Quantifying viral pandemic potential from experimental transmission studies.

In the past two decades, two pandemic respiratory viruses (H1N1 and SARS-CoV-2) have emerged via spillover from animal reservoirs. In an effort to avert future pandemics, surveillance studies aimed at identifying zoonotic viruses at high risk of spilling over into humans act to monitor the 'viral chatter' at the animal-human interface. These studies are hampered, however, by the diversity of zoonotic viruses and the limited tools available to assess pandemic risk. Methods currently in use include the characterization of candidate viruses using laboratory assays and experimental transmission studies in animal models. However, transmission experiments yield relatively low-resolution outputs that are not immediately translatable to projections of viral dynamics at the level of a host population. To address this gap, we present an analytical framework to extend the use of measurements from experimental transmission studies to generate more quantitative risk assessments. Specifically, we develop modeling approaches for estimating transmission parameters and gauging population-level emergence risk using within-host viral titer data from index and contact animals. To illustrate the use of these approaches, we apply them to two recently published influenza A virus (IAV) ferret transmission experiments: one using influenza A/California/07/2009 (H1N1pdm09) and one using influenza A/Hong Kong/1/1968 (H3N2). We find that, when controlling for viral titers, the H3N2 virus tends to be less transmissible than the H1N1 virus. Because of this difference in infectiousness and more robust replication of H1N1 in ferrets, we further find that the H1N1 virus has a higher projected reproduction number than the H3N2 virus and therefore more likely to cause an epidemic following introduction. Incorporating estimates of the generation interval for each virus, we find that the H1N1 virus has a higher projected epidemic growth rate than the H3N2 virus. The methods we present to assess relative pandemic risk across viral isolates can be used to improve quantitative risk assessment of other emerging viruses of pandemic concern.