Only a few studies have investigated the potential of using geotagged social media data for predicting the patterns of spatio-temporal spread of vector-borne diseases. We herein demonstrated the role of human mobility in the intra-urban spread of dengue by weighting local incidence data with geo-tagged Twitter data as a proxy for human mobility across 45 neighborhoods in Yogyakarta city, Indonesia. To estimate the dengue virus importation pressure in each study neighborhood monthly, we developed an algorithm to estimate a dynamic mobility-weighted incidence index (MI), which quantifies the level of exposure to virus importation in any given neighborhood. Using a Bayesian spatio-temporal regression model, we estimated the coefficients and predictiveness of the MI index for lags up to 6 months. Specifically, we used a Poisson regression model with an unstructured spatial covariance matrix. We compared the predictability of the MI index to that of the dengue incidence rate over the preceding months in the same neighborhood (autocorrelation) and that of the mobility information alone. We based our estimates on a volume of 1·302·405 geotagged tweets (from 118·114 unique users) and monthly dengue incidence data for the 45 study neighborhoods in Yogyakarta city over the period from August 2016 to June 2018. The MI index, as a standalone variable, had the highest explanatory power for predicting dengue transmission risk in the study neighborhoods, with the greatest predictive ability at a 3-months lead time. The MI index was a better predictor of the dengue risk in a neighborhood than the recent transmission patterns in the same neighborhood, or just the mobility patterns between neighborhoods. Our results suggest that human mobility is an important driver of the spread of dengue within cities when combined with information on local circulation of the dengue virus. The geotagged Twitter data can provide important information on human mobility patterns to improve our understanding of the direction and the risk of spread of diseases, such as dengue. The proposed MI index together with traditional data sources can provide useful information for the development of more accurate and efficient early warning and response systems.

A combination of incidence data and mobility proxies from social media predicts the intra-urban spread of dengue in Yogyakarta, Indonesia

2015 American Journal of Gastroenterology Lecture: How Digital Health Will Transform Gastroenterology.

This study seeks to validate a search protocol of ill health-related terms using Twitter data which can later be used to understand if, and how, Twitter can reveal information on the current health situation We extracted conversations related to health and disease postings on Twitter using a set of pre-defined keywords, assessed the prevalence, frequency, and timing of such content in these conversations, and validated how this search protocol was able to detect relevant disease tweets Classification and Regression Trees (CART) algorithm was used to train and test search protocols of disease and health hits comparing to those identified by our team The accuracy of predictions showed a good validity with AUC beyond 08 Our study shows that monitoring of public sentiment on Twitter can be used as a real-time proxy for health events

pdf/mining-of-health-and-disease-events-on-twitter-validating-4qtyeg8ws3.pdf

Mining of health and disease events on Twitter: validating search protocols within the setting of Indonesia

BACKGROUND: Twitter is a free social networking and micro-blogging service that enables its users to read and share information with user and media communities in messages no longer than 140-character. In the year of 2016, there were more than 24 million Indonesian twitter users sharing news, events, as well as personal feelings and experiences on Twitter. This study seeks to validate a search protocol of health related terms using real-time Twitter data which can later be used to understand if, and how, twitter can reveal information on the current health situation in Indonesia. In this validation study of mining protocols, we: 1) extracted geo-located conversations related to health and disease postings on Twitter using a set of pre-defined keywords, 2) assessed the prevalence, frequency and timing of such content in these conversations, and 3) validated how this search protocol was able to detect relevant disease tweets.

SUBJECT AND METHODS: Groups of words and phrases relevant to disease symptoms and health outcomes were used in a protocol developed in the Indonesian language in order to extract relevant content from geo-tagged Twitter feeds. A supervised learning algorithm using Classification and Regression Tree´s (CART) was used to validate search protocols of disease and health hits comparing to those identified by a team of human experts. The experts categorized tweets as positive or negative in respect to health events. The model fit was evaluated based on prediction perfor-mance.

RESULTS: 390 tweets from historical Twitter feeds and 1,145,649 tweets from Twitter stream feeds during the period July 26th to August 1st, 2016. Only twitter hits with health related keywords in the Indonesian language were obtained. The accuracy of predictions of mined hits versus expert validated hits using the CART algorithm showed good validity with AUC beyond 0.8.

CONCLUSION: Monitoring of public sentiment on Twitter, combined with contextual knowledge about the disease, can detect health and disease tweets and potentially be used as a valuable real-time proxy for health events over space and time.

Keywords: social networking, disease detection, disease early warning, digital epidemiology, big data analysis

/pdf/validating-search-protocols-for-mining-of-health-and-disease-175pmi8okt.pdf

Validating search protocols for mining of health and disease events on twitter

In the year of 2016, there were more than 24 million Indonesian twitter users sharing news, events, as well as personal feelings and experiences on Twitter. This study seeks to validate a search protocol of health-related terms using real-time Twitter data which can later be used to understand if, and how, twitter can reveal information on the current health situation in Indonesia. In this validation study of mining protocols, we extracted geo-located conversations related to health and disease postings on Twitter using a set of pre-defined keywords, assessed the prevalence, frequency and timing of such content in these conversations, and validated how this search protocol was able to detect relevant disease tweets. 
Groups of words and phrases relevant to disease symptoms and health outcomes were used in a protocol developed in the Indonesian language in order to extract relevant content from geo-tagged Twitter feeds. A supervised learning algorithm using Classification and Regression Trees was used to validate search protocols of disease and health hits comparing to those identified by a team of human experts. The experts categorized tweets as positive or negative in respect to health events. The model fit was evaluated based on prediction performance. 
We observed 390 tweets from historical Twitter feeds and 1,145,649 tweets from Twitter stream feeds during the period July 26th to August 1st, 2016. Only twitter hits with health related keywords in the Indonesian language were obtained. The accuracy of predictions of mined hits versus expert validated hits using the CART algorithm showed good validity with AUC beyond 0.8. 
Our study shows that monitoring of public sentiment on Twitter, combined with contextual knowledge about the disease, can detect health and disease tweets and potentially be used as a valuable real-time proxy for health events over space and time.

Validating search protocols for mining of health and disease events on Twitter

Su1027 Measuring Health Related Quality of Life (HRQoL) in Crohn's Disease Using Twitter: A Pilot Study of Social Media As a Novel Tool to Assess Disease Burden

Intrauterine Insemination After Human Chorionic Gonadotropin Trigger or Luteinizing Hormone Surge

Principal investigator sex and trial success: an analysis of early discontinuation, results reporting, and publication

Cervical cancer continues to represent an immense burden worldwide. Fortunately, in all stages of the disease and therapeutic modalities there have been significant advances in the past 5 years with the promise of improving oncologic outcomes and decreasing morbidity. The introduction of immunotherapy into the care of patients with metastatic and recurrent cervical cancer, a disease with a historically poor prognosis and limited treatment options, has altered the standard of care. 

Advances in Cervical Cancer

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Papers

Su1027 Measuring Health Related Quality of Life (HRQoL) in Crohn's Disease Using Twitter: A Pilot Study of Social Media As a Novel Tool to Assess Disease Burden

Intrauterine Insemination After Human Chorionic Gonadotropin Trigger or Luteinizing Hormone Surge

Principal investigator sex and trial success: an analysis of early discontinuation, results reporting, and publication

Advances in Cervical Cancer