Improving risk assessment of the emergence of novel influenza A viruses by incorporating environmental surveillance

Authors: PEPIN, KIM - Animal And Plant Health Inspection Service (APHIS); HOPKEN, MATTHEW - Colorado State University; SHRINER, SUE - Animal And Plant Health Inspection Service (APHIS); Spackman, Erica; ABDO, ZAID - Colorado State University; PARRISH, COLIN - Cornell University; 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: 8/12/2019
Citation: Pepin, K., Hopken, M., Shriner, S., Spackman, E., Abdo, Z., Parrish, C., Riley, S., Lloyd-Smith, J., Piaggio, A. 2019. Improving risk assessment of the emergence of novel influenza A viruses by incorporating environmental surveillance. Philosophical Transactions of the Royal Society B. 374(1782):1-10. https://doi.org/10.1098/rstb.2018.0346.
DOI: https://doi.org/10.1098/rstb.2018.0346

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 influenza A viruses (IAV) generate genetic novelty. Reassortment is an important driver of host jumps and is widespread according to retrospective surveillance studies. However, predicting the epidemiological risk of reassortant emergence in novel hosts from surveillance data remains challenging. IAV strains persist and co-occur in the environment, promoting co-infection during environmental transmission. These conditions offer opportunity to understand reassortant emergence in reservoir and spillover hosts. Specifically, environmental RNA could provide rich information for understanding the evolutionary ecology of segmented viruses, and transform our ability to quantify 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, experimental ecology and epidemiological modelling are well positioned to address these challenges. Coupling quantitative disease models and natural transmission studies with new molecular technologies, such as deep-mutational scanning and single-virus sequencing of environmental samples, should dramatically improve our understanding of viral co-occurrence and reassortment. We define observable risk metrics for emerging molecular technologies and propose a conceptual research framework for improving accuracy and efficiency of risk prediction. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.