Digital Epidemiology: designing machine learning approaches to combine Internet-based data sources to monitor and forecast disease activity in multiple locations and spatial resolutions

Presented May 24, 2018.

Mauricio Santillana, MS, PhD describes machine learning methodologies that leverage Internet-based information from search engines, twitter microblogs, crowd-sourced disease surveillance systems, electronic medical records, and historical synchronicities in disease activity across spatial regions, to successfully monitor and forecast disease outbreaks in multiple locations around the globe in near real-time.

Presenter

May 24, 2018

Using Google Dengue Trends to Estimate Climate Effects in Mexico

The incidence of dengue fever (DF) has increased 30 fold between 1960 and 2010. The literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of DF outbreaks. We use real-time data from GDT and real-time temperature estimates from NASA Earth observing systems to examine the relationship between dengue and climate in 17 Mexican states from 2003–2011. For the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue.

 

July 13, 2018

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