Outbreak

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.

Contact tracing is an important strategy employed in surveillance to aid prompt detection of infectious disease and control outbreaks. It involves the identification of those who have come in contact with an infectious person and following them up for the duration of the incubation period of the disease to promptly detect symptoms and signs and institute treatment thus reducing chances of disease spread to other susceptible individuals. It is a primary means of disease prevention. The importance of cooperation from contacts being traced cannot be overemphasized as they are required to promptly report symptoms, avoid gatherings and travelling until they are cleared by the surveillance team. The follow-up should also link contacts who become symptomatic to designated care centers. In 2014, during the Ebola outbreak in Nigeria, the disease spread from Lagos to another State in the country by a contact who travelled out of Lagos to Rivers state. To identify risk factors among contacts of Lassa fever cases which can predispose to disease spread and institute control measures, we interviewed primary contacts of Lassa fever cases during an outbreak response in Kogi State Nigeria, 2016.

Objective: To identify risk factors among contacts of Lassa fever cases which can predispose to disease spread and institute control measures.

Although cases of acute HAV have declined in recent years, elevated numbers of HAV infections began to be reported by California and Michigan in the fall of 2016.1,2 Since this time, associated outbreaks have been reported in 9 additional states (Arizona, Utah, Kentucky, Missouri, Tennessee, Indiana, Ohio, Arkansas, and West Virginia).3 No common source of food, beverages or drugs have been identified and transmission appears to be primarily person-to-person with high-risk individuals including people experiencing homelessness, those who use illicit drugs and their close direct contacts. In June 2018, CDC issued a Health Alert Network Advisory providing additional guidance on identification and prevention of HAV and updates on the outbreaks.4 This prompted our office to more closely review our HAV surveillance, to identify Veterans who may be part of these outbreaks, and assess risk factors and outcomes of HAV infection.

Objective: To conduct surveillance for acute Hepatitis A virus (HAV) infections in Veterans from states reporting outbreaks among high-risk individuals beginning in fiscal year (FY) 2017.

Reliable detection and accurate scoping of outbreaks of foodborne illness are the keys to effective mitigation of their impacts. However, relatively small number of persons affected and underreporting, challenge the reliability of surveillance models. In this work, we correlate a record of identified outbreaks and sporadic cases of Salmonellosis in humans retained in PulseNet1, and diagnosis codes in hospital claims collected in California from 2006 to 2010. We hypothesize that the data support and reliability of detection could be improved by including cases in which Salmonella infection may be confused2.

Objective

To investigate utility of using inpatient and emergency room diagnoses to detect outbreaks of Salmonellosis in humans. To quantify the impact of including in the analysis cases diagnosed with conditions that may have physiological appearance similar to Salmonellosis.

In light of recent communicable disease outbreaks, the ability of Florida Department of HealthÕs (FDOH) syndromic surveillance system, ESSENCE-FL, to identify emergent disease outbreaks using reportable disease data and algorithms originally designed for emergency department chief complaint data was examined. Preliminary work on this analysis presented last year was recently updated and expanded to include additional diseases, further levels of locale, and detector algorithm comparisons. Cases are entered into Merlin, the Bureau of EpidemiologyÕs secure web-based reporting and epidemiologic analysis system, by all 67 county health departments and the de-identified case data are sent hourly to ESSENCE-FL. These data are then available for ad hoc queries, allowing users to observe unusual changes in disease activity and assist in timely identification of infectious disease outbreaks. Based on system algorithms, weekly case tallies are assigned an increasing intensity awareness status from normal to alert and are monitored by county and state epidemiologists to guide timely disease control efforts, but may not by themselves be definitive actionable information.

Objective

To determine if there is an association between known outbreak activity and ESSENCE generated alerts. 

The Veterans Health Administration (VHA) uses the Electronic Surveillance System for the Early Notification of Community-based Epidemics to detect disease outbreaks and other health-related events earlier than other forms of surveillance. Although Veterans may use any VHA facility in the world, the strongest predictor of which health care facility is accessed is geographic proximity to the patient's residence. A number of outbreaks have occurred in the Veteran population when geographically separate groups convened in a single location for professional or social events. One classic example was the initial Legionnaire's disease outbreak, identified among participants at the Legionnaire's convention in Philadelphia in the late 1970s. Numerous events involving travel by large Veteran (and employee) populations are scheduled each year.

Objective

To develop an algorithm to identify disease outbreaks by detecting aberrantly large proportions of patient residential ZIP codes outside a health care facility catchment area.

Shigella remains highly infectious in the United States and rapid detection of Shigella outbreaks is crucial for disease control and timely public health actions. The New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS) for local health departments (LHDs) to collect clinical and laboratory testing information and supplement epidemiologic information for the patients from New York State, excluding New York City, with infectious diseases. The CDESS includes reported cases that are involved in outbreaks and which constituted the base for identifying any outbreak. The selection of a fitted outbreak detection method would play a critical role in enhancing disease surveillance.

Objective

To explore the possibility of using statistical methods to detect Shigella outbreaks, assess the effectiveness of the methods to signal real outbreaks, provide manageable information for follow-up activities and avoid unnecessary surveillance work.

It is admitted that real time surveillance system permits to reduce delay of outbreak detection, and preventive measures implementation. It is usually based on pre diagnostic numeric data collection and transmission. ASTER (Alerte et surveillance en temps reel) is a real time surveillance system for French Armed Forces deployed in French Guiana and Djibouti, constituted by 2 kinds of networks : several declaration networks and one analysis network. On June 2011, an outbreak occurred among a French Army Regiment in Djibouti, which has permitted to evaluate ASTER in real conditions.

Objective

To discuss advantages of real time surveillance system within Armed Forces, using a real outbreak case.