Economic Modeling of Syndromic Surveillance Systems -- A Roundtable Discussion on Association of state and Territorial Health Official's (ASTHO) Investment Decision Model

One of ASTHO’s key goals is to help its jurisdictions meet member needs for technical assistance, including making informed decisions about their syndromic surveillance options. To help them make such decisions, ASTHO worked with Booz Allen to create a decision analysis model, which factors in both a Value of Information (VOI) model and a Return on Investment (ROI) model. The model provides a dashboard of its outputs, which is a simple, easy-to-understand comparative view of multiple syndromic surveillance investment scenarios.


October 05, 2017

An Early Warning Influenza Model using Alberta RealTime Syndromic Data (ARTSSN)

Standardized electronic pre-diagnostic information is routinely collected in Alberta, Canada. ARTSSN is an automated real-time surveillance data repository able to rapidly refresh data that include school absenteeism information, calls about health concerns from Health Link Alberta; a provincial telephone service for health advice and information, and emergency department visits categorized by standardized chief complaint. Until recently, real-time ARTSSN data for public health surveillance and decision making has been underutilized.


October 24, 2017

Modelling Based Estimates for Severe Pneumonia and Pneumonia Deaths in Indian States

The Child Health Epidemiology Reference Group (CHERG) has predicted around 43 million pneumonia cases in India. It is recognized that for huge nation like India, which accounts for 23% of global pneumonia burden, the national estimates may hide regional disparities. In this context, we have generated Indian state specific burden of severe pneumonia, pneumococcal pneumonia and pneumonia deaths through use of mathematical model.


November 22, 2017

Surveillance of Surveillance: Inventorying Gaps and Commonalities Across the Universe of Surveillance Systems

Health surveillance systems provide important functionalities to detect, monitor, respond, prevent, and report on a variety of conditions across multiple owners. They offer important capabilities, with some of the most fundamental including data warehousing and transfer, descriptive statistics, geographic analysis, and data mining and querying. We observe that while there is significant variety among surveillance systems, many may still report duplicative data sources, use basic forms of analysis, and provide rudimentary functionality.


December 03, 2017

Assessing the Impact of Climate Change and Land Use Variation on Microbial Transport Using Watershed Scale-modeling

The scientific community accepts that global climate change (CC) will affect the dispersion of microbial organisms in the environment. Risks posed by the transport of these organisms to future communities may be very different than those posed today. A shift in health risks may also be linked to climate driven land-use change, which may alter both microbial loadings to receiving waters and human exposure pathways. Uncertainty surrounding microbial fate and transport renders the assessment of CC effects on waterborne pathogens complex and difficult to forecast.

August 22, 2018

Use of Syndromic Surveillance Information for Expanded Assessment of Wildfire Disaster

Syndromic surveillance information can be a useful for the early recognition of outbreaks, acute public health events and in response to natural disasters. Inhalation of particulate matter from wildland fire smoke has been linked to various acute respiratory and cardiovascular health effects. Historically, wildfire disasters occur across Southern California on a recurring basis. During 2003 and 2007, wildfires ravaged San Diego County and resulted in historic levels of population evacuation, significant impact on air quality and loss of lives and infrastructure.

October 12, 2017

Automated Surveillance of Outpatients with Pneumonia: A Performance Evaluation

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.


January 24, 2018

Modeling Baseline Shifts in Multivariate Disease Outbreak Detection

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.


June 25, 2018

The National Operational Epidemiological Modeling Process

Participants will be provided with an overview of a study to determine the requirements of a national operational modeling process, including the study, methodology, and key findings. These include an overview of the current operational epidemiological modeling landscape, a summary of recommendations for the establishment of a national operational epidemiological modeling process, and recommendations for its implementation.

One Certified in Public Health (CPH) recertification credit will be available for attending this webinar and completing a short post-presentation evaluation.

October 18, 2017

Using Cultural Modeling to Inform a NEDSS-Compatible System Functionality Evaluation

The National Notifiable Disease Surveillance System (NNDSS) comprises many activities including collaborations, processes, standards, and systems which support gathering data from US states and territories. As part of NNDSS, the National Electronic Disease Surveillance System (NEDSS) provides the standards, tools, and resources to support reporting public health jurisdictions (jurisdictions). The NEDSS Base System (NBS) is a CDC-developed, software application available to jurisdictions to collect, manage, analyze and report national notifiable disease (NND) data.

July 13, 2018


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