Browsing by Author "Ghosh, Saurav"
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- Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016McGowan, Craig J.; Biggerstaff, Matthew; Johansson, Michael; Apfeldorf, Karyn M.; Ben-Nun, Michal; Brooks, Logan; Convertino, Matteo; Erraguntla, Madhav; Farrow, David C.; Freeze, John; Ghosh, Saurav; Hyun, Sangwon; Kandula, Sasikiran; Lega, Joceline; Liu, Yang; Michaud, Nicholas; Morita, Haruka; Niemi, Jarad; Ramakrishnan, Naren; Ray, Evan L.; Reich, Nicholas G.; Riley, Pete; Shaman, Jeffrey; Tibshirani, Ryan; Vespignani, Alessandro; Zhang, Qian; Reed, Carrie; Rosenfeld, Roni; Ulloa, Nehemias; Will, Katie; Turtle, James; Bacon, David; Riley, Steven; Yang, Wan; The Influenza Forecasting Working Group (Nature Publishing Group, 2019-01-24)Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts. © 2019, The Author(s).
- News Analytics for Global Infectious Disease SurveillanceGhosh, Saurav (Virginia Tech, 2017-11-29)Traditional disease surveillance can be augmented with a wide variety of open sources, such as online news media, twitter, blogs, and web search records. Rapidly increasing volumes of these open sources are proving to be extremely valuable resources in helping analyze, detect, and forecast outbreaks of infectious diseases, especially new diseases or diseases spreading to new regions. However, these sources are in general unstructured (noisy) and construction of surveillance tools ranging from real-time disease outbreak monitoring to construction of epidemiological line lists involves considerable human supervision. Intelligent modeling of such sources using text mining methods such as, topic models, deep learning and dependency parsing can lead to automated generation of the mentioned surveillance tools. Moreover, real-time global availability of these open sources from web-based bio-surveillance systems, such as HealthMap and WHO Disease Outbreak News (DONs) can aid in development of generic tools which will be applicable to a wide range of diseases (rare, endemic and emerging) across different regions of the world. In this dissertation, we explore various methods of using internet news reports to develop generic surveillance tools which can supplement traditional surveillance systems and aid in early detection of outbreaks. We primarily investigate three major problems related to infectious disease surveillance as follows. (i) Can trends in online news reporting monitor and possibly estimate infectious disease outbreaks? We introduce approaches that use temporal topic models over HealthMap corpus for detecting rare and endemic disease topics as well as capturing temporal trends (seasonality, abrupt peaks) for each disease topic. The discovery of temporal topic trends is followed by time-series regression techniques to estimate future disease incidence. (ii) In the second problem, we seek to automate the creation of epidemiological line lists for emerging diseases from WHO DONs in a near real-time setting. For this purpose, we formulate Guided Epidemiological Line List (GELL), an approach that combines neural word embeddings with information extracted from dependency parse-trees at the sentence level to extract line list features. (iii) Finally, for the third problem, we aim to characterize diseases automatically from HealthMap corpus using a disease-specific word embedding model which were subsequently evaluated against human curated ones for accuracies.
- Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease OutbreaksGhosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren (Nature, 2017-01-19)In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.