Mathematics might save you a trip to the ER
Since the time of Hippocrates, it’s been known that certain illnesses come and go with the seasons. More recently, scientists have learned that these cyclic recurrences of disease, known as seasonality, are often related to the weather.
Now researchers at Tufts School of Medicine are developing mathematical models based on environmental factors such as temperature to predict when an outbreak of disease will occur and how many people will get sick.
“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,” said Elena Naumova, associate professor of public health and family medicine, who is leading the research. “But having a more accurate and reliable method of disease surveillance is crucial to forecasting outbreaks in order to implement warning systems,” she said.
Naumova and her colleagues tested their mathematical models with data gathered by the Massachusetts Department of Public Health on six intestinal diseases: giardiasis, cryptosporidiosis, salmonellosis, campylobacteriosis, shigellosis and hepatitis A. They all cause nausea, diarrhea, abdominal cramping and often fever.
While previous studies on seasonality have used monthly or quarterly data, the Tufts researchers used daily data, enabling them 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 would save time, health-care costs, and most importantly, may save many people a trip to the hospital,” Naumova said.
Using a decade of data (from 1992 to 2001), the researchers analyzed the timing, duration and magnitude of each of the six diseases and compared these values to the corresponding average daily outdoor temperature in Massachusetts. Both salmonellosis and cryptosporidiosis peaked at the end of July, the hottest time of year in Massachusetts. However, outbreaks of giardiasis, shigellosis and cryptosporidiosis spiked one month after that temperature peak. There was no observable trend for hepatitis A.
“Several factors may explain the one-month delay of giardiasis, shigellosis and cryptosporidiosis,” Naumova said, “including different routes of transmission of each pathogen, greater spread of a disease due to close person-to-person contact and different symptoms among patients. More than likely, it is a combination of factors.”
Naumova also noted that the second peak in disease may be linked to recreational water use. “By August in Massachusetts, recreational water sources are at their warmest, having been heated all summer long. This higher water temperature, combined with close person-to-person contact, may be the reason for the second peak of outbreaks observed with these three pathogens.”
Disease surveillance and alert systems are crucial to preventing the spread of disease, Naumova said. “At both the global and community level, public health officials are working with epidemiologists to develop standardized alert and response systems at the first signs of an outbreak,” she said. “It is our hope that this mathematical model, based on daily data, will contribute a degree of accuracy in the field of outbreak forecasting and disease surveillance.”
The research, which was funded by the U.S. Environmental Protection Agency and the National Institute of Allergy and Infectious Diseases, was published in the journal Epidemiology and Infection.
Naumova, a biostatistician, is director of the Tufts Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID), a group that is working to improve biomedical research by developing innovative computational tools to assist life science researchers, public health professionals and policy makers.
This story appeared in the November 2007 issue of the Tufts Journal.