Data fusion and machine learning to identify threat vectors for the Zika virus and classify vulnerability

With the continued spread of the zika virus in the United States in both Florida and Virginia, increased public awareness, prevention and targeted prediction is necessary to effectively mitigate further infection and propagation of the virus throughout the human population. The goal of this project is to utilize publicly accessible data and HPC resources coupled with machine learning algorithms to identify potential threat vectors for the spread of the zika virus in Texas, the United States and globally by correlating available zika case data collected from incident reports in medical databases (e.g., CDC, Florida Department of Health) with known bodies of water in various earth science databases (e.g., USGS NAQWA Data, NASA ASTER Data, TWDB Data) and by using known mosquito population centers as a proxy for trends in population distribution (e.g., WHO, European CDC, Texas Data) while correlating historical trends in the spread of other mosquito borne diseases (e.g., chikungunya, malaria, dengue, yellow fever, west nile, etc.). The resulting analysis should refine the identification of the specific threat vectors for the spread of the virus which will correspondingly increase the effectiveness of the limited resources allocated towards combating the disease through better strategic implementation of defense measures. The minimal outcome of this research is a better understanding of the factors involved in the spread of the zika virus, with the greater potential to save additional lives through more effective resource utilization and public outreach.