Extracting Signals from Social Media for Chronic Disease Surveillance

Abstract

Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis

“…In addition, social media data have been used to improve our understanding of Ebola [37] and Zika virus infections [38-40]. Although disease surveillance efforts tend to focus on acute infectious diseases, studies have also been conducted on chronic diseases such as cancer [41], hypertension [41], asthma [41-43], diabetes [44], and seasonal allergic rhinitis [45-47]. Systematic reviews have also been conducted on disease surveillance based on social media data [4-6].…”

Using Arabic Tweets to Understand Drug Selling Behaviors

“…Sidana et al [12], for example, identified health-related tweets and non-health-related tweets, modeled the development of ailments, and predicted diseases. Zhang et al [13] developed a model that uses several data sources (i.e., data on hospital visits, the prevalence of asthma among adults in the US, and Twitter) to monitor chronic diseases, including asthma, and to predict hospital visits. Byrd, Mansurov, and Baysal [14] demonstrated how to use tweets to detect and surveil influenza in a given area at a given time.…”

Measuring Global Disease with Wikipedia

“…These traces of disease observations are embedded in search queries [5, 7, 9, 12, 14, 17, 21, 25, 26, 31, 32, 33, 39, 49, 50, 53, 59, 63, 64, 71, 72 73, 77, 78, 81, 85, 87, 90, 97, 103, 104, 109, 119, 126, 127, 131, 132, 141, 142, 144, 146, 157, 158, 162, 163, 166, 168, 169, 170, 173, 177, 179, 180, 182], social media messages [1, 2, 8, 10, 20, 36, 40, 41, 42, 46, 51, 60, 62, 68, 76, 84, 89, 92, 93, 115, 116, 118, 123, 124, 148, 149, 151, 176], web server access logs [57, 79, 101, 105], and combinations thereof [13, 19, 30, 91, 136, 143, 167]. …”

“…The disease surveillance work cited above has been applied to a wide variety of infectious and non-infectious conditions: allergies [87], asthma [136, 176], avian influenza [25], cancer [39], chicken pox [109, 126], chikungunya [109], chlamydia [42, 78, 109], cholera [36, 57], dengue [7, 31, 32, 57, 62, 109], diabetes [42, 60], dysentery [180], Ebola [5, 57], erythromelalgia [63], food poisoning [12], gastroenteritis [45, 50, 71, 126], gonorrhea [77, 78, 109], hand foot and mouth disease [26, 167], heart disease [51, 60], hepatitis [109], HIV/AIDS [57, 76, 177, 180], influenza [1, 2, 8, 9, 10, 13, 19, 20, 21, 30, 33, 40, 41, 43, 46, 48, 53, 57, 59, 68, 72, 73, 79, 81, 84, 85, 89, 90, 91, 92, 93, 97, 101, 103, 104, 105, 109, 115, 116, 118, 123, 124, 126, 131, 132, 141, 14...…”