Infectious Disease

There has been much research on statistical methods of prospective outbreak detection that are aimed at identifying unusual clusters of one syndrome or disease, and some work on multivariate surveillance methods. In England and Wales, automated laboratory surveillance of infectious diseases has been undertaken since the early 1990’s. The statistical methodology of this automated system is described in. However, there has been little research on outbreak detection methods that are suited to large, multiple surveillance systems involving thousands of different organisms.

Objective

To look at the diversity of the patterns displayed by a range of organisms, and to seek a simple family of models that adequately describes all organisms, rather than a well-fitting model for any particular organism.

EIDSS supports collection and analysis of epidemiological, clinical and laboratory information on infectious diseases in medical, veterinary and environmental sectors. At this moment the system is deployed in Kazakhstan at 150 sites (planned 271) in the veterinary surveillance and at 8 sites (planned 23) in human surveillance. The system enforces the one-health concept and provides capacity to improve surveillance and response to infectious disease including especially dangerous like CCHF. EIDSS has been in development since 2005 and is a free-of-charge tool with plans for open-source development. The system development is based on expertise of a number of US and international experts including CDC, WRAIR, USAMRIID, et al.

Objective:

The objective of this demonstration is to show conference attendees how one-health surveillance in medical, veterinary and environmental sectors can be improved with Electronic Integrated Disease Surveillance System (EIDSS) using CCHF as an example from Kazakhstan.

 

Syndromic surveillance system, which collects non-specific syndromes in the early stages of disease development, has great advantages in promoting early detection of epidemics and reducing the burden of disease confirmation. It is especially effective for surveillance in resource-poor settings, where laboratory confirmation is not possible or practical. Integrating syndromic surveillance with traditional case report system may generate timely, effective and sensitive information for early warning and control of infectious diseases in rural China. A syndromic surveillance system (ISSC) has been implemented in rural Jiangxi Province of China since August 2011.

Objective

To describe the distribution of the infectious related symptoms in an internet-based syndromic surveillance system reported by doctors in village health stations, township and county hospitals in rural Jiangxi Province, China, and to identify the major infectious diseases for syndromic surveillance in different levels of health facility.

Early detection of rarely occurring but potentially harmful diseases such as bio-threat agents (e.g., anthrax), chemical agents (e.g., sarin), and naturally occurring diseases (e.g., meningitis) is critical for rapid initiation of treatment, infection control measures, and emergency response plans. To facilitate clinicians’ ability to detect these diseases, various syndrome definitions have been developed. Due to the rarity of these diseases, standard statistical methodologies for validating syndrome definitions are not applicable.

Objective

To develop and test a novel syndrome definition validation approach for rarely occurring diseases.

Detection and response to seasonal outbreaks of endemic diseases provides an excellent testbed for quantitative bio-surveillance. As a case study we focus on annual influenza outbreaks. To incorporate observed year-over-year variation in flu incidence cases and timing of outbreaks, we analyze a stochastic compartmental SIS model that includes seasonal forcing by a latent Markovian factor. Epidemic detection then consists in identifying the presence of the environmental factor (“high” flu season), as well as estimation of the epidemic parameters, such as contact and recovery rates.

Objective

Development of a sequential Bayesian methodology for inference and detection of seasonal infectious disease epidemics.

Each year Ministry of Health and Social welfare of Tanzania under Epidemiology Section has been reporting many suspected cases of Shigella throughout the country. However only fewer laboratories have been reporting the confirmed cases.

Objective:

To determine whether the IDSR system meets its purpose and objectives, to evaluate the system attributes, and provide recommendations to improve the IDSR system, using the example of bacillary dysentery, a priority disease in Tanzania.

 

In the South East Asia Region (SEAR), infectious disease continues to be a leading cause of death. SEAR countries, like Vietnam, are also at risk for outbreaks of emerging diseases due to high population density, proximity to animals and deforestation. Given Vietnam’s location in SEAR and its recurrent outbreaks of zoonotic diseases— timely surveillance in Vietnam is critical to global public health. Online news sources have been recognized as potential sources for early detection of emerging disease outbreaks, as was the case with SARS.  HealthMap, an innovative disease surveillance system developed at Boston Children’s Hospital, leverages the expediency of online news media by using text-mining technology to monitor and map global disease outbreaks reported by news sources.

Objective

To present the development of a surveillance system utilizing online Vietnamese language media sources to detect disease events in Vietnam and the South East Asian Region.

Objective:

To describe disease and illness surveillance utilized during the 2012 Republican National Convention (RNC) held August 26-30, 2012 in Tampa, FL.

Introduction:

While the Tampa Bay Area has previously hosted other high profile events that required heightened disease surveillance (e.g., two Super Bowls), the 2012 RNC marked the first national special security event (NSSE) held in Florida. The Hillsborough County Health Department (HCHD), in conjunction with the Pinellas County Health Department (PinCHD) coordinated disease surveillance activities during this time frame. This presentation will focus of the disease surveillance efforts of the Hillsborough County Health Department during the 2012 RNC. In addition to the surveillance systems that are used routinely, the HCHD Epidemiology Program implemented additional systems designed to rapidly detect individual cases and outbreaks of public health importance. The short duration of RNC, coupled with the large number of visitors to our area, provided additional surveillance challenges. Tropical Storm Isaac, which threatened Tampa in the days leading up to RNC, and an overwhelming law enforcement presence likely dissuaded many protestors from coming to Tampa. As a result, a tiny fraction of the number of protestors that were expected actually showed up.

Objective:

This work presents our first steps in developing a Global Real-time Infectious Disease Surveillance System (GRIDDS) employing robust and novel in-fectious disease epidemiology models with real-time inference and pre/exercise planning capabilities for Lahore, Pakistan. The objective of this work is to address the infectious disease surveillance challenges (specific to developing countries such as Pakistan) and develop a collaborative capability for monitoring and managing outbreaks of natural or manmade infectious diseases in Pakistan.