Antibodies of influenza A(H1N1)pdm09 virus in pigs' sera cross-react with other influenza A virus subtypes. A retrospective epidemiological interpretation of Norway's serosurveillance data from 2009-2017

Overview of Modern Commercial Kits for Laboratory Diagnosis of African Swine Fever and Swine Influenza A Viruses

Rapid and early detection of infectious diseases in pigs is important, especially for the implementation of control measures in suspected cases of African swine fever (ASF), as an effective and safe vaccine is not yet available in most of the affected countries. Additionally, analysis for swine influenza is of significance due to its high morbidity rate (up to 100%) despite a lower mortality rate compared to ASF. The wide distribution of swine influenza A virus (SwIAV) across various countries, the emergence of constantly new recombinant strains, and the danger of human infection underscore the need for rapid and accurate diagnosis. Several diagnostic approaches and commercial methods should be applied depending on the scenario, type of sample and the objective of the studies being implemented. At the early diagnosis of an outbreak, virus genome detection using a variety of PCR assays proves to be the most sensitive and specific technique. As the disease evolves, serology gains diagnostic value, as specific antibodies appear later in the course of the disease (after 7-10 days post-infection (DPI) for ASF and between 10-21 DPI for SwIAV). The ongoing development of commercial kits with enhanced sensitivity and specificity is evident. This review aims to analyse recent advances and current commercial kits utilised for the diagnosis of ASF and SwIAV.

Genetic Diversity of Type A Influenza Viruses Found in Swine Herds in Northwestern Poland from 2017 to 2019: The One Health Perspective

Influenza A viruses (IAV) are still a cause of concern for public health and veterinary services worldwide. With (-) RNA-segmented genome architecture, influenza viruses are prone to reassortment and can generate a great variety of strains, some capable of crossing interspecies barriers. Seasonal IAV strains continuously spread from humans to pigs, leading to multiple reassortation events with strains endemic to swine. Due to its high adaptability to humans, a reassortant strain based on "human-like" genes could potentially be a carrier of avian origin segments responsible for high virulence, and hence become the next pandemic strain with unseen pathogenicity. The rapid evolution of sequencing methods has provided a fast and cost-efficient way to assess the genetic diversity of IAV. In this study, we investigated the genetic diversity of swine influenza viruses (swIAVs) collected from Polish farms. A total of 376 samples were collected from 11 farms. The infection was confirmed in 112 cases. The isolates were subjected to next-generation sequencing (NGS), resulting in 93 full genome sequences. Phylogenetic analysis classified 59 isolates as genotype T (H1avN2g) and 34 isolates as genotype P (H1pdmN1pdm), all of which had an internal gene cassette (IGC) derived from the H1N1pdm09-like strain. These data are consistent with evolutionary trends in European swIAVs. The applied methodology proved to be useful in monitoring the genetic diversity of IAV at the human-animal interface.

Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

In order to quantify projections of disease burden and to prioritise disease control strategies in the animal population, good mathematical modelling of infectious disease dynamics is required. This article investigates the suitability of discrete-time Markov chain (DTMC) as one such model for forecasting disease burden in the Norwegian pig population after the incursion of influenza A(H1N1)pdm09 virus (H1N1pdm09) in Norwegian pigs in 2009. By the year-end, Norway's active surveillance further detected 20 positive herds from 54 random pig herds, giving an estimated initial population prevalence of 37% (95% CI 25-52). Since then, Norway's yearly surveillance of pig herd prevalence has given this study 11 years of data from 2009 to 2020 to work with. Longitudinally, the pig herd prevalence for H1N1pdm09 rose sharply to >40% in three years and then fluctuated narrowly between 48% and 49% for 6 years before declining. This initial longitudinal pattern in herd prevalence from 2009 to 2016 inspired this study to of test the steady-state discrete-time Markov chain model in forecasting disease prevalence. With the pig herd as the unit of analysis, the parameters for DTMC came from the initial two years of surveillance data after the outbreak, namely vector prevalence, first herd incidence and recovery rates. The latter two probabilities formed the fixed probability transition matrix for use in a discrete-time Markov chain (DTMC) that is quite similar to another compartmental model, the susceptible-infected-susceptible (SIS) model. These DTMC of predicted prevalence (DTMCP) showed good congruence (Pearson correlation = 0.88) with the subsequently observed herd prevalence for seven years from 2010 to 2016. While the DTMCP converged to the stationary (endemic) state of 48% in 2012, after three time steps, the observed prevalence declined instead from 48% after 2016 to 25% in 2018 before rising to 29% in 2020. A sudden plunge in H1N1pdm09 prevalence amongst Norwegians during the 2016/2017 human flu season may have had a knock-on effect in reducing the force of infection in pig herds in Norway. This paper endeavours to present the discrete-time Markov chain (DTMC) as a feasible but limited tool in forecasting the sequence of a predicted infectious disease's prevalence after it's incursion as an exotic disease.