Implementation of big-data integration analysis strategies to predict host-competence for vector-borne diseases

The comprehensive goal for this project is to implement a big-data integrative analysis strategy to predict host-competence in insect vectors that transmit infectious diseases.

There has recently been an emergence of widespread, vector-borne epidemics, including the zika outbreak in 2015-16 and the current chikungunya manifestation in parts of South America.

Additionally, leishmania, malaria and other vector-borne parasitic diseases that are currently endemic in parts of South America are predicted to increase in cases and occupy larger regions with increased temperatures due to global warming.

In lieu of ongoing climate change, widespread vector-borne diseases are becoming an increasing threat across Latin America, the Caribbean, as well as Africa and large regions of Asia, and tools to monitor transmission potential are becoming tremendously important.

With rapid advances in data-driven science and the massive production of NGS data we have reached the potential of performing informative metadata analyses.

The ultimate goal of this study is to uncover genetic information that enables the generation of molecular and computational tools with the capacity of defining vector competence linked to vector-borne diseases.

We further anticipate that the tools will be modifiable to encompass studies of vector-competence linked to a broad range of disease, which in light of global warming, have the potential of causing epidemics outside the regions where they are currently endemic.