Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence

Background Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.Results Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon.Conclusion The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting

Assessing the Online Social Environment for Surveillance of Obesity Prevalence

Background: Understanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data. Purpose This article explores the relationship between online social environment via web-based social networks and population obesity prevalence. Methods: We performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook. Results: Higher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set. Conclusions: Activity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions

Big Data Opportunities for Global Infectious Disease Surveillance

Simon Hay and colleagues discuss the potential and challenges of producing continually updated infectious disease risk maps using diverse and large volume data sources such as social media

Digital Surveillance: A Novel Approach to Monitoring the Illegal Wildlife Trade

A dearth of information obscures the true scale of the global illegal trade in wildlife. Herein, we introduce an automated web crawling surveillance system developed to monitor reports on illegally traded wildlife. A resource for enforcement officials as well as the general public, the freely available website, http://www.healthmap.org/wildlifetrade, provides a customizable visualization of worldwide reports on interceptions of illegally traded wildlife and wildlife products. From August 1, 2010 to July 31, 2011, publicly available English language illegal wildlife trade reports from official and unofficial sources were collected and categorized by location and species involved. During this interval, 858 illegal wildlife trade reports were collected from 89 countries. Countries with the highest number of reports included India (n = 146, 15.6%), the United States (n = 143, 15.3%), South Africa (n = 75, 8.0%), China (n = 41, 4.4%), and Vietnam (n = 37, 4.0%). Species reported as traded or poached included elephants (n = 107, 12.5%), rhinoceros (n = 103, 12.0%), tigers (n = 68, 7.9%), leopards (n = 54, 6.3%), and pangolins (n = 45, 5.2%). The use of unofficial data sources, such as online news sites and social networks, to collect information on international wildlife trade augments traditional approaches drawing on official reporting and presents a novel source of intelligence with which to monitor and collect news in support of enforcement against this threat to wildlife conservation worldwide

Application of change point analysis to daily influenza-like illness emergency department visits

Background: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009–10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states. Conclusions and significance As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems

Self-Reported Fever and Measured Temperature in Emergency Department Records Used for Syndromic Surveillance

Many public health agencies monitor population health using syndromic surveillance, generally employing information from emergency department (ED) visit records. When combined with other information, objective evidence of fever may enhance the accuracy with which surveillance systems detect syndromes of interest, such as influenza-like illness. This study found that patient chief complaint of self-reported fever was more readily available in ED records than measured temperature and that the majority of patients with an elevated temperature recorded also self-reported fever. Due to its currently limited availability, we conclude that measured temperature is likely to add little value to self-reported fever in syndromic surveillance for febrile illness using ED records

Preventing Pandemics via International Development: A Systems Approach

Tiffany Bogich and colleagues find that breakdown or absence of public health infrastructure is most often the driver in pandemic outbreaks, whose prevention requires mainstream development funding rather than emergency funding

Triatomine Infestation in Guatemala: Spatial Assessment after Two Rounds of Vector Control

In 2000, the Guatemalan Ministry of Health initiated a Chagas disease program to control Rhodnius prolixus and Triatoma dimidiata by periodic house spraying with pyrethroid insecticides to characterize infestation patterns and analyze the contribution of programmatic practices to these patterns. Spatial infestation patterns at three time points were identified using the Getis-Ord Gi*(d) test. Logistic regression was used to assess predictors of reinfestation after pyrethroid insecticide administration. Spatial analysis showed high and low clusters of infestation at three time points. After two rounds of spray, 178 communities persistently fell in high infestation clusters. A time lapse between rounds of vector control greater than 6 months was associated with 1.54 (95% confidence interval = 1.07-2.23) times increased odds of reinfestation after first spray, whereas a time lapse of greater than 1 year was associated with 2.66 (95% confidence interval = 1.85-3.83) times increased odds of reinfestation after first spray compared with localities where the time lapse was less than 180 days. The time lapse between rounds of vector control should remain under 1 year. Spatial analysis can guide targeted vector control efforts by enabling tracking of reinfestation hotspots and improved targeting of resources

Digital Epidemiology

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges