Forecasting

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The CDC’s ILINet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. However, ILINet indicators are typically reported at a lag of 1-2 weeks. Another source of severity data, Google Flu Trends, is calculated by aggregating Google searches for certain influenza related terms. Google Flu Trends data is provided in near-real time, but is a less direct measurement of severity than ILINet indicators, and is likely to suffer from bias. We create a hierarchical model to estimate epidemic severity for the 2014 - 2015 epidemic season which incorporates current and historical data from both ILINet and Google Flu Trends, allowing our model to benefit both from the recency of Google Flu Trends data and the accuracy of ILINet data.

Objective

To use multiple data sources of influenza epidemic severity to inform a model which can estimate and forecast severity for the current influenza epidemic season by accounting for the bias from each source.

Unhealthy diet is becoming the most important preventable cause of chronic disease burden. Dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment. Assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality. Mass administration of dietary surveys is impractical and prohibitory expensive, and surveys typically fail to address variation of food selection at high geographic resolution. Marketing companies such as the Nielsen cooperation continuously collect and centralize scanned grocery transaction records from a geographically representative sample of retail food outlets to guide product promotions. These data can be harnessed to develop a model for the demand of specific foods using store and neighborhood attributes, providing a rich and detailed picture of the “foodscape” in an urban environment. In this study, we generated a spatial profile of food selection from estimated sales in food outlets in the Census Metropolitan Area (CMA) of Montreal, Canada, using regular carbonated soft drinks (i.e. non-diet soda) as an initial example.

Objective

To demonstrate a method for estimating neighborhood food selection with secondary use of digital marketing data; grocery transaction records and retail business registry.