"Smartphones are amazing devices," Osgood said. "The sensors can track information like where you go for a morning walk, which grocery store or pharmacy you shop at and information on contact patterns. WiFi, Bluetooth, accelerometer and GPS can be used to collect this information through iEpi. It also has a triggered survey component to determine situational context."
The information can then be fed into simulation models that can predict, for example, how a flu like H1N1 would move though a population like the Department of Computer Science - the pilot community Osgood and Stanley tested - or Saskatoon.
"We run groundhog day simulations hundreds of thousands of times based on a set of disease parameters and a set of personal contacts to come up with a probability of someone getting sick," explained Stanley. "We test health policies, like staying at home when sick or getting vaccinations, to test if it makes a difference."
This critical behavioral information has been left out of other models as it has been too unreliable and expensive. iEpi can help change this, capturing so much data that it becomes possible to predict how a disease might spread based on location, number of people who come in contact and other factors.
"There's nothing like this (iEpi) as far as we are aware. All this data makes the models more reliable and robust," said Osgood.
Researchers at Columbia University have already used iEpi to track the effects of moving from low-income housing to mixed-income housing. They wanted to understand results from a previous study that showed young women experienced reduced obesity, improved graduation rates and lower crime, whereas young men had slightly higher crime involvement.
"We can use this to figure out why," said Osgood. "Is it because young women got more exercise because it was safer or had access to parks and better food options? Did the young men go back to their old neighbourhoods?"
Closer to home, Osgood and Stanley are working Drs. Jill Newstead-Angel and Roland Dyck from the College of Medicine on gestational diabetes outcomes in Saskatchewan.
"Our modeling suggests that gestational diabetes may be responsible for a large number of diabetes cases in Saskatchewan," Osgood said. "We are looking at how the burden of diabetes is shaped by a number of risk factors with an eye to prevention and control strategies. This is aimed at informing policies and what's most effective; what's the best way to prevent disease."
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University of Saskatchewan