While large volumes of epidemiological surveillance data are collected by Canadian public health authorities and researchers, such data suffers from some shortcomings. Collection of most information relies on individual self-reporting, which can be notoriously unreliable. Physically measured information (such as that collected by the Canadian Health Measures Survey, NHANES III and some other surveys) is traditionally costly and burdensome to acquire.
The rise of sensor-bearing smartphones offers the potential for enabling data collection from ubiquitous epidemiological sensing applications downloaded and run by volunteers. We have built and deployed a Google Android-based smartphine application -- iEpi -- that collects and wirelessly reports on a variety of types of epidemiologically relevant information. iEpi is our second generation system, and builds on research insights and lessons learned from of our first-generation system: The more restrictive FluNet wireless mote-based system, deployed for 3 months during pandemic influenza season (2009). iEpi is attractive in that it can operate in the background on commodity smartphones -- opening up the possibility for third-party phone users to eventually opt into research studies, and incentivizing study participants to keep an iEpi smartphone nearby and charged.
iEpi collects, encrypts and opportunistically wirelessly reports on a variety of sensor modalities, including physical proximity (estimated via Bluetooth RSSI levels), physical activity levels (via 3-axis accelerometers), location (via GPS) and responses to (optionally context-triggered) prompted surveys (which can contain conditional questions, looping constructs involving one or more questions, and parameters). This system offers a simple query mechanism for data collection requests, including requests contingent on other events or history. We have recently extended iEpi to further collect information from third-party bluetooth device (a weight scale); further such extensions are anticipated in the future.
We are working to help ensure that our iEpi system will confer benefits not only to health administrators (e.g. for improved nosocomial infection control) and health researchers (as a new source of rich data on relationships between human behaviour and health), but also for researchers in areas such as delay tolerant networks.
We see our iEpi system as a natural complement to our computational models; the two work together to yield very powerful decision-making tools. Data from iEpi helps to ground our models with a profusion (~1.5M records per participant per month) of detailed, longitudinal data at the individual level. Various types of modeling we apply -- agent-based and aggregate simulation models, as well as inferential and statistical models -- help to "make sense" of this data, and to relate it to the choices that need to be made.
For further information, please see the following streaming videos:
My presentation from the Institute for Systems Science and Health 2011 demonstrates how we can leaverage such data using 3 systems science modeling techniques.
Presentation delivered at the 2012 Annual Meeting for the Society for Epidemiological Research focuses on how sensing can inform the design of rich simulation models, but also comments on the synergy between sensing and dynamic models.
Some example images produced from iEpi data are shown below.
Within the diagram below, nodes are wifi locations. Two wifi nodes are considered connected if at least one participant detected them in the same 5-minute timeslot with a requisite signal strength. The nodes are shown with independent horizontal and vertical spans. The length of the horizontal axis for a node varies in proportion to the density of nonparticipants (people per unit time) detected at that node. The length of the vertical axis of a node is proportional to the density of participants detected at that node.
The following diagram depicts likelihood of infection in different locations in Saskatoon, as judged by a transmission model for a hypothetical influenza-like illness.
In the diagram below, a rough proxy for physical activity (based on accelerometer readings from participants' cellphones) were used to estimate levels of physical acitivity observed throughout Saskatoon over a one-month period.
The below depicts wifi locations by non-participant time density (size) and count of distinct non-participants seen at location (brightness). Lines show association between a participant and particular locations at which they were present.
 and count of distinct non-participants seen at location (brightness).png)
Wifi locations here are shown as circles, each with area proportional to non-participant density. Brightness indicates count of discint non-participants seen at a location. Participants are shown in red, with connections to the locations with which they were associated.
