The tremendous volume of data being collected from social media, environmental sensors, lab tests, smartphones and browsing patterns is providing vital insights into real-life human behaviour. And it’s transforming the modelling of public health interventions, according to internationally respected health data science expert Professor Nate Osgood.
Professor Osgood, from the University of Saskatchewan in Canada, told a packed seminar in Sydney co-hosted by the Sax Institute and the Australian Prevention Partnership Centre, that dynamic simulation modelling could make sense of the cacophony of data and assess the implications of big data for policy.
Dynamic simulation models are computer representations of the real world that enable researchers to test policy interventions. Incorporating information derived from big data enables researchers to take into account how real human behaviour plays into the complex interactions that influence health, he said.
A science of the whole
“We are facing policy challenges which involve complex systems and often cannot be simply reduced to individual components – in the real world there are multiple levels of context,” he told the seminar, Integrating Big Data and dynamic models to support health decision making, held at the University of Sydney.
“What’s needed is a science of the whole. Simulation modelling enables us to understand all the pieces, their interactions and how behaviour of the whole comes out of these connected parts.”
A key program of work at the Prevention Centre, simulation modelling provides a ‘what-if’ tool to test the outcomes of different policy scenarios.
Used in engineering, defence and business for decades, simulation models map complex problems by bringing together a variety of evidence sources, such as research, datasets, expert knowledge, practice experience and administrative data.
A key to human behaviour
Adding big data to the mix was key to understanding human behaviour, Professor Osgood said. It offered large volume, high velocity, highly varied and more accurate information than was currently possible through the literature or self-reporting.
For example, self-reported physical activity was often vastly different to information gained when people’s movements were tracked through GPS or fitbit data, while self-reported dietary intake could vary greatly from what was indicated via physical measurements such as photos or glucose monitoring via a smartphone.
Filling the gaps
“Big data can fill in the gaps in our simulation models,” he said. “Putting big data and models together allows us to much more reliably learn from our interventions, what worked and what didn’t work, and gives us a clue as to how we might do it better next time.”
He quoted a project in Canada, Ethica iEpi, which used big data to fill gaps in a simulation model developed to combat foodborne diseases. Data collected from a smartphone app was used to understand when and where people ate from food vendors, those who developed stomach upsets that both did and did not require medical attention, and how long they stayed sick.
The project found that gleaning this information from just 4% of smartphone users as sentinels could reduce the amount of food-borne illness by 60%, by enabling contaminated restaurants to be identified more quickly.
“With more nimble adversarial corporate actors who are running circles around health authorities by tapping into social networking and the latest technologies, this is urgently needed,” he said.
“It is critical to understand how the system behaves to be able to develop reliable interventions and policy regimes that will yield long-term gains – fixes that stay fixed.”
Big data sources revolutionising dynamic simulation modelling
- Twitter (feeds)
- Facebook (status updates)
- Environmental sensors (weather, municipal, building)
- Lab test results
- Point of sale records
- Administrative data
- Questionnaire responses (mobile, web)
- Voice audio
- Incoming/outgoing calls
- Communication infrastructure proximity data
- Health information browsing behaviour
- Sequence data
- Consumer electronic devices sensors (physical activity, proximity, location, etc)
The Sax Institute and The Australian Prevention Partnership Centre were delighted to host international health data science expert Professor Nate Osgood at a seminar that attracted 170 participants in Sydney in May 2016.
Professor Osgood, from the University of Saskatchewan in Canada, told the audience that the tremendous volume of data being collected from social media, environmental sensors, lab tests, smartphones and browsing patterns is providing vital insights into real-life human behaviour — and transforming the modelling of public health interventions.
He said dynamic simulation modelling could make sense of the cacophony of data and assess the implications of big data for policy.
Find out more
- Read about dynamic simulation modelling at the Prevention Centre