Featured Post from University of Delaware

Understanding waterfowl movement and bird flu

While poultry houses are at risk, those encountering geese in neighborhoods don't need to be too concerned for themselves or their dogs, according to a University of Delaware expert

Mar 13, 2025

2 min

Scores of waterfowl are currently on the move, and experts are worried they could further spread highly pathogenic avian influenza, commonly known as bird flu. But these flocks, which can number in the thousands and include Canada geese and snow geese, are of most concern around poultry houses, said University of Delaware’s Christopher Williams. The risk to humans and dogs getting bird flu from geese who inhabit their neighborhoods remains low, Williams said.

Williams, a professor wildlife ecology in UD’s College of Agriculture and Natural Resources, explained why this is happening:

• Smaller ducks such as blue-winged teal, larger ducks such as mallards, and larger geese like Canada geese and snow geese are heading north to their breeding grounds.

• Waterfowl are a primary reservoir for avian influenza viruses, meaning they can harbor bird flu strains viruses, spread them, and in some cases show no signs of disease. They can shed the virus through their saliva, naval secretions, and feces.

• Waterfowl serve as the primary vector of avian influenza to the U.S. poultry industry which has affected more than 8 million commercial and backyard chickens and turkeys that have been culled to keep the virus from spreading.

“While we are worried about these flocks congregating near poultry houses, the risk of humans getting bird flu from waterfowl remains low,” Williams said. “Further, walking dogs around areas with geese flocks should pose little threat to dogs’ health as avian influenza is very rare in dogs.”

Nevertheless, Williams cautions people and dogs to avoid touching sick birds or dead birds.

A large segment of the Canada geese population are not migratory, Williams said. They will stick around the Mid-Atlantic year-round and tend to congregate and breed near grocery store parking lots, golf courses and neighborhoods.

“Those birds are being drawn to needing two things. No. 1 is they need water including any natural pond or water containment area,” Williams said. “And all the geese species are herbivores, so No. 2 they eat grass. There are places that have both water and grass that will provide ideal nesting habitat for the geese and their chicks.”

Reporters can connect with Williams by visiting his profile and clicking on the “contact” button.

Connect with:

Chris Williams

Professor, Wildlife Ecology

Prof. Williams serves as Director of the Waterfowl and Upland Gamebird Center.

ZoologyGame Bird EcologyWildlife EcologyWaterfowl EcologyBird Populations and Habitats

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Decoding epilepsy, one brainwave at a time

Epilepsy isn’t always easy to diagnose. Seizures often don't occur during routine brain-wave recordings, leaving doctors without the direct observation they need to make a clear diagnosis. In a proof-of-concept study in mice, University of Delaware researchers and collaborators showed that using artificial intelligence to detect early warning signs hidden in the brain's electrical rhythms can identify subtle EEG differences linked to a genetic form of epilepsy, even when no visible seizures occurred. The findings, published in the Journal of Neural Engineering, set the stage for the next phase of the research, which will test the method on EEGs from children being evaluated for epilepsy at Nemours Children's Health. A dictionary of brain waves Neurologists often use EEGs to help diagnose epilepsy, but routine recordings offer only about a 20-minute snapshot of brain activity. Without a seizure captured during that window, clinicians must look for far subtler clues that can be difficult to detect visually. That's where AI comes in. “Our machine-learning approach lets the algorithm learn the brain’s ‘language’ of waveforms, spotting subtle patterns humans might miss during manual review,” said Austin Brockmeier, assistant professor in electrical and computer engineering and computer and information sciences. Starting small with a mouse model When Brockmeier presented his computational neuroscience research at a seminar, he caught the attention of Amanda Hernan, an affiliated associate professor of psychological and brain sciences and biomedical engineering at UD and senior research scientist at Nemours Children’s Health. Hernan studies how variations in brain activity affect thinking and learning in children with epilepsy. The two decided to put machine learning to the test using EEGs from mice with epilepsy-causing variations in the TSC1 gene. The researchers used a panel of more than 40 mice, including animals with and without the gene variation, across three different genetic backgrounds, or strains. They extracted EEG segments from five days of recordings from each mouse for analysis. Because the EEG segments contained no seizure activity, the algorithm had to detect differences in the brain's baseline activity alone. It was able to distinguish between the mouse strains and to detect the TSC1 gene variation with high accuracy in two of the three strains. “These results show that EEG patterns contain measurable signals of neurological differences, even without visible seizures,” Hernan said. Taking it to the clinic Now, Brockmeier and Hernan will next apply their approach to EEG recordings from children being evaluated for epilepsy at Nemours Children's Health. Pediatric EEGs are shorter than the multi-day recordings used in the mouse study, and children present with many different types of epilepsy. But the researchers are optimistic. “The goal is to identify biomarkers that flag underlying changes in the brain’s electrical activity before seizures occur,” Hernan said. Earlier detection could lead to earlier treatment and less uncertainty for families. That uncertainty, Hernan said, takes a toll. “Seizures follow natural cycles, but without a way to know where you are in that cycle, the anticipation can be incredibly anxiety-provoking,” she explained. Better pattern recognition could also improve treatment decisions. For example, if a new medication is introduced during a natural lull in seizure activity, its benefits could be overestimated. Looking further ahead, the researchers envision a future where wearable EEG devices allow continuous, real-time monitoring for those with high risk of seizures. Similar approaches could eventually be applied to other neurological conditions, including autism and ADHD. "This is a step toward precision medicine," Brockmeier said. "Brain-wave typing could help identify which interventions will work best for a given patient." For families navigating the daily uncertainty of epilepsy, that kind of precision could make a huge difference. To speak with Brockmeier and Hernan, please reach out to mediarelations@udel.edu.

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