An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico.

Ensemble method for dengue prediction.

Background: In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data.

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

Background: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.

Methods: We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea.

Preliminary development of a fiber optic sensor for measuring bilirubin.

Preliminary development of a fiber optic bilirubin sensor is described, where an unclad sensing portion is used to provide evanescent wave interaction of the transmitted light with the chemical environment. By using a wavelength corresponding to a bilirubin absorption peak, the Beer-Lambert Law can be used to relate the concentration of bilirubin surrounding the sensing portion to the amount of absorbed light. Initial testing in vitro suggests that the sensor response is consistent with the results of bulk absorption measurements as well as the Beer-Lambert Law.

Prediction of high incidence of dengue in the Philippines.

Background: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines.

Methods: Model inputs were chosen based on studies indicating variables that may impact dengue incidence.

A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data.

Background: Dengue is the most common arboviral disease of humans, with more than one third of the world's population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy.

Developing open source, self-contained disease surveillance software applications for use in resource-limited settings.

Background: Emerging public health threats often originate in resource-limited countries. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005, which call for significantly increased reporting and response capabilities for all signatory nations. Electronic biosurveillance systems can improve the timeliness of public health data collection, aid in the early detection of and response to disease outbreaks, and enhance situational awareness.

A meta-analysis of point-of-care laboratory tests in the diagnosis of novel 2009 swine-lineage pandemic influenza A (H1N1).

This paper reviews 14 published studies describing performance characteristics, including sensitivity and specificity, of commercially available rapid, point-of-care (POC) influenza tests in patients affected by an outbreak of a novel swine-related influenza A (H1N1) that was declared a pandemic in 2009. Although these POC tests were not intended to be specific for this pandemic influenza strain, the nonspecialized skills required and the timeliness of results make these POC tests potentially valuable for clinical and public health use. Pooled sensitivity and specificity for the POC tests studied were 68% and 81%, respectively, but published values were not homogeneous with sensitivities and specificities ranging from 10% to 88% and 51% to 100%, respectively.

Research priorities for syndromic surveillance systems response: consensus development using nominal group technique.

Objective: To identify a set of fundable and practically feasible research priorities in the field of syndromic surveillance response on the basis of expert consensus.

Methods: The nominal group technique was used to structure an expert panel meeting in February 2009. Eleven national experts participated in the meeting, representing health departments at the city, county, state, and federal levels as well as academia and the military.

A survey of usage protocols of syndromic surveillance systems by state public health departments in the United States.

Objective: To broadly describe current syndromic surveillance systems in use throughout the United States and to provide basic descriptive information on responses to syndromic system signals.

Methods: Cross-sectional survey (telephone and e-mail) of state epidemiologists in all 50 states and the District of Columbia.

Results: Forty-one states participated in the survey for a response rate of 80 percent.

Framework for the development of response protocols for public health syndromic surveillance systems: case studies of 8 US states.

Objectives: To describe current syndromic surveillance system response protocols in health departments from 8 diverse states in the United States and to develop a framework for health departments to use as a guide in initial design and/or enhancement of response protocols.

Methods: Case study design that incorporated in-depth interviews with health department staff, textual analysis of response plans, and a Delphi survey of syndromic surveillance response experts.

Results: All 8 states and 30 of the 33 eligible health departments agreed to participate (91% response rate).

Enhancing Disease Surveillance Event Communication Among Jurisdictions.

Automated disease surveillance systems are becoming widely used by the public health community. However, communication among non-collocated and widely dispersed users still needs improvement. A web-based software tool for enhancing user communications was completely integrated into an existing automated disease surveillance system and was tested during two simulated exercises and operational use involving multiple jurisdictions.

Bridging radiology and public health: the emerging field of radiologic public health informatics.

Radiology and public health have an emerging opportunity to collaborate, in which radiology's vast supply of imaging data can be integrated into public health information systems for epidemiologic assessments and responses to population health problems. Fueling the linkage of radiology and public health include (i) the transition from analog film to digital formats, enabling flexible use of radiologic data; (ii) radiology's role in imaging across nearly all medical and surgical subspecialties, which establishes a foundation for a consolidated and uniform database of images and reports for public health use; and (iii) the use of radiologic data to characterize disease patterns in a population occupying a geographic area at one time and to characterize disease progression over time via follow-up examinations. The backbone for this integration is through informatics projects such as Systematized Nomenclature of Medicine Clinical Terms and RadLex constructing terminology libraries and ontologies, as well as algorithms integrating data from the electronic health record and Digital Imaging and Communications in Medicine Structured Reporting.

Pediatric patient asthma-related emergency department visits and admissions in Washington, DC, from 2001-2004, and associations with air quality, socio-economic status and age group.

Background: The District of Columbia (DC) Department of Health, under a grant from the US Centers for Disease Control and Prevention, established an Environmental Public Health Tracking Program. As part of this program, the goals of this contextual pilot study are to quantify short-term associations between daily pediatric emergency department (ED) visits and admissions for asthma exacerbations with ozone and particulate concentrations, and broader associations with socio-economic status and age group.

Methods: Data included daily counts of de-identified asthma-related pediatric ED visits for DC residents and daily ozone and particulate concentrations during 2001-2004.