BACKGROUND The increasing frequency and intensity of dengue outbreaks in endemic and non-endemic countries requires a rational, evidence based response. To this end, we aimed to collate the experiences of a number of affected countries, identify strengths and limitations in dengue surveillance, outbreak preparedness, detection and response and contribute towards the development of a model conti...

BACKGROUND Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable...

BACKGROUND We hypothesize that epidemics around their onset tend to affect primarily a well-defined subgroup of the overall population that is for some reason particularly susceptible. While the vulnerable cohort is often well described for many human diseases, this is not the case for instance when we wish to detect a novel computer virus. Clustering may be used to define the subgroups that wi...

In the field of diagnostic microbiology, rapid molecular methods are critically important for detecting pathogens. With rapid and accurate detection, preventive measures can be put in place early, thereby preventing loss of life and further spread of a disease. From a preparedness perspective, early detection and response are important in order to minimize the consequences. During the past 2 de...

and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Daniel Zeng, Hsinchun Chen, Cecil Lynch, Millicent Eidson, and Ivan Gotham 1 Management Information Systems Department, Eller College of Management, University of Arizona, Tucson, Arizona 85721; 2 Division of Medical Informatics, School of Medicine, University of California, Davis, California 95616; also with California Department of Health Services; 3 New York State Department of Health, Alban...

We describe a method to improve detection of disease outbreaks in pre-diagnostic time series data. The method uses multiple forecasters and learns the linear combination to minimize the expected squared error of the next day's forecast. This combination adaptively changes over time. This adaptive ensemble combination is used to generate a disease alert score for each day, using a separate multi...

Objective To develop a web-enabled Digital Disease Detection Dashboard (D4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. D4 is a user-friendly, cloudenabled, and R Shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of...