Recent events such as pandemic influenza A (H1N1)pdm09 have demonstrated how mutations in a viral genome can greatly impact disease spread and population health risk. Thus, there is now a greater need to merge viral genetics within state health agency surveillance practice. This is particularly relevant for zoonotic viruse that are transmittable between animals and humans such as influenza, rabies, and West Nile Virus. As an added complexity, there are many potential drivers of virus transmission that need to be considered including climate, population and travel, and ultimately, genetic polymorphisms in the virus itself. Zoonotic disease surveillance at the state level is most often performed using data that originates from passive case reporting by laboratories or clinicians rather than secondary data from resources such as GenBank. While these data are sufficient for federal reporting purposes and basic trend analysis, they only measure the number of suspected or confirmed cases and not the genetic characteristics of the virus. When states and federal agencies do use genotyping, it is often limited to certain pathogens (mostly bacteria) and only for samples that are reported through passive surveillance or during outbreak investigations. The omission of secondary viral genetic data limits the types of analysis by state health agencies. For example, current reportable disease data do not enable epidemiologists to determine the origin of a particular viral strain, trace how it has spread, or identify climate, population, and genetic factrs enabling it to propagate. In this study, we will develop and evaluate an integrated bioinformatics framework to supplement current zoonotic disease surveillance approaches at state health agencies. We hypothesize that a framework that properly merges viral genetic data with climate, population, and travel data can accurately predict the timing of initial peaks of seasonal epidemics caused by zoonotic viruses. Health agencies can then use these trends to prioritize control measures and reduce morbidity and mortality. In addition, we will address the barriers to health agency utilization of bioinformatics resources and secondary data by developing an online portal for accessing and querying of complex viral genetic models. We will measure the perceived usefulness of information from our framework as part of our long-term goal of utilization and adoption by health agencies.
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 seasona trends of zoonotic virus epidemics and communicate these findings to different health agencies.
We will develop and evaluate a bioinformatics system that predicts seasonal patterns of zoonotic epidemics while considering the impact of climate and populations differences. We will also examine the usefulness of the information from a bioinformatics system to support public health surveillance. A better understanding of the complex role between climate, population, and virus will enable health agencies to target the most at-risk areas resulting in a reduction of morbidity and mortality from zoonotic infectious diseases.
