Anticipating Viral Species Jumps: Bioinformatics and Data Needs

摘要

Viral species jumps (also called host jumps) occur when a virus acquires the ability to infect and spread among individuals of a new host species. Historical examples of animal viruses that jumped into human hosts include HIV, SARS coronavirus and influenza A virus. Globally, these viruses have exacted high socioeconomic and health costs. The ability to predict viral species jumps can reduce such costs by enabling swifter outbreak mitigation strategies and prevention of initial or secondary human infection. Currently, most emerging infectious disease surveillance efforts seek the ecological drivers behind spillover events - factors like climate, land use and population migrations driving infections that do not spread between humans. By contrast, we focus here on the evolutionary drivers behind species jumps - the genetic changes over time driving infections that spread efficiently among humans. We see an opportunity to apply field surveillance and laboratory data to better understand how viral species jumps occur. There are publicly available extant data that can be marshaled. To build a mechanistic framework of understanding, data must be integrated and accessible to users for analysis and modeling, as well as formulation and testing of hypotheses. In short, bioinformatics must be applied. To that end, the Defense Threat Reduction Agency's Advanced Systems and Concepts Office hosted a workshop that gathered computational biologists and information scientists to explore the types of data needed, the computational methods required, and suitable platforms to share information among interdisciplinary stakeholders.