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

Virtual Speed Networking with the Analytic Solutions Committee (ASC)

Presented January 11, 2018.

The purpose of the event was to stimulate and facilitate constructive communication and collaboration among analytic method developers and practitioners charged with routine public health surveillance, ranging from disease outbreak surveillance to chronic disease burden assessment and disaster response.

January 11, 2018

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

An estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3.

January 19, 2018

Opioid Surveillance using Social Media: How URLs are shared among Reddit members

Nearly 100 people per day die from opioid overdose in the United States. Further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths. However, with increasing use of social media comes increasing opportunity to seek and share information. For instance, 80% of Internet users obtain health information online, including popular social interaction sites like Reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153.

January 21, 2018

Leveraging Discussions on Reddit for Disease Surveillance

In recent years, individuals have been using social network sites like Facebook, Twitter, and Reddit to discuss health-related topics. These social media platforms consequently became new avenues for research and applications for researchers, for instance disease surveillance. Reddit, in particular, can potentially provide more in-depth contextual insights compared to Twitter, and Reddit members discuss potentially more diverse topics than Facebook members. However, identifying relevant discussions remains a challenge in large datasets like Reddit.

January 21, 2018

Correlation of Tweets Mentioning Influenza Illness and Traditional Surveillance Data

The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source.

January 21, 2018

Now Trending in Your Community: Social Media Insights For Your Public Health Mission

In today’s fast paced world, information is available (and expected) instantaneously. Social media has only fueled this expectation as it has permeated all aspects of our lives. More and more of the population is turning to social media outlets to share their thoughts and update their status, especially during disasters. With all these conversations occurring, it is only reasonable to assume that health status is part of the information being shared.

November 22, 2017

Semantic Analysis of Open Source Data for Syndromic Surveillance

Social media messages are often short, informal, and ungrammatical. They frequently involve text, images, audio, or video, which makes the identification of useful information difficult. This complexity reduces the efficacy of standard information extraction techniques1. However, recent advances in NLP, especially methods tailored to social media2, have shown promise in improving real-time PH surveillance and emergency response3. Surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization.

August 10, 2017

Towards Tracking Opium Related Discussions in Social Media

In recent years, the use of social media has increased at an unprecedented rate. For example, the popular social media platform Reddit (http://www.reddit.com) had 83 billion page views from over 88,000 active sub-communities (subreddits) in 2015. Members of Reddit made over 73 million individual posts and over 725 million associated comments in the same year [1].

August 20, 2017

A Digital Platform for Local Foodborne Illness and Outbreak Surveillance

Foodborne illness affects 1 in 4 Americans, annually. However, only a fraction of affected individuals seek medical attention. To supplement traditional approaches to foodborne disease surveillance, researchers and public health departments are considering reports of foodborne illness on social media sites. In this project, we work with local public health departments to develop a platform that uses digital data sources such as, Twitter and Yelp, to supplement foodborne disease surveillance efforts.

August 07, 2017

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This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.

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