Dengue Fever (DF) is a vector-borne disease of the flavivirus family carried by the Aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. Nearly 400 million people become infected each year, while roughly one-third of the world’s population live in areas of risk. Dengue fever has been endemic to Colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk.

Uncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.

Objective:

We present the EpiEarly, EpiGrid, and EpiCast tools for mechanistically-based biological decision support. The range of tools covers coarse-, medium-, and fine-grained models. The coarse-grained, aggregated time-series only data tool (EpiEarly) provides a statistic quantifying epidemic growth potential and associated uncertainties. The medium grained, geographically-resolved model (EpiGrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs.

In 2013, the Utah Department of Health (UDOH) began working with hospital and reference laboratories to implement electronic laboratory reporting (ELR) of reportable communicable disease data. Laboratories utilize HL7 message structure and standard terminologies such as LOINC and SNOMED to send data to UDOH. These messages must be evaluated for validity, translated, and entered into Utah’s communicable disease surveillance system (UT-NEDSS), where they can be accessed by local and state investigators and epidemiologists.