BOSTON Since the days of Hippocrates, people have known that certain illnesses come and go with the seasons. More recently, researchers have learned that these cyclic recurrences of disease, known as seasonality, are often related to the weather. In order to accurately predict when outbreaks of disease will occur, and how many people will be effected, Elena Naumova, PhD, associate professor in the Department of Public Heath and Family Medicine at Tufts University School of Medicine in Boston, and colleagues, are studying seasonality by creating mathematical models based on environmental factors like outdoor temperature.
Until recently, public health workers and epidemiologists have eyeballed outbreak cycles relative to the weather in order to estimate when the next outbreak will strike a population, explains Naumova. But having a more accurate and reliable method of disease surveillance is crucial to forecasting outbreaks in order to implement warning systems, says Naumova. She and colleagues have developed mathematical models that will more accurately assess seasonality in an effort to better predict when an outbreak will peak and how many people may fall ill.
Naumova and colleagues tested their mathematical models with data gathered from the Massachusetts Department of Public Health on six diseases: giardiasis, cryptosporidiosis, salmonellosis, campylobacteriosis, shigellosis and hepatitis A, all characterized by nausea, diarrhea, abdominal cramping and often fever. Whereas many previous epidemiological studies investigating seasonality have used monthly data or quarterly data, Naumova and colleagues used daily data, enabling the researchers to detect more subtle changes in disease patterns that may have been previously overlooked.
With more than 1,000 cases of salmonellosis alone each year in Massachusetts, awareness of these subtle changes is crucial because if the public can be alerted to an outbreak even a few days earlier, it
|Contact: Siobhan Gallagher|
Tufts University, Health Sciences