Two novel human-like H3N2 influenza A virus strains, A/swine/Oklahoma/65980/2017 (H3N2) and A/swine/Oklahoma/65260/2017 (H3N2), were isolated from porcine samples submitted to the Iowa State University Veterinary Diagnostic Laboratory in the United States.
Four distinct species of human coronaviruses (HCoVs) circulate in humans. Despite the recent attention due to SARS-CoV-2, a comprehensive understanding of the molecular epidemiology and genomic evolution of HCoVs remains unclear. Here, we employed primary differentiated human nasal epithelial cells for the successful isolation and genome sequencing of HCoVs derived from two retrospective cohorts in Singapore and Tanzania. Phylodynamic inference shows that HCoV-229E and HCoV-OC43 were subject to stronger genetic drift and reduced purifying selection from the early 2000s onwards, primarily targeting spike Domain A and B. This resulted in increased lineage diversification, coinciding with a higher effective reproductive number (Re>1.0). However, HCoV-NL63 and HCoV-HKU1 experienced weaker genetic drift and selective pressure with prolonged regional persistence. Our findings suggest that HCoV-229E and HCoV-OC43 viruses are adept at generating new variants and achieving widespread intercontinental dissemination driven by continuous genetic drift, recombination, and complex migration patterns.
Abstract The 2009 H1N1 pandemic (pdm09) lineage is a major component of the H1 influenza A virus (IAV) that causes seasonal outbreaks annually. Since its introduction in the 2009–10 season, this lineage has evolved into distinct, successive clades in humans. Predicting the fitness of influenza clades is essential to forecasting future prevalence, providing a critical opportunity to develop a response to mitigate infection. The relative fitness of pdm09 lineages was retrospectively inferred via relative reproduction rate (RRe) through RelRe, a programme that implements a renewal equation to estimate the relative difference in reproduction number between cocirculating clades. For this analysis, pdm09 lineage sequences from the USA, collected from 2017 to 2023 in both human and swine hosts, were downloaded from public databases. Clade designations were assigned using Nextclade. Human case count data were divided by each influenza season, and the RRe was estimated at 3-month intervals. The RRe was then used to forecast clade frequency 90 days into the future, and the predictions were compared to the historical data. The highest predicted frequency at 90 days corresponded to the most frequently detected lineage in 9 out of 13 predictions (69%). The pdm09 lineage plays an important role at the human–swine influenza interface. Bayesian inference using both human and swine data indicated unequal transmission rates of the pdm09 lineage, with 53–79 noted transmissions from human to swine and 0–2 in reverse using the available genetic data. Metadata analysis revealed that new clades of pdm09 in humans were typically detected in swine as early as ~8–20 months after clade emergence in humans. Understanding RRe and the fitness of contemporary IAV strains enables the identification of high-risk reverse-zoonotic strains and provides critical time for responding to emergent human clades.
Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion, however quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing risk have the potential to facilitate better informed choices. Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk, and quantify the impact of biosecurity practices on disease risk at individual farms. Quantifying the variable impact on predicted risk 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to: the turnover and number of employees; the surrounding density of swine premises and pigs; the sharing of trailers; distance from the public road; and production type. In addition, the development of individualized biosecurity assessments provides the opportunity to guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it potential to be applied to wider areas of biosecurity benchmarking, to address weaknesses in other livestock systems and industry relevant diseases.
