tetano
Editor, Senior Moderator
Npj Viruses
. 2024;2(1):64.
doi: 10.1038/s44298-024-00074-w. Epub 2024 Dec 9. Optimal thresholds and key parameters for predicting influenza A virus transmission events in ferrets
Troy J Kieran[SUP] 1 [/SUP], Xiangjie Sun[SUP] 1 [/SUP], Taronna R Maines[SUP] 1 [/SUP], Jessica A Belser[SUP] 1 [/SUP]
Affiliations
Although assessments of influenza A virus transmissibility in the ferret model play a critical role in pandemic risk evaluations, few studies have investigated which virological data collected from virus-inoculated animals are most predictive of subsequent virus transmission to naïve contacts. We compiled viral titer data from >475 ferrets inoculated with 97 contemporary IAV (including high- and low-pathogenicity avian, swine-origin, and human viruses of multiple HA subtypes) that served as donors for assessments of virus transmission in the presence of direct contact (DCT) or via respiratory droplets (RDT). A diversity of molecular determinants, clinical parameters, and infectious titer measurements and derived quantities were examined to identify which metrics were most statistically supported with transmission outcome. Higher viral loads in nasal wash (NW) specimens were strongly associated with higher transmission frequencies in DCT, but not RDT models. However, viruses that reached peak titers in NW specimens early (day 1 p.i.) were strongly associated with higher transmission in both models. Interestingly, viruses with 'intermediate' transmission outcomes (33-66%) had NW titers and derived quantities more similar to non-transmissible viruses (<33%) in a DCT setting, but with efficiently transmissible viruses (>67%) in a RDT setting. Machine learning was employed to further assess the predictive role of summary measures and varied interpretation of intermediate transmission outcomes in both DCT and RDT models, with models employing these different thresholds yielding high performance metrics against both internal and external datasets. Collectively, these findings suggest that higher viral load in inoculated animals can be predictive of DCT outcomes, whereas the timing of when peak titers are detected in inoculated animals can inform RDT outcomes. Identification that intermediate transmission outcomes should be contextualized relative to the transmission mode assessed provides needed refinement towards improving interpretation of ferret transmission studies in the context of pandemic risk assessment.
Keywords: Influenza virus; Viral transmission.
. 2024;2(1):64.
doi: 10.1038/s44298-024-00074-w. Epub 2024 Dec 9. Optimal thresholds and key parameters for predicting influenza A virus transmission events in ferrets
Troy J Kieran[SUP] 1 [/SUP], Xiangjie Sun[SUP] 1 [/SUP], Taronna R Maines[SUP] 1 [/SUP], Jessica A Belser[SUP] 1 [/SUP]
Affiliations
- PMID: 39664046
- PMCID: PMC11628394
- DOI: 10.1038/s44298-024-00074-w
Although assessments of influenza A virus transmissibility in the ferret model play a critical role in pandemic risk evaluations, few studies have investigated which virological data collected from virus-inoculated animals are most predictive of subsequent virus transmission to naïve contacts. We compiled viral titer data from >475 ferrets inoculated with 97 contemporary IAV (including high- and low-pathogenicity avian, swine-origin, and human viruses of multiple HA subtypes) that served as donors for assessments of virus transmission in the presence of direct contact (DCT) or via respiratory droplets (RDT). A diversity of molecular determinants, clinical parameters, and infectious titer measurements and derived quantities were examined to identify which metrics were most statistically supported with transmission outcome. Higher viral loads in nasal wash (NW) specimens were strongly associated with higher transmission frequencies in DCT, but not RDT models. However, viruses that reached peak titers in NW specimens early (day 1 p.i.) were strongly associated with higher transmission in both models. Interestingly, viruses with 'intermediate' transmission outcomes (33-66%) had NW titers and derived quantities more similar to non-transmissible viruses (<33%) in a DCT setting, but with efficiently transmissible viruses (>67%) in a RDT setting. Machine learning was employed to further assess the predictive role of summary measures and varied interpretation of intermediate transmission outcomes in both DCT and RDT models, with models employing these different thresholds yielding high performance metrics against both internal and external datasets. Collectively, these findings suggest that higher viral load in inoculated animals can be predictive of DCT outcomes, whereas the timing of when peak titers are detected in inoculated animals can inform RDT outcomes. Identification that intermediate transmission outcomes should be contextualized relative to the transmission mode assessed provides needed refinement towards improving interpretation of ferret transmission studies in the context of pandemic risk assessment.
Keywords: Influenza virus; Viral transmission.