Avian influenza virus is a causal agent of avian influenza, which is belonging to Orthomyxoviridae, and possess the two types of glycoproteins on the surface of a virion; hemagglutinin (HA) and neuraminidase (NA). Based on the reactivity to antiserum, the HA and NA of Type A influenza viruses are classified into H1 to H16, and N1 to N9, respectively. Since 1997, high pathogenicity avian influenza (HPAI), especially caused by the infection of H5HA, had been reported in Asia. Especially, since 2010, H5N8 HPAI viruses (HPAIVs) have been prevalent not only in Asia but also in other regions including Europe and Africa. As of 2020, the H5N8s were further classified into clade 184.108.40.206 according to the amino acid sequence, and H5 HPAIV subclades of a to h in the clade 220.127.116.11 are confirmed. Graduate School of Veterinary Medicine was designated as OIE reference laboratory for highly and low pathogenic avian influenza since 2005 and had conducted several actions including global surveillance of avian influenza. We had already stocked the isolates and developed the genetic database of them (https://virusdb.czc.hokudai.ac.jp).
In October 2020, we isolated the H5N8 HPAIV from a fecal sample collected at the Komuke Lake in Hokkaido under the global surveillance, which was designated as A/northern pintail/Hokkaido/M13/2020 (H5N8) (NP/Hok/20). It was revealed that NP/Hok/20 was classified into the H5 clade 18.104.22.168b in the HA of the phylogenetic tree and genetically very close to the H5N8 HPAIV isolated in Europe in the winter of 2019-2020. Furthermore, comprehensive phylogenetical analysis using the H5N8 HPAIV isolated in the Far East in the winter of 2020-2021 revealed that those isolates were classified into three categories including 1) the European isolates in the winter of 2019-2020, 2) the European isolates in the winter of 2020-2021, and 3) isolates in China and Mongolia. Since the genetic reassortment between the European H5N8 isolates and avian influenza viruses in the Asia was confirmed in the phylogenetical analysis, further spread with wider genetic variation might occur.
HPAI spread situations in the Europe and Japan in the end of 2020 were similar to ones in the end of 2016; many of the contagious viruses were brought by bird migration from the northern territories. Given the situation that very close viruses were isolated at both edges of the Eurasian continent at the same season, it is likely that the contagious viruses had been already perpetuated in the northern territory where the migratory birds nest in the summer season. Periodic updates of intensive survey at the global level as well as the reinforcement of the biosecurity measures in the poultry farm are essential to prepare for future HPAI outbreak.
Avian influenza (AI) surveillance in wild birds had been conducted all over the world, as migratory water birds are worrying potential carriers of highly pathogenic avian influenza (HPAI) viruses. Risk assessment of AI in wild birds had been conducted intensively in European and American countries, and target species and high priority areas for AI surveillance have been specified. Nowadays, passive surveillance of dead and debilitated birds are major surveillance methods in most of these countries. National HPAI surveillance in wild birds was started in Japan in 2008. Passive surveillance of reported dead birds and active surveillance of waterbird feces were conducted in each prefecture as part of the national surveillance. The livestock hygiene service centers in most of the prefectures conducted influenza rapid diagnostic tests. AI surveillance in wild birds is essential not only to perceive infection status in wild birds, but to provide important information for rare bird conservation, both in the wild and in captivity. In addition, the early detection of HPAI infection in wild birds plays an important role in the alert of poultry and captive birds. Surveillance system in wild birds should be prepared to maintain at any situation, such as in the middle of severe outbreaks of livestock diseases.
The objectives of the present study were to analyze the associations of climate conditions and lunar cycle with daily calving frequency of cows, to determine factors associated with calvings at night, and to investigate the relationship between tide level and calving frequency on a large dairy commercial farm in Kyushu, Japan. The present study was conducted on a dairy farm having approximately 2,500 Holstein cows and collected 8,485 calving data from 2013 to 2016. As a result, daily calving frequency was not associated with both climate conditions such as maximum temperature and lunar cycle. Proportion of calvings at night (7 pm to 7 am) was 46.3% and lower than that at day time. Primiparous cows had 1.11 times higher odds ratio (95% confidence interval: 1.01-1.22) for calving at night than multiparous cows. Additionally, cows that calved at night in July to September had 1.16 times higher odds ratio (95% confidence interval: 1.03-1.31) than those that calved in January to March. In primiparous cows, positive linear relationship between tide level and calving frequency was found (coefficient±SE; 0.00086±0.000362; P=0.01), but those not in multiparous cows. In summary, no associations of climate condition and lunar cycle with calving frequency were found in dairy cows, and primiparous cows and cows calved in July to September were prone to calvings at night. Additionally, a significant relationship between tide level and calving frequency was found in primiparous cows.
Evaluation of regression models is essential for identifying avoidable errors in the data manipulation process and model building, and assessing the robustness of model results. The model evaluation has two steps; assessment of the overall goodness of fit and identifying influential observations. This article covers a fundamental concept of the model evaluation and provides a few strategies to deal with poorly fitting models. A tutorial material using R is developed where readers can replicate line-by-line a model building and evaluation process.