Analytics, Machine Learning & NLP -- use in BioSurveillance and Public Health practice

Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today’s applications.

September 25, 2017

The Public Health Community Platform: Shared Resources For Enterprise Solutions

Public health is at a precipice of increasing demand for the consumption and analysis of large amounts of disparate data, the centralization of local and state IT offices, and the compartmentalization of programmatic technology solutions. Public health informatics needs differ across programmatic areas, but may have commonalities across jurisdictions.

December 12, 2017

Ratio of Excess ED ILI Visits to Seroprevalence, Influenza A/H1N1 Infection, FL, 2009

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.

December 28, 2017

Assessing the Work Practices and Information Needs of Disease Investigators

Investigation of cases, clusters, and outbreaks of infectious disease is a complex process requiring substantial support from protocols, distributed and cooperative work, and information systems. We set out to identify public health information needs, the types of data required to meet these needs, and the potential alignment with visualizations of this data.


September 25, 2017

Adjustment for Baseline Level of Dengue Cases Due to Increased Testing in Singapore

Dengue is endemic in Singapore, with epidemics of increasing magnitude occurring on a six-year cycle in 1986/7, 1992, 1998, 2004/5, 2007 and 2013. The incidence per 100,000 population ranged from 87.2 to 105.6 in 2009-20121 , and surged to 410.6 in 2013. The mean weekly number of dengue cases over a five-year period provides an indication of the baseline level. We illustrate an adjustment that has been made to the computation of the baseline level due to increased testing for dengue in 2013.


October 23, 2017

Real-time Forecasting of the 2014 Dengue Fever Season in Thailand

Dengue is a major cause of morbidity in Thailand. Annual outbreaks of varying sizes provide a particular challenge to the public health system because treatment of severe cases requires significant resources. Advanced warning of increases in incidence could help public health authorities allocate resources more effectively and mitigate the impact of epidemics.


To develop a statistical model for dengue fever surveillance that uses data from across Thailand to give early warning of developing epidemics.

December 29, 2017

Assessment of National Poison Data System Algorithms to identify Public Health Events

NPDS is a near real-time surveillance system and national database operated by the American Association of Poison Control Centers. NPDS receives records of all calls made to the 55 regional US poison centers (PCs). The Centers for Disease Control and Prevention (CDC) use NPDS to 1) provide public health surveillance for chemical, radiological and biological exposures and illnesses, 2) identify early markers of chemical, radiological, and biological incidents, and 3) find potential cases and enhance situational awareness during a known incident.

September 25, 2017

Advancing Epidemic Prediction and Forecasting: A New US Government Initiative

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.

October 23, 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

Detecting Outbreaks in Time-Series Data with RecentMax

We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.

October 03, 2017


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