BioSurveillance

The sanitary-epidemiological monitoring system in Kazakhstan is passive and statistical in nature. Due to the modern threats, activation and emergence of new and recurring diseases (corona virus, Ebola, etc.) it is vital to transition from current epidemiological surveillance approaches to new prognostic, epi risk probability assessment, and bio risk management technologies, and in addition to urgent response develop preventive measures procedures to minimize the consequences of potential epi outbreaks.

Objective

Development of a concept for sanitary-epidemiological monitoring system reinforcement in Kazakhstan based on the deployed electronic surveillance system, risk assessment and management approach, and establishment of a Situational Center. 

Evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity. In many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden. However, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance.

Objective

Improve situational awareness for influenza by combining multiple data sources to predict influenza outbreaks in at-risk populations.

Many methods to detect outbreaks currently exist, although most are ineffective in the face of real data, resulting in high false positivity. More complicated methods have better precision, but can be difficult to interpret and justify. Praedico™ is a next generation biosurveillance application built on top of a Hadoop High Performance Cluster that incorporates multiple syndromic surveillance methods of alerting, and a machine-learning (ML) model using a decision tree classifier  evaluating over 100 different signals simultaneously, within a user friendly interface.

Objective

To compare syndromic surveillance alerting in VA using Praedico™ and ESSENCE.

The National Biosurveillance Integration Center (NBIC) has the responsibility to integrate, analyze, and share the nation’s biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. In addition to its interagency partners, NBIC supports the Office of Health Affairs and DHS components responsible for safeguarding U.S. ports of entry. More than 150 U.S. international airports process an estimated two billion passengers and 50 million metric tons of cargo arriving in the U.S. from more than 1,000 international airports located outside the U.S. Entry and customs screening are points where travelers from international destinations pass; a logical location for assessing health of incoming travelers in order to identify and control import of diseases of emerging diseases. NBIC examined peer-reviewed literature, region-specific disease spectrum/frequency, and air travel patterns to assess options for ports of entry health screening as well as the challenges and potential benefits for active screening programs.

Objective

NBIC analysts evaluated the options and effectiveness of airport symptom-based health screening programs available during emerging disease outbreaks occurring outside the U.S.

The National Biosurveillance Integration Center (NBIC) has the responsibility to integrate, analyze, and share the nation’s biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts.

Objective

To evaluate different government and commercial air travel route and volume data sources for utility in determining likely points of arrival and subsequent spread of communicable diseases originating from outbreaks outside the United States.

Wholesome food in adequate quantities is essential to human beings for their existence. However, diseases spread due to contaminated food are a common problem throughout the world and an important cause of reduced economic productivity. Food borne illness can, therefore, be considered a major international health problem and a significant cause of economic loss. Approximately 10 to 20% of food-borne disease outbreaks cause due to contamination by the food handlers. In Sri Lanka, information about food hygiene practices in plantation sector is scarce. Therefore, this study was designed as a preliminary study to identify hygiene practices in food processing in the plantation sector for the establishment of a surveillance system in Sri Lanka.

Objective

To develop a food hygiene surveillance system to improve food safety measures within food establishments in the plantation sector of Sri Lanka

The Joint Incentive Fund (JIF) Authorization creates innovative DoD/VA sharing initiatives. In 2009, DoD and VA commenced a biosurveillance JIF project whose principle objectives include improved situational awareness of combined VA/ DoD populations 1 and determining the optimal business model allowing both agency biosurveillance programs to operate more efficiently by: 1) consolidating information technology assets; 2) targeting enhanced collaboration for improved public health outcomes; and 3) improving buying power, and return on investment. We analyzed various interoperability models aimed at biosurveillance data sharing, asset consolidation and enhanced collaboration. Potential end states to be evaluated include maintaining separate Departmental systems, bidirectional exchange of data to separately managed systems, consolidation of data within one Department and shared access to a common system, consolidation of data in a neutral repository accessed by separately run legacy systems, or a custom developed biosurveillance solution utilizing a common data repository.

Objective

Determine an optimal course of action for achieving a more mission and cost-effective model for implementing combined or collaborative biosurveillance across the Departments of Veterans Affairs (VA) and Defense (DoD).

The CDC provides data on incidences of diseases on its website (https://data.cdc.gov/). Data is available at national, regional, and state levels, and is uploaded to the CDC’s website on a weekly basis. The CDCPlot web application (available at https://michaud.shinyapps.io/ CDCPlot/), built using the Shiny package in R, provides a quick and user-friendly method of visualizing this data. Users are able to the select timeframes, locations, and diseases which they wish to view, and plots are produced. There is an optional alert threshold, which will alert users when a disease increases significantly from one week to the next. In addition, CDCPlot provides visualizations of CDC data on Pneumonia and Influenza mortality.

Objective

To demonstrate the current features and functionality of the CDCPlot application, and to introduce potential new features of the application. 

Emerging disease clusters must be detected in a timely manner so that necessary remedial action can be taken to prevent the spread of an outbreak. The Exponentially Weighted Moving Average method (EWMA) is a particularly popular method, and has been utilized for disease surveillance in the United States.

A spatio-temporal EWMA statistic is proposed for on-line disease surveillance over multiple geographic regions. To capture spatial association, disease counts of neighboring regions are pooled together, similar to a method originally proposed by Raubertas for a different control chart. Also to increase statistical power in testing multiple EWMA statistics simultaneously, false discovery rate (FDR) is used instead of the traditional family-wise error rate (FWER).

Objective

To propose a computationally simple and a fast algorithm to detect disease outbreaks in multiple regions