition to local data centers crosses political boundaries. Majorities of Democrats, Republicans, and independents said they opposed new facilities in their areas, although the proportions differed: 69% of Democrats, 54% of Republicans, and 53% of independents expressed opposition. Resistance was strongest among adults younger than 30, reaching 70%, and declined to 57% among people 65 and older. The pattern is notable because younger adults are often assumed to be more comfortable with emerging technologies and are more likely to use AI-powered services in education, work, entertainment, and communication.

Despite the shift in views about local infrastructure, broader opinions about AI have remained stable. Thirty-nine percent of respondents said they expected AI’s impact on the United States over the next decade to be somewhat or very negative, compared with 18% who expected a positive impact. The result was statistically unchanged from the spring survey. In addition, 68% said the federal government has done “too little” to regulate AI, a view shared by majorities of Democrats, Republicans, and independents. The results suggest that concerns about AI regulation have become persistent rather than being driven solely by short-term news events.

Respondents distinguished sharply between areas where AI might produce public benefits and areas where it could create risks. Among 13 sectors examined, medical research and scientific discovery was the only category receiving a net-positive assessment, with anticipated benefits exceeding expected harms by 41 percentage points. AI systems are increasingly being used to identify patterns in biological data, predict molecular structures, assist with medical imaging, and help researchers screen potential treatments. These applications may accelerate work that would otherwise require years of laboratory experiments, although they still depend on human validation and carefully controlled clinical studies.

The most negative assessments involved personal privacy and data security, which registered a net score of minus 63 points. Children’s online safety followed at minus 50 points, while employment and jobs reached minus 46 points. These concerns reflect the technical and social consequences of deploying AI at scale. Models may be trained on or exposed to sensitive information, automated systems can make decisions that are difficult to explain, and workplace applications may alter or eliminate certain tasks. The survey does not measure whether these risks will materialize in specific industries, but it indicates that many Americans view them as more immediate than AI’s possible benefits outside medicine.

Personal experience with AI was associated with more favorable opinions about its national impact. Among respondents who said they had never used AI during the previous month, 54% expected its effect on the country to be negative. That proportion fell to 40% among light users and to 29% among those who used AI many times or almost every day. However, frequent use did not make people more supportive of data centers. Opposition to building new facilities near respondents’ homes remained essentially flat across different levels of AI use. This distinction suggests that familiarity may influence perceptions of digital tools, while concerns about electricity demand, water consumption, noise, traffic, land use, and local utility costs may remain regardless of personal experience with AI software.

“The people who use AI the most are the most optimistic about what it will do for the country, and that has been consistent across our surveys,” said Matt Levendusky, a University of Pennsylvania political science professor and the Stephen and Mary Baran Chair in the Institutions of Democracy at APPC. He said that optimism had clear limits, particularly when questions involved privacy, employment, or the construction of a data center nearby. The survey was conducted by SSRS for APPC’s Institutions of Democracy division among 1,320 U.S. adult citizens from June 16 through July 19, 2026. Respondents were surveyed primarily online, with a small telephone sample, and weighted to match population benchmarks. The margin of error for the full sample was plus or minus 3.5 percentage points, with larger margins for subgroups. Together, the findings indicate that the next phase of the AI debate may be shaped less by abstract promises about intelligent software than by negotiations over the highly physical systems that make it possible.

Subject of Research: People

Article Title: Opposition to Local Data Centers Rises Sharply as Views on AI Hold Steady

Web References: Annenberg Public Policy Center news release; Reuters; The New York Times; Associated Press

References: Annenberg Public Policy Center Institutions of Democracy national survey, June 16–July 19, 2026; APPC AI topline survey data; APPC survey methodology

Image Credits: Annenberg Public Policy Center

Keywords: Artificial intelligence, data centers, public opinion, AI regulation, medical research, privacy, data security, employment, social surveys, political science