Infectious Disease

Evidence about the value of syndromic surveillance data for outbreak detection is limited. In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018.

Objective: Describe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia.

The mission of the Infectious-Disease-Epidemiology Department at the Robert Koch Institute is the prevention, detection and control of infections in the German population. For this purpose it has a set of surveillance and outbreak-detection systems in place. Some of these cover a wide range of diseases, e.g. the traditional surveillance of about 80 notifiable diseases, while others are specialised for the timely assessment of only one or a few diseases, e.g. participatory syndromic surveillance of acute respiratory infections. Many different such data sources have to be combined to allow a holistic view of the epidemiological situation. The continuous integration of many heterogeneous data streams into a readily available and accessible product remains a big challenge in infectious-disease epidemiology.

Objective: Providing an integrative tool for public health experts to rapidly assess the epidemiological situation based on data streams from different surveillance systems and relevant external factors, e.g. weather or socio-economic conditions. The efficient implementation in a modular architecture of disease- or task-specific visualisations and interactions, their combination in dashboards and integration in a consistent, general web application. The user-oriented development through an iterative process in close collaboration with epidemiologists.

Increasingly public health decision-makers are using syndromic surveillance for real-time reassurance and situational awareness in addition to early warning1. Decision-makers using intelligence, including syndromic data, need to understand what the systems are capable of detecting, what they cannot detect and specifically how much reassurance should be inferred when syndromic systems report nothing detected. In this study we quantify the detection capabilities of syndromic surveillance systems used by Public Health England (PHE). The key measures for detection capabilities are specificity and sensitivity (although timeliness is also very important for surveillance systems)2. However, measuring the specificity and sensitivity of syndromic surveillance systems is not straight forward. Firstly, syndromic systems are usually multi-purpose and may be better at identifying certain types of public health threat than others. Secondly, whilst it is easy to quantify statistical aberration detection algorithms, surveillance systems involve other stages, including data collection and human decision-making, which also affect detection capabilities. Here, we have taken a systems thinking approach to understand potential barriers to detection, and summarize what we know about detection capabilities of syndromic surveillance systems in England.

Objective: To communicate the detection capabilities of syndromic surveillance systems to public health decision makers.

Situational awareness is important for both early warning and early detection of a disease outbreak, and analytics and tools that furnish information on how an infectious outbreak would either emerge or unfold provide enhanced situational awareness for decision makers/analysts/public health officials, and support planning for prevention or mitigation. Data sharing and expert analysis of incoming information are key to enhancing situational awareness of an unfolding event. In this presentation, we will describe a suite of tools developed at Los Alamos National Laboratory (LANL) that provide actionable information and knowledge for enhanced situational awareness during an unfolding event; The biosurveillance resource directory (BRD), the biosurveillance analytics resource directory (BaRD) and the surveillance window app (SWAP).

Objective

To develop a suite of tools that provides actionable information and knowledge for enhanced situational awareness during an unfolding event such as an infectious disease outbreak.

Argus is an event-based surveillance system which captures information from publicly available Internet media in multiple languages. The information is contextualized and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system's users. In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.

Objective

To demonstrate how event-based biosurveillance can be utilized to closely monitor disease emergence in an isolated rural area, where medical information and epidemiological data are limited, toward identifying areas for public health intervention improvements.

Data obtained through public health surveillance systems are used to detect and locate clusters of cases of diseases in space-time, which may indicate the occurrence of an outbreak or an epidemic. We present a methodology based on adaptive likelihood ratios to compare the null hypothesis (no outbreaks) against the alternative hypothesis (presence of an emerging disease cluster).

Objective

Disease surveillance is based on methodologies to detect outbreaks as soon as possible, given an acceptable false alarm rate. We present an adaptive likelihood ratio method based on the properties of the martingale structure which allows the determination of an upper limit for the false alarm rate.

A devastating cholera outbreak began in Haiti in 2010. Sequencing of Vibrio cholerae isolates showed that the epidemic was likely the result of the introduction of cholera from a distant geographic source. The same strain of cholera was detected in other countries within 100 days. The unique instigation and geographic spread of this epidemic highlight the need for improvements in timely global outbreak surveillance. Novel information sources have been shown to provide early information about public health events and disease epidemiology. Particularly, volume of Internet metrics such as web searches or micro-blogs have been shown to be a good corollary for public health events. In this study, we evaluate geographic trends in online social media following an infectious disease outbreak to determine whether this may enable prediction of secondary outbreak locations.

Objective

To evaluate the association between and develop a risk model relating geographic trends of social media and spread of an infectious disease outbreak.

Informal surveillance systems like HealthMap are effective at the early detection of outbreaks. However, reliance on informal sources such as news media makes the efficiency of these systems vulnerable to newsroom constraints, namely high-profile disease events drawing reporting resources at the expense of other potential outbreaks and diminished staff over weekends and holidays. To our knowledge, this effect on informal or syndromic surveillance systems has yet to be studied.

Objective

Reporting about large public health events may reduce effective disease surveillance by syndromic or informal surveillance systems. The goal is to determine to what extent this problem exists and characterize situations in which it is likely to occur.

Taiwan had established a nation-wide emergency department (ED)-based syndromic surveillance system since 2004, with a mean detection sensitivity of 0.67 in 2004-06 [1]. However, this system may not represent the true epidemic situation of infectious disease in community, particularly those who don't seek medical care [2]. Moreover, the epidemiological settings, sources of the infection and social network all together may still facilitate the transmissions. These rooted problems cannot be rapidly solved.

Objective

This study has two specific aims:

(1) to establish a web-based, public-access infectious disease reporting system (www.eid.url.tw), using newly designed public syndrome groups and based on computational and participatory epidemiology

(2) to evaluate this system by comparing the epidemiological patterns with national-wide electronic health-database and traditional passive surveillance systems from Taiwan-CDC.