|Pepin, Kim - Animal And Plant Health Inspection Service (APHIS)|
|Hopken, Matthew - Colorado State University|
|Shriner, Sue - Animal And Plant Health Inspection Service (APHIS)|
|Abdo, Zaid - Colorado State University|
|Munster, Vincent - Rocky Mountain Laboratories National Institute Of Allergy And Infectious Diseases (NIAID)|
|Parrish, Colin - Cornell University - New York|
|Riley, Steven - Imperial College|
|Lloyd-smith, James - University Of California|
|Piaggio, Antoinette - Animal And Plant Health Inspection Service (APHIS)|
Submitted to: Philosophical Transactions of the Royal Society B
Publication Type: Review Article
Publication Acceptance Date: 3/12/2019
Publication Date: N/A
Interpretive Summary: Avian influenza is able to adapt to new conditions and new hosts through rapid genetic changes. One of the most important genetic changes is for the virus to swap genes with other influenza viruses. Measuring this phenomenon is challenging, but as new tools for analyzing genetic data have been developed in recent years, it is becoming possible. These new tools provide a highly detailed picture of genetic data that can be used to look at avian influenza viruses in a natural environment, such as wild waterfowl habitats. The information can be compiled and applied to understanding how the virus spreads and what makes some virus strains able to persist longer. The ultimate goal is to create models of how influenza behaves in different host habitat system and aid prevention and control by predicting for how the virus will spread to domestic birds.
Technical Abstract: Reassortment is an evolutionary mechanism by which segmented ribonucleic acid (RNA) viruses generate evolutionary novelty, an important driver of host jumps. While genomic surveillance technologies can identify reassortment events, predicting reassortant emergence in novel hosts (epidemiological risk) from surveillance data remains challenging. RNA viruses that are spread primarily through environmental transmission (e.g., avian influenza viruses) present an opportunity to understand reassortant emergence in reservoir and spillover hosts because environmental transmission allows viral strains to aggregate, persist, and co-infect hosts. Thus, environmental RNA could provide rich information for understanding the evolutionary ecology of segmented viruses, and transforming our ability to predict epidemiological risk to spillover hosts. However, significant challenges with recovering and interpreting genomic RNA from the environment have impeded progress towards predicting reassortant emergence from environmental surveillance data. We discuss how the fields of genomics and epidemiological modeling are well-positioned to overcome these challenges. When coupled with quantitative disease models, the application of new genomic technologies such as single-virus sequencing and deep-mutational scanning to environmental samples, could dramatically improve the accuracy and efficiency of risk prediction. We define risk metrics that can be observed using the cutting-edge genomic technology, and propose a conceptual research framework for predicting epidemiological risk.