School of public health

Faculty member appointed to named professorship

In recognition of distinguished scholarship in the area of disease modeling and significant contribution to public health, the Yale Corporation has appointed Alison Galvani, director of the new Center for Infectious Disease Modeling and Analysis at Yale, to the Burnett and Stender Families Professorship in Public Health. Galvani, 38, ranks as one of the leading mathematical biologists in the world and is believed to be the youngest faculty member ever appointed to a named professorship in the history of the Yale School of Public Health. “This chair, the first new chair at YSPH in almost 50 years, will ensure support for important research on infectious diseases in perpetuity,” said Dean Paul Cleary.

YSPH Day of Service

As part of YSPH’s centennial celebration, more than 100 students, faculty, staff, and alumni worked at sites around Connecticut on the Yale Day of Service in early May, helping communities and promoting health and well being. The projects included painting hallways and apartments at the Hillside Family Shelter in New Haven, developing a new garden at New Haven Farms, sorting nonperishable foods and fresh produce at the Connecticut Food Bank warehouse in East Haven, and creating innovative approaches to communicating health risks to the public at the Connecticut State Department of Health in Hartford.

Model predicts best use of antibiotics

A new mathematical model developed by a team of scientists led by the Yale School of Public Health may help predict the optimal dosing of antibiotics.  Antibiotics were introduced more than 70 years ago, but substantial uncertainty remains about how the drugs should be used by patients to ensure recovery while minimizing toxic side effects and the risk of developing antibiotic resistance. The authors hope their new model may eventually help inform the design of more effective antibiotic dosing regimens based on chemical kinetic properties of antibiotics alone. The research appeared in the journal Science Translational Medicine and describes a model that uses information about how antibiotics bind to bacterial target molecules to predict how these drugs will affect individual bacterial cells and populations of bacteria.

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