Description

In this study, we will develop and evaluate an integrated bioinformatics system for surveillance that predicts seasonal trends across different zoonotic viruses. We will address the barriers to health agency utilization of bioinformatics resources by developing an online portal to simplify access and querying of complex models. In addition, we will measure the perceived usefulness of information from our bioinformatics system as a precursor for future utilization by state health agency epidemiologists.

In Aim 1, we will develop an automated bioinformatics system that models virus diffusion while testing the significance of climate, population, and genetic predictors. As part of this effort, we will provide a publically available Web portal for health agencies and other users to access our results, and run their own models. In Aim 2, we will use our platform to identify significant climate, population, and genetic predictors of diffusion across different zoonotic viruses including influenza and WNV. In Aim 3, we will evaluate the accuracy of a bioinformatics system that uses statistically significant climate, population, and genetic predictors to identify seasonal trends of zoonotic virus epidemics and communicate these findings to different health agencies.

We hypothesize that a bioinformatics system that integrates climate, population, and genetic predictors for phylogeography will accurately predict, at the beginning of each season, the timing of initial epidemic peaks of virus spread as verified at the end of the season by observed data from federal surveillance programs.
StatusFinished
Effective start/end date4/6/153/31/19

Funding

  • HHS: National Institutes of Health (NIH): $1,118,049.00

Fingerprint

Zoonoses
Computational Biology
Climate
Population Genetics
Population
Viruses
Health
Phylogeography
Orthomyxoviridae