The Council of State and Territorial Epidemiologists (CSTE), in collaboration with the Centers for Disease Control and Prevention’s (CDC) National Syndromic Surveillance Program (NSSP), virtually convened the 2020 Syndromic Surveillance Symposium from November 17-19, 2020. The event was held during the following dates and times:
Analytics
Description
The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.
Objective
To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.
Description
The purpose of the National Collaborative for Bio-preparedness (NCB-P) is to enhance biosurveillance and situational awareness to better inform decision-making using a statewide approach. EMS represents a unique potential data source because it intersects with patients at the point of insult or injury, thus providing information on the timing and location of care. North Carolina uses a standardized EMS data collection system, the Prehospital Medical Information System (PreMIS), to collect information on EMS encounters across the state using the National EMS Information System (NEMSIS) template. Since NEMSIS is planned to be incorporated by EMS agencies in every state, an EMS-based approach to biosurveillance is extensible nationally.
Objective
To develop a statewide biosurveillance system based on emergency medical services (EMS) information which employs both symptom-based illness categorization and spatiotemporal analysis.
Referenced File
Description
The aerosol release of a pathogen during a bioterrorist incident may not always be caught on environmental sensors - it may be too small, may consist of a preparation that is coarse and heavy (and consequently precipitates quickly) or may simply have occurred in an uninstrumented location. In such a case, the first intimation of an event is the first definitive diagnosis of a patient. Being able to infer the size of the attack, its time, and the dose received has important ramifications for planning a response. Estimates drawn from such a short observation period will have limited accuracy, and hence establishing confidence levels (i.e., error bounds) on these estimates is an major concern. These estimates of outbreak characteristics can be also be used as initial conditions for epidemic models to predict the evolution of disease (along with error bounds in the predictions), in particular, communicable diseases in which the contagious period starts soon after infection (e.g., plague).
In this paper, we will consider anthrax and smallpox as our model pathogens. Since the contagious period of smallpox usually starts after the long incubation period (7–17 days), and the early epoch will consist only of index cases, we will model it as a non-contagious disease. Inputs will be obtained from simulated outbreaks as well as from the Sverdlovsk anthrax outbreak of 1979.
Objective
This paper presents a method that infers the number of infected people, the time of infection and the dose received from an aerosol release of a pathogen during a bioterrorism incident. Inputs into the inference process are the number of new diagnosed patients showing symptoms each day as observed over a short duration (3–4 days) during the early epoch of the outbreak.
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Description
Irregularly shaped spatial disease clusters occur commonly in epidemiological studies, but their geographic delineation is poorly defined. Most current spatial scan software usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, corresponding to varying degrees of penalization parameters imposed to the freedom of shape. Even when a fairly complete set of solutions is available, the choice of the most appropriate parameter setting is left to the practitioner, whose decision is often subjective.
Objective
We propose a novel approach to the delineation of irregularly shaped disease clusters, treating it as a multi-objective optimization problem. We present a new insight into the geographic meaning of the cluster solution set, providing a quantitative approach to the problem of selecting the most appropriate solution among the many possible ones.
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Description
The primary objective of this study is to assess the capability of an advanced text analytics tool that uses natural language processing techniques to extract important medical information collected as part of routine emergency room care (history, symptoms, vital signs, test results, initial diagnosis, etc.). This information will be automatically, accurately, and efficiently converted from unstructured text into use-able information, which can then be used to identify cases that are the result of a naturally occurring outbreak or bioterrorism event. This information would then be available to (1) communicate to the treating physician, and (2) message back to organizations aggregating data at a higher level, such as the Centers for Disease Control and Prevention (CDC) and the Department of Homeland Security (DHS).
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Description
The Los Angeles County (LAC) Bioterrorism Preparedness and Response Unit has made significant progress in automating the syndromic surveillance system. The surveillance system receives electronic data on a daily basis from different hospital information systems, then standardizes and generates analytical results.
OBJECTIVE
This article describes architecture, analytical method, and software applications used in automating the LAC syndromic surveillance system.
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Description
On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant.
Objective
To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system.
Description
A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida (Hillsborough, Pinellas, Manatee and Pasco) provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year’s pandemic. During the pandemic, high-level decison-makers wanted timely, credible forecasts as to the likely near-term course of the pandemic. The cumulative percentage of people who will be infected by the end of the epidemic can be estimated from the intrinsic reproductive number of the viral strain, its R0 , which can be measured early in the epidemic. If the current cumulative number of infections can be estimated, then one can determine what fraction of the eventual total number of infected people have already been infected.
Objective
To estimate the number of infections due to the novel 2009 influenza A/H1N1 virus corresponding to each ED visit for ILI in a four-county area of Florida. Knowing such ratios, one could (in future similar situations) estimate the cumulative number of infections due to a novel influenza virus in a population.
Description
The National Science and Technology Council, within the Executive Office of the President, established the Pandemic Prediction and Forecasting Science and Technology (PPFST) Working Group in 2013. The PPFST Working Group supports the US Predict the Next Pandemic Initiative, and serves as a forum to accelerate the development of federal infectious disease outbreak prediction and forecasting capabilities. Priorities include identification, evaluation, and integration of disparate biosurveillance and other data streams for prediction/forecasting; characterization of the decision context for US Government use of prediction/forecasting models; and development of a common US Government vision for federal prediction/forecasting capabilities. The Working Group comprises 18 federal departments and agencies, as well as the National Security Council, Office of Science and Technology Policy (OSTP), and Office of Management and Budget. OSTP, the Centers for Disease Control and Prevention, and the Department of Defense chair the Working Group.
Objective
To accelerate the development of US federal infectious disease outbreak prediction (i.e., identification of future time and place of a disease event) and forecasting (i.e., disease spread) capabilities.