BioSurveillance

To review current trends and issues in the development and use of mobile apps for public health surveillance and decision making in settings with different resource availability and technological development. The panel discussion will address cross-cutting issues of general interest, including timeliness, recruitment, validation, and engagement by presenting innovative examples of apps conceived for various uses in human and animal surveillance.

Introduction

An increasing number of mobile applications available for download provide biosurveillance capabilities using new and traditional data streams. Biosurveillance apps span a wide range of settings, uses, technologies, and resource capacities that provide health analysts rapid and efficient means of data collection, visualization, and analyses. However, this technological “heaven” is not free from the challenges of traditional biosurveillance applications, namely validation to inform specificity and recruitment and engagement to ensure representativeness. This panel will provide guidance for future development and utility of mobile apps by illustrating how these matters are addressed in field tested mobile applications.

Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets and increases power to detect irregularly-shaped clusters, However, unconstrained subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is too under-constrained for use with case/control point data. Thus we propose the star-shaped scan statistic (StarScan), a novel method that efficiently maximizes the loglikelihood ratio over irregularly-shaped clusters, while incorporating soft constraints on smoothness. More precisely, we allow the radius of the cluster around a center location to vary along with angle, and penalize proportional to the total change in radius.

Objective

We present StarScan, a novel scan statistic for accurately detecting irregularly-shaped disease outbreaks. StarScan maximizes a penalized log-likelihood ratio statistic, allowing the radius around a central location to vary as a function of the angle and applying a penalty proportional to the total change in radius.

 

Since the release of anthrax in October of 2001, there has been increased interest in developing efficient prospective disease surveillance schemes. Poisson CUSUM is a control chart-based method that has been widely used to detect aberrations in disease counts in a single region collected over fixed time intervals. Over the past few years, different methods have been proposed to extend Poisson CUSUM charts to capture the spatial association among several regions simultaneously. In the proposed method, we extend an algorithm in industrial process control using multiple Poisson CUSUM charts to the spatial setting. The spatial association among regions is captured using the method proposed by Raubertas, which has been successfully applied in several prospective surveillance schemes. Also, to improve the power of the traditional multiple Poisson CUSUM charts, Poisson CUSUM charts were used along with fault discovery rate (FDR) control techniques.

Objective

To develop a computationally simple and fast algorithm for rapid detection of outbreaks producing easily interpretable results.

Bordetella pertussis infection (whooping cough) has been on the rise and the most cases in the US since 1955 were reported in 2012 (48,277 or 15.4 per 100,000). Pertussis is highly infectious and can cause serious illness in infants and children as well as adults, and in general is preventable by vaccination. Since 2005, it has been recommended that anyone 19-64 years old should have a onetime booster of the pertussis vaccine (Tdap). In 2010, that recommendation was broadened stating people 65 years old and older should also obtain a booster of Tdap. Given the increased number of pertussis cases in the Western US, and that approximately 20% of these cases occurred in patients >20 years of age, we performed pertussis surveillance in Veterans in care at VA medical facilities.

Objective

To perform pertussis surveillance in VA facilities in the Western US.

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. In fact, studies by Johns Hopkins University and Harvard University have shown that social media reporting can serve as an early indicator and warning of emerging health issues within a community. Whether people are talking about being sick themselves or fear of illness in the community, there is a wealth of knowledge to be gained by tapping into this information. Yet gaining insight and understanding from social media data can be problematic. The unstructured nature of the data, the presence of social media “spam”, and the frequency of reposting information makes social media a noisy data source. Being able to harness this data would provide the opportunity to use social media as an effective situational awareness and early warning tool for biosurveillance missions. But how do you accomplish this? There are tens of millions of conversations happening on social media every day that would need to be sifted through to get to the health related topics. No public health entity has the time or staffing for that endeavor.

Objective

The goal of the Now Trending website is to provide a web based tool that pulls out relevant Twitter conversations concerning illness and disasters and provides meaningful analytics on how those conversations are trending. The website gives the user the ability to view trends overall and for specific geographic areas.

Effective use of data for disease surveillance depends critically on the ability to trust and quantify the quality of source data. The Scalable Data Integration for Disease Surveillance project is developing tools to integrate and present surveillance data from multiple sources, with an initial focus on malaria. Consideration of data quality is particularly important when integrating data from diverse clinical, population-based, and other sources. Several global initiatives to reduce the burden of malaria (Presidents Malaria Initiative, Roll Back Malaria Initiative and The Global Fund to Fight AIDS, Tuberculosis and Malaria) have published lists of recommended indicators. Values for these indicators can be obtained from different data sources, with each source having different data quality properties as a consequence of the type of data collected and the method used to collect the data. Our goal is to develop a framework for organizing the data quality (DQ) properties of indicators used for disease surveillance in this setting.

Biosurveillance Portal (BSP) is a web-based enterprise environment that is aimed to facilitate international collaboration, communication, and information-sharing in support of the detection, management, and mitigation of biological events in Korea. In Oct 2013, Republic of Korea (ROK) Ministry of National Defense has made the project agreement with United States (US) Department of Defense Joint Program Executive Office of Chemical and Biological Defense to develop Biosurveillance Portal which will provide tools and capabilities to facilitate timely identification and detection of biological events to minimize operational impacts on ROK-US Forces. As a part of this project, Armed Forces Medical Command (AFMC) undertook the initiative to develop the Military Active Realtime Syndromic Surveillance system.

Objective

This presentation aims to elaborate our experiences from initiating a syndromic surveillance system as a part of current biosurveillance developments in Korea. We developed Military Active Realtime Syndromic Surveillance (MARSS) system with data from all of 19 Korean military hospitals as a part of the US-ROK joint Biosurveillance Project.

Since the adoption of antibiotics in the early 20th century, a plethora of clinical pathogens have acquired resistance to one or more modern-day antibiotics. This has resulted in antimicrobial resistance (AMR) being recognized as a severe threat to human and animal health worldwide. Recent work has demonstrated that AMR bacteria are widely prevalent in the environment, perhaps exacerbated by the widespread use of antibiotics for clinical or agricultural purposes.

Objective

To assess the temporal dynamics of airborne bacterial communities in four locations around the National Capital Region and the dispersion of antimicrobial resistant (AMR) genes present within them.

The National Strategy for Biosurveillance promotes a national effort to improve early detection and enable ongoing situational awareness of all-hazards threats. Implicit in the Strategy’s implementation plan is the need to upgrade capabilities and integrate multiple disparate data sources, including more complete electronic health record (EHR) data into future biosurveillance capabilities. Thus, new biosurveillance applications are clearly needed. Praedico™ is a next generation biosurveillance application that incorporates cloud computing technology, a Big Data platform utilizing MongoDB as a data management system, machine-learning algorithms, geospatial and advanced graphical tools, multiple EHR domains, and customizable social media streaming from public health-related sources, all within a user friendly interface.

Objective

The purpose of our study was to conduct an initial assessment of the biosurveillance capabilities of a new software application called Praedico™ and compare results obtained from previous queries with the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE).