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Surveillance Systems

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.

Submitted by teresa.hamby@d… on

The choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. Our work aims to characterize the performance of detectors based on outbreak types. We are using Bayesian networks (BN) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data.

Objective

To predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance.

Submitted by teresa.hamby@d… on

EPS is a comprehensive effort to complement other types of surveillance and provide early detection and situational awareness of significant endemic, zoonotic, and emerging diseases of livestock. The concept for EPS involves gathering syndromic and observational data from multiple animal health surveillance sources, including private practitioners, livestock markets, livestock harvest facilities, and veterinary diagnostic laboratories. A signal indicating a potential animal health event in one data stream can be corroborated in the other streams. For swine surveillance in the U.S., USDA-APHIS monitors the number of swine condemned for specific reasons. Likewise, industry practitioners share front-line clinical information within their practitioner network to detect anomalies. This case summary demonstrates the successful outcome of implementing an EPS pilot program through Federal and industry partnership.

Objective

To describe detection and response for an erysipelas outbreak in market swine in the United States (U.S.) using Food Safety and Inspection Service (FSIS) slaughter condemnation data, and coordination with the swine industry in an Enhanced Passive Surveillance (EPS) pilot project.

Submitted by teresa.hamby@d… on

The explosive use of social media sites presents a unique opportunity for developing alternative methods for understanding the health of the public. The near ubiquity of smartphones has further increased the volume and resolution of data that is shared through these sites. The emerging field of digital epidemiology has focused on methods to analyze and use this “digital exhaust” to augment traditional epidemiologic methods. When applied to the task of disease detection they often detect outbreaks 1-2 weeks earlier than their traditional counterpart. Many of these approaches successfully employ data mining techniques to detect symptoms associated with influenza-like illness. Others can identify the appearance of novel symptom patterns, allowing the ability to detect the emergence of a new illness in a population. However, behaviors that lead to increased risk for disease have not yet received this treatment.

Objective

Create an analysis pipeline that can detect the behavioral determinants of disease in the population using social media data.

Submitted by teresa.hamby@d… on

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.

Submitted by jababrad@indiana.edu on

Zimbabwe's National Health Laboratory Services faces multiple challenges related to inadequate financial support and skilled human resources, insufficient infrastructure, and inefficient tracking of clinical samples collected by health facilities. The slow turnaround time and poor management of the sample testing process, as well as delivery of results remain critical challenges. Compounding these problems further is a manual system for tracking large volumes of samples. This laborious and time-consuming process is inefficient for management of high amounts of incoming medical samples, frequently resulting in incomplete and inaccurate data. Additionally, health facilities are unable to monitor clinical samples and results in transit, leading to misplaced samples and missing results. Furthermore, although the laboratory service runs on a tiered network system - with lower level laboratories referring surveillance samples to higher level laboratories, processing of samples is not fulfilled promptly. The solutions to these challenges are divergent - sometimes even pointing in different directions. To this end, the Zimbabwe Ministry of Health and Child Care (MoHCC) has identified and integrated a LIMS to improve tracking of samples from the time of collection through results delivery.

Objective:

Understand the challenges that exist in the Zimbabwe health systems, that could be addressed through the integration of a Laboratory Information Management System (LIMS). Understand key aspects for consideration when selecting and adapting a LIMS in a resource limited setting. Showcase improvements in laboratory information management processes following adoption of a LIMS.

Submitted by elamb on

Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1. A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language. The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors. LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.

Objective:

LANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.

Submitted by elamb on

Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.

Objective:

Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.

Submitted by elamb on

In 2015, there were 212 million new cases of malaria, and about 429,000 malaria death, worldwide. African countries accounted for almost 90% of global cases of malaria and 92% of malaria deaths. Currently, malaria data are scattered across different countries, laboratories, and organizations in different heterogeneous data formats and repositories. The diversity of access methodologies makes it difficult to retrieve relevant data in a timely manner. Moreover, lack of rich metadata limits the reusability of data and its integration. The current process of discovering, accessing and reusing the data is inefficient and error-prone profoundly hindering surveillance efforts. As our knowledge about malaria and appropriate preventive measures becomes more comprehensive malaria data management systems, data collection standards, and data stewardship are certain to change regularly. Collectively these changes will make it more difficult to perform accurate data analytics or achieve reliable estimates of important metrics, such as infection rates. Consequently, there is a critical need to rapidly re-assess the integrity of data and knowledge infrastructures that experts depend on to support their surveillance tasks.

Objective:

Malaria is one of the top causes of death in Africa and some other regions in the world. Data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. Improving the interoperability of data sources through the use of shared semantics is a key consideration when designing surveillance systems, which must be robust in the face of dynamic changes to one or more components of a distributed infrastructure. Here we introduce a semantic framework to improve interoperability of malaria surveillance systems (SIEMA).

Submitted by elamb on

On April 14, 2016, British Columbia (BC)’s Provincial Health Officer declared a public health emergency due to a significant increase in drug-related overdoses and deaths in the Province. Despite the declaration, 161 suspected drug overdose deaths were reported across the Province in December 2016, a 137% increase over the number of deaths occurring in the same month of 2015. In response to the surge overdoses, Vancouver Coastal Health Authority (VCH), one of 5 health regions within BC, rapidly implemented a number of novel harm reduction initiatives. Overdose Prevention Sites (OPS) were opened on December 8, 2016. At these sites, people using illicit drugs are supervised by peers who can provide rapid intervention if an overdose occurs. The Mobile Medical Unit (MMU), a temporary state-of-art medical facility, was deployed on December 13, 2016 to reduce the congestion for the BC Ambulance Service (BCAS) and a major urban emergency department (ED). Following deployment of the MMU, services were transitioned to a permanent program at the Downtown Eastside Connections Clinic (DTES Connections) in the spring of 2017. DTES Connections was created to provide rapid access to addiction treatment. In order to keep pace with the rapidly increasing number of novel harm reduction initiatives, enhanced surveillance programs were implemented at VCH to monitor and evaluate these innovative harm reduction activities, including development of new surveillance programs for the MMU, OPS and DTES Connections, along with existing routine surveillance system from EDs and a Supervised Injection Site (Insite).

Objective:

To describe the use of multiple data sources to monitor overdoses in near real-time in order to evaluate response to the provincial overdose emergency

Submitted by elamb on
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