Occurrence and spread of influenza A(H1N1)pdm09 virus infection in Norwegian pig herds based on active serosurveillance from 2010 to 2014

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in colombia

Biosecurity protocols (BP) and good management practices are key to reduce the risk of introduction and transmission of infectious diseases into the pig farms. In this observational cross-sectional study, survey data were collected from 176 pig farms with inventories over 100 sows in Colombia. We analyzed a complex survey dataset to explore the structure and identify clustering patterns using Multiple Correspondence Analysis (MCA) of swine farms in Colombia, and estimated its association with Influenza A virus detection. Two principal dimensions contributed to 27.6% of the dataset variation. Farms with highest contribution to dimension 1 were larger farrow-to-finish farms, using self-replacement of gilts and implementing most of the measures evaluated. In contrast, farms with highest contribution to dimension 2 were medium to large farrow-to-finish farms, but implemented biosecurity in a lower degree. Additionally, two farm clusters were identified by Hierarchical Cluster Analysis (HCA), and the odds of influenza A virus detection was statistically different between clusters (OR 7.29, CI: 1.7,66, p = < 0.01). Moreover, after logistic regression analysis, three important variables were associated with higher odds of influenza detection: (1) “location in an area with a high density of pigs”, (2) “farm size”, and (3) “after cleaning and disinfecting, the facilities are allowed to dry before use”. Our results revealed two clustering patterns of swine farms. This systematic analysis of complex survey data identified relationships between biosecurity, husbandry practices and influenza status. This approach helped to identify gaps on biosecurity and key elements for designing successful strategies to prevent and control swine respiratory diseases in the swine industry.

The epidemiology of swine influenza

Globally swine influenza is one of the most important diseases of the pig industry, with various subtypes of swine influenza virus co-circulating in the field. Swine influenza can not only cause large economic losses for the pig industry but can also lead to epidemics or pandemics in the human population. We provide an overview of the pathogenic characteristics of the disease, diagnosis, risk factors for the occurrence on pig farms, impact on pigs and humans and methods to control it. This review is designed to promote understanding of the epidemiology of swine influenza which will benefit the control of the disease in both pigs and humans.

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.

Widespread Prevalence of Antibodies Against Swine Influenza A (pdm H1N1 09) Virus in Pigs of Eastern Uttar Pradesh, India

Swine influenza virus (SIV) belongs to family Orthomyxoviridae and can cause acute respiratory infection in pigs. Several pandemic H1N1 human fatal influenza cases were reported in India. Though pigs are predisposed to both avian and human influenza virus infections with the potential to generate novel reassortants, there are only a few reports of SIV in Indian pigs. We conducted a serological survey to assess the status of H1N1 infection in pigs of various states in India, between 2009 and 2016. Based on Haemagglutination inhibition (HI) assay, seroprevalence rate of H1N1 virus ranged between 5.2% (2009) and 36.3% (2011). Widespread prevalence of antibody was observed in eastern Uttar Pradesh from 6.2 to 37.5% during the study period. Co-circulation of seasonal H1N1 virus along with pandemic H1N1 virus was indicated by the presence of specific antibodies against seasonal H1N1 virus in eastern part of Uttar Pradesh. Seroprevalence rate in pigs and influenza infection trend in human shows the possible spill over transmission of influenza to pigs from human. Hence, besides serological surveillance, continuous and systematic molecular surveillance should be implemented in pig population to reduce/quantify the risk and emergence of pandemic influenza.

Singapore COVID-19 Pandemic Response as a Successful Model Framework for Low-Resource Health Care Settings in Africa?

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to spread and evolve across the planet. The crosscutting impacts of the virus, individual country responses to the virus, and the state of preparedness of local public health systems greatly vary across the world. The ostensibly late arrival of the virus in Africa has allowed learning, innovation, and adaptation of methods that have been successful in the early-hit countries. This article analyzes how Singapore has responded to the COVID-19 pandemic and proposes that adaptations of the Singapore pandemic response model would bode well for Africa's response to the COVID-19 pandemic in ways that also take into account regional differences in health care infrastructures, socioeconomic resilience, poverty, and the vast population diversity in the African continent. As the pandemic evolves, the lessons learned in Asia, in particular, and the emerging new experiences in African countries should inform, ideally in real time, how best to steer the world populations into safety, including those in low-resource health care settings. Finally, we note that the current COVID-19 pandemic is also a test for our collective ability to scale and surge public health in response to future and likely equally challenging zoonosis infections that jump from animals to humans, not to mention climate change-related planetary health calamities in the 21st century. Hence, what we learn effectively from the current COVID-19 pandemic shall have broad, enduring, and intergenerational relevance for the future of planetary heath and society.

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

Since the incursion of influenza A(H1N1)pdm09 virus in 2009, serosurveillance every year of the Norwegian pig population revealed the herd prevalence for influenza A(H1N1)pdm09 (HIN1pdm09) has stabilised between 40% and 50%. Between 30 September 2009 and 14 September 2017, the Norwegian Veterinary Institute and Norwegian Food Safety Authority screened 35,551 pigs for antibodies to influenza A viruses (IAVs) from 8,636 herds and found 26% or 8,819 pigs' sera ELISA positive (titre ≥40). Subtyping these IAV antibodies from 8,214 pigs in 3,629 herds, by a routine haemagglutination inhibition test (HAIT) against four standard antigens produced 13,771 positive results (HAIT titre ≥40) of binding antibodies. The four antigen subtypes eliciting positive HAIT titre in descending frequencies were immunogen H1N1pdm09 (n = 8,200 or 99.8%), swine influenza A virus (SIVs) subtypes swH1N1 (n = 5,164 or 62%), swH1N2 (n = 395 or 5%) and swH3N2 (n = 12 or 0.1%). Of these 8,214 pig pigs sera, 3,039 produced homologous HAIT subtyping, almost exclusively immunogen H1N1pdm09 (n = 3,026 or 99.6%). Using HAIT titre of pig and herd geometric mean titre (GMT) as two continuous outcome variables, and with the data already structured hierarchically, we used mixed effects linear regression analysis to investigate the impact of predictors of interests had on the outcomes. For the full data, the predictors in the regression model include categorical predictors antigen subtype (H1N1pdm09, swH1N1, swH1N2 & swH3N2), and production type (sow herd or fattening herd), ordinal predictors year (longitudinally from 2009 to 2017) and number of antigens in heterologous reactions (1, 2, 3, 4) in the same pig serum. The last predictor, the proportion of HAIT positive (antigen specific) in tested pigs within the herd, was a continuous predictor, which served as a proxy for days post-infection (dpi) or humoral response time in the pig or herd. Regression analysis on individual pig HAIT titres showed that antigen as a predictor, the coefficient for immunogen H1N1pdm09 was at least fourfold higher (P < 0.001) than the three SIVs antigen subtypes, whose much lower coefficients were statistically no different between the three SIVs antigen subtypes. Correspondingly, for herd GMT, immunogen H1N1pdm09 was 28–40-fold higher than the three SIVs antigen subtypes. Excluding the HAIT data of the three SIVs antigen subtypes, regression analysis focusing only on immunogen H1N1pdm09 increased greatly the coefficients of the predictors in the models. Homologous reactions (99.6% H1N1pdm09) have lower HAIT titres while the likelihood of the number of antigens involved in HAIT heterologous reactions in a single pig serum increased with higher HAIT titres of immunogen H1N1pdm09. For predictor ‘production’, sows and sow herds had higher HAIT titres and GMT compared to fattening pigs and fattening herds respectively. Herds with ‘higher proportion of pigs tested positive’ also had higher HAIT titre in the pig and herd GMT.

Prevalence, distribution and risk factors of farmer reported swine influenza infection in Guangdong Province, China

A cross-sectional study was undertaken to better understand the husbandry, management and biosecurity practices of pig farms in Guangdong Province (GD), China to identify risk factors for farmer reported swine influenza (SI) on their farms. Questionnaires were administered to 153 owners/managers of piggeries (average of 7 from each of the 21 prefectures in GD). Univariable and multivariable logistic regression analyses were used to identify risk factors for farmer reported SI in piggeries during the six months preceding the questionnaire administration. The ability of wild birds to enter piggeries (OR 2.50, 95% CI: 1.01-6.16), the presence of poultry on a pig-farm (OR 3.24, 95% CI: 1.52-6.94) and no biosecurity measures applied to workers before entry to the piggery (OR 2.65, 95% CI: 1.04-6.78) were found to increase the likelihood of SI being reported by farmers in a multivariable logistic regression model. The findings of this study highlight the importance of understanding the local pig industry and the practices adopted when developing control measures to reduce the risk of SI to pig farms.

Distribution of gene segments of the pandemic A(H1N1) 2009 virus lineage in pig populations

Swine influenza viruses (SIVs) are important not only for pig farming, but also for public health. In fact, pandemic A(H1N1) 2009 viruses [A(H1N1)pdm09] were derived from SIVs. Therefore, timely characterization of locally circulating SIVs is necessary for understanding the global status of SIVs. To genetically characterize SIVs circulating in Japanese pig populations, we isolated 24 SIVs of three subtypes (17 H1N1, four H1N2 and three H3N2 strains) from 14 pig farms in Japan from 2013 to 2016. Genetic analyses revealed that the haemagglutinin (HA) and neuraminidase (NA) genes of the 17 H1N1 and the HA gene of one H1N2, A/swine/Aichi/02/2016 (H1N2), SIVs belonged to the A(H1N1)pdm09 lineage. More importantly, all of the remaining six gene segments (i.e., PB1, PB1, PA, NP, M and NS) of the 24 SIVs, regardless of the HA and NA subtype, were also classified as belonging to the A(H1N1)pdm09 lineage. These results indicate that gene segments of A(H1N1)pdm09 lineage are widely distributed in SIVs circulating in Japanese pig populations In addition, the NA gene of A/swine/Aichi/02/2016 (H1N2) shared less than 88.5% nucleotide identity with that of the closest relative A/swine/Miyagi/5/2003 (H1N2), which was isolated in Japan in 2003. These results indicate the sustained circulation of classical H1N2-derived SIVs with remarkable diversity in the NA genes in Japanese pig populations. These findings highlight the necessity of both intensive biosecurity systems and active SIV surveillance in pig populations worldwide for both animal and public health.

Serological Survey of the Influenza a Virus in Polish Farrow-to-finish Pig Herds in 2011-2015

Introduction

The aim of this study was to assess the seroprevalence of swine influenza A virus (SIV) in Polish farrow-to-finish pig herds.

Material and Methods

Serum samples collected from 5,952 pigs, from 145 farrow-to-finish herds were tested for the presence of antibodies against H1N1, H1N1pdm09, H1N2, and H3N2 SIV subtypes using haemagglutination inhibition (HI) test. Samples with HI titres equal or higher than 20 were considered positive.

Results

HI antibodies to at least one of the analysed SIV subtypes were detected in 129 (89%) herds and in 2,263 (38%) serum samples. Antibodies to multiple SIV subtypes were detected in 104 (71.7%) herds and in 996 (16.7%) serum samples. Concerning the seroprevalence rate, according to age category, the highest prevalence of the antibodies was detected in weaners, with regard to the H1N1, H1N2, and H3N2, and in sows, with regard to the H1N1pdm09. The lowest seroprevalence for all evaluated SIV subtypes was detected in finishers.

Conclusion

The study indicates that antibodies against single and multiple SIV subtypes are circulating in Polish farrow-to-finish herds and highlights the importance of conducting a molecular surveillance programme in future studies.