Infectious Disease

After MERS outbreak in 2015, the provincial government and infectious disease control center (GIDCC) initiated an emergency department (ED) based Gyeonggi-do provincial acute febrile illness (AFI) surveillance network (GAFINet) to monitor for a subsequent outbreak of emerging or imported infectious diseases since September 2016. Following pilot operation from September to December 2016, the operation was run for one year from June 2017 to May 2018. GAFINet Initiative involves ten hospitals, consisted of four university-affiliated hospitals and six provincial medical centers in Gyeonggi-do province. These hospitals participated in this network voluntarily.

Objective: The purpose of this study is to describe and evaluate the results of the GAFINET(Gyeonggi Acute Febrile Illness Surveillance Network) operated for one year.

Legionellosis is a respiratory illness that is mostly (80-90%) caused by the bacterium Legionella pneumophila. It is associated with a mild febrile illness, Pontiac fever, or Legionnaires'™ disease (1), a source of severe, community-acquired pneumonia. Legionella bacteria mostly affect elderly persons specifically those with underlying debilitating illnesses and with lowered immune systems. Water is the major natural reservoir for Legionella, and the pathogen is found in many different natural and artificial aquatic environments such as cooling towers or water systems in buildings, including hospitals. An abrupt increase in the incidence of Legionnaires' has been noted since 2003 throughout the nation. According to CDC, about 6,000 cases of Legionnaires' disease were reported in the United State in 2015 (1). Incidence rates of Legionnaires for the year 2015 were 1.06 and 1.90 (ref) for Texas and the United States respectively (2). Increased number of reported cases might be due to the fact of an older population, more at risk individuals, aging plumbing infrastructure, and increased testing for Legionnaires' disease by various hospitals and laboratories.

Objective: To study trends and patterns in legionnaires' disease cases in Houston, Texas, from 2014-2017.

Intestinal infectious diseases (IID) is a common cause of illness in the community and results in a high burden of consultations to general practice, mostly affecting the health of infants, preschool children, young adults and elderly people, especially those living in low income countries. According to the published study on the global burden of disease, intestinal infectious diseases were responsible for 221,300 deaths worldwide in 2013. The Chinese Ministry of Health has listed bacillary dysentery, amebic dysentery, typhoid fever and paratyphoid fever as notifiable Class-B communicable diseases and other infectious diarrhea as notifiable Class-C communicable diseases to be included in the surveillance system and reporting network since 2004. Many studies of IID in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level IID cases in a major city such as Beijing are still unknown. We aim to analyze the epidemiology features and identify space-time clusters of Beijing IID at a fine spatial scale in this study.

Objective: To investigate epidemiological features and identify high relative risk space-time Intestinal infectious diseases clusters at the township level in Beijing city in order to provide the scientific evidence for making prevention and control measures.

Traditionally, public health agencies (PHAs) wait for hospital, laboratory or clinic staff to initiate case reports. However, this passive approach is burdensome for reporters and produces incomplete and delayed reports, which can hinder assessment of disease in the community and potentially delay recognition of patterns and outbreaks. Modern surveillance practice is shifting toward greater use of electronically transmitted disease information. The adoption of electronic health record (EHR) systems and health information exchange (HIE) among clinical organizations and systems, driven by policies such as the meaningful use™ program, is creating an information infrastructure that public health organizations can take advantage of to improve surveillance practice.

Objective: To enhance the process by which outpatient providers report surveillance case information to public health authorities following a laboratory-confirmed diagnosis of a reportable 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.