Influenza D virus

Liu, Runxia; Sheng, Zizhang; Huang, Can; Wang, Dan Dan; Li, Feng

doi:10.1016/j.coviro.2020.08.004

Cited by 33 publications

(23 citation statements)

References 59 publications

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“…Dane et al., 2019; Ferguson et al., 2015, 2018; Flynn et al., 2018; Gaudino et al., 2020; Gorin et al., 2019; Murakami et al., 2019; Nedland et al., 2018; O'Donovan et al., 2019; Oliva et al., 2019; Salem et al., 2017; Silveira et al., 2019; Snoeck et al., 2018). A review paper proposed the cattle as reservoir and amplification host with periodic spill over to other hosts listed before (Liu et al., 2020). In addition, a recent study identified IDV genome in Asian poultry farm aerosol.…”

Section: Introductionmentioning

confidence: 99%

Influenza D virus in respiratory disease in Canadian, province of Québec, cattle: Relative importance and evidence of new reassortment between different clades

Saegerman

Gaudino

Savard³

et al. 2021

Transbounding Emerging Dis

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Background: Influenza D virus (IDV), a segmented single stranded negative sense ribonucleic acid (RNA) virus belongs to the new Delta influenza virus genus of the Orthomyxoviridae family.Cattle was proposed as the natural reservoir of IDV in which infection was associated with mild to moderate respiratory clinical signs (i.e. cough, nasal discharge, and dyspnoea). Methods and principal findings: In order to investigate the role of IDV in bovine respiratory disease, during the period 2017-2020, 883 nasal or naso-pharyngeal swabs from Canadian cattle with respiratory signs (cough and/or dyspnoea) were tested by (RT-)qPCR for IDV and other major bovine viral (bovine herpesvirus 1, bovine viral diarrhoea virus, bovine respiratory syncytial virus, bovine parainfluenza virus 3, and bovine coronavirus) and bacterial (Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis) respiratory pathogens. In addition, whole genome sequencing and phylogenetic analyses were carried out on five IDV positive samples. The prevalence of IDV RT-qPCR (with cut-off: Cq < 38) at animal level was estimated at 5.32% (95% confidence interval: 3.94-7.02). Positive result of IDV was significantly associated with (RT-)qPCR positive results for bovine respiratory syncytial virus and Mycoplasma bovis. While phylogenetic analyses indicate that most segments belonged to clade D/660, reassortment between clades D/660 and D/OK were evidenced in four samples collected in 2018-2020. Conclusions and significance: Relative importance of influenza D virus and associated pathogens in bovine respiratory disease of Canadian dairy cattle was established. Whole genome sequencing demonstrated evidence of reassortment between clades D/660 and D/OK. Both these new pieces of information claim for more surveillance of IDV in cattle production worldwide.

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Section: Introductionmentioning

confidence: 99%

Influenza D virus in respiratory disease in Canadian, province of Québec, cattle: Relative importance and evidence of new reassortment between different clades

Saegerman

Gaudino

Savard³

et al. 2021

Transbounding Emerging Dis

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“…The homology of amino acid sequences between IDV and ICV is roughly 50%, however, the distance between the two influenza viruses is similar to the one found between influenza A and B viruses (IAVs, IBVs). 2,3 No cross-reactivity has been detected between IDV and ICV. 4 Although the virus has been first isolated in swine, several epidemiological and serological studies support the hypothesis that cattle represent the natural reservoir of IDV with periodical spillover events to other animal hosts (i.e., camel, sheep, swine, horse, goat).…”

mentioning

confidence: 98%

Detection of antibodies against influenza D virus in swine veterinarians in Italy in 2004

Trombetta

Montomoli

Bartolo

et al. 2021

Journal of Medical Virology

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Influenza D virus (IDV) was first isolated from a swine with respiratory disease symptoms in 2011 in the United States. Epidemiological and serological studies support the hypothesis that cattle represent the natural reservoir of IDV with periodical spillover events to other animal hosts. Little is known about the seroprevalence in humans and in specific target groups such as veterinarians in Italy. This study was designed to assess the prevalence of antibodies against two influenza D lineages (D/660 and D/OK) in Italy in archived serum samples from veterinarians working with swine collected in 2004. Serum samples were tested by haemagglutination inhibition (HI) and virus neutralization (VN) assays. Results showed that 4.88% (4/82) of tested samples were positive for D/660 and 2.44% (2/82) for D/OK by HI assay. Three out of four samples showed positivity when tested by VN

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“…[19][20][21]. There are four types of influenza viruses: influenza A, B, C, and D. However, influenza A (H1N1 and H3N2) and influenza B are the main influenza viruses that cause current seasonal influenza [22][23][24]. Although in clinical diagnosis, influenza cannot be easily distinguished from other acute respiratory illnesses, such as common cold, bronchitis, or viral pneumonia, etc., influenza is usually more serious than the common cold, and the duration of treatment is longer than the common cold.…”

Section: The Definition Of Influenza-like Illnessesmentioning

confidence: 99%

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

Wei

et al. 2022

Sustainability

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As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.

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Influenza D virus

Cited by 33 publications

References 59 publications

Influenza D virus in respiratory disease in Canadian, province of Québec, cattle: Relative importance and evidence of new reassortment between different clades

Influenza D virus in respiratory disease in Canadian, province of Québec, cattle: Relative importance and evidence of new reassortment between different clades

Detection of antibodies against influenza D virus in swine veterinarians in Italy in 2004

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

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