Seasonal Epidemics Research Articles

Norovirus Infection in the Sverdlovsk Region, 2009–2022: Retrospective Epidemiological Analysis and Statistical Modeling Results

Introduction: Norovirus infection (NVI) is a highly contagious disease that is most common in children and entails significant health care costs. Objectives: To analyze and describe epidemic spread patterns of norovirus infection in the Sverdlovsk Region in 2009–2022 and to examine its determinants using statistical modeling techniques. Materials and methods: We have analyzed statistics on the monthly number of NVI cases and newborns, publicly available data on weather factors and population migration in the Sverdlovsk Region. Negative binomial regression was used to assess contribution of natural and social factors to disease incidence. The statistical analysis was carried out in the R environment. Results: A long-term increasing trend in NVI incidence that stabilized on a relatively high level in 2016–2018 was observed. The epidemic season usually started in October or November and lasted until April–May. Children under 6 years of age were at highest risk of the infection. Distribution of the disease incidence across the Sverdlovsk Region was uneven; relatively high rates were registered in some municipalities. The results of statistical modeling showed a negative correlation between weather conditions (mean temperature and relative humidity), high COVID-19 rates, and NVI incidence. The amount of precipitation, atmospheric pressure, sunshine duration, and external migration were found to be statistically insignificant predictors. In addition to the above, the level of herd immunity is likely to affect the NVI incidence, although it is hard to estimate the extent of its impact due to the lack of trustworthy data on the duration of immunity to noroviruses. Conclusion: Norovirus infection is a regional health care challenge, especially among preschool children. Ambient air temperature, relative humidity, and social distancing due to rising COVID-19 incidence rates proved to be negative predictors associated with a decrease in the number of NVI cases in the study area.

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo.

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

The role of temperature in the start of seasonal infectious disease epidemics

Many infectious diseases display strong seasonal dynamics. When both hosts and parasites are influenced by seasonal variables, it is unclear if the start of an epidemic is limited by host or parasite factors or both. The Daphnia–Pasteuria host–parasite system exhibits seasonal epidemics. We aimed to ascertain how temperature contributes to the timing of P. ramosa epidemics in early spring. To this aim, we experimentally disentangled this effect from the effects of temperature on host development and phenology and from that of host traits on parasite time to visible infection. We hypothesized that the parasite is additionally directly limited by low temperatures beyond its need for available hosts. We found that parasite time to visible infection decreased with increasing temperature at a faster rate than host time to hatching and maturity did, consistent with this hypothesis. We also found that hosts hatched from sexual resting stages are less likely to become infected than those produced clonally, and that hosts resistant to many known parasite strains are slower to show signs of visible infection compared to those susceptible to many. Together, these results imply that climate change could lead to earlier seasonal epidemics for this host–parasite system, which may also impact longer‐term population dynamics.

Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.

Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.

Respiratory syncytial virus: can we still believe that after pandemic bronchiolitis is not a critical issue for public health?

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infection among infants and young children, resulting in annual epidemics worldwide. Since the beginning of the COVID-19 pandemic, non-pharmacological interventions were applied, interfering with the circulation of most respiratory viruses, including RSV. The aim of this study is to analyze the RSV infection trend among hospitalized infants during the actual epidemic season (2022–2023) in comparison with the last pre-pandemic season (2018–2019), in order to outline whether significant differences emerge due to COVID-19 pandemia. We retrospectively reviewed medical data on infants hospitalized at the Bambino Gesù Children’s Hospital with diagnosis of bronchiolitis in the current epidemic season and in the last pre-pandemic season, 2018–2019. RSV remains the main etiological agent of bronchiolitis in terms of frequency and severity of infections in the ongoing epidemic season. The first RSV case of the 2022–2023 season was detected at week 42 vs week 47 in the 2018–2019 season. The length of epidemic season was of 17 weeks in 2022–2023 vs 18 weeks in 2018–2019. Comparing the two seasons, age at admission was significantly higher in the current season (median age 2022–2023 65 days vs median age 2018–2019 58 days), but the disease severity was similar. Conclusions: The 2022–2023 bronchiolitis season in Italy started earlier than the usual pre-pandemic seasons but seasonality pattern may be going back to the pre-pandemic one. This season was not more severe than the previous ones. The impact of RSV disease on health care systems and costs remains a critical issue.What is Known:• RSV is one of the major leading causes of hospitalization among children aged less than 3 months. SarsCOV2 pandemic interfered with the seasonal circulation of most respiratory viruses, Including RSV.What is New:• The 2022–2023 bronchiolitis season in Italy started and peaked earlier than the usual pre-pandemic seasons but seasonality pattern may be realigning to the pre-pandemic one. The impact of RSV disease on health care systems and costs is concerning.

Clinical impact of human parainfluenza virus infections before and during the COVID-19 pandemic in Southern China

Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory tract infections (ARTIs). Non-pharmaceutical interventions (NPIs) were widely administered to combat the pandemic of the coronavirus disease 2019 (COVID-19). Respiratory specimens were obtained from 10,454 hospitalized children with ARTIs to detect HPIV. We investigated differences in epidemiological and clinical characteristics of HPIV infections before (2017–2019) and during the COVID-19 pandemic (2020–2022). HPIVs were detected in 392 (3.75%, 392/10,454) patients, of whom 70 (17.86%), 48 (12.24%), and 274 (69.90%) were positive for HPIV1, HPIV2, and HPIV3, respectively. Detection rates of HPIV3 were higher in 2020–2022 than in 2017–2019 (3.38% vs. 2.24%). The seasonal distribution of HPIV1 showed no difference, but HPIV3 peaked between September and December during the COVID-19 pandemic, which differed from previous epidemiological patterns. Compared to the period before the COVID-19 pandemic, there has been a noticeable decrease in the incidence of asthma, moist rales, and emesis in patients infected with HPIV1 and in asthma, expectoration, and severe pneumonia in patients infected with HPIV3 during 2020–2022. The detection rates of HPIV increased in Southern China during the COVID-19 outbreak, which underlines the importance of continuous surveillance of HPIV in the next epidemic season.

Development of B-cell response during immunization with inactivated influenza vaccines "Grippol plus", "Sovigripp" and "Ultrix"

The worldwide circulating influenza viruses annually lead to serious medical and socio-economic consequences. It is generally recognized that vaccination is the most effective and safe strategy for preventing influenza and its complications. In order to reduce side effects when using live viruses, split and subunit influenza vaccines are widely used. To date, the characteristics of B cell response after immunization with influenza vaccines remain insufficiently studied. The aim of our study was to compare the effects of immunization with different influenza vaccines, i.e., “Sovigripp”, “Grippol plus” and “Ultrix”, on the B cell response. The study was conducted on the base of Clinical Department at the A.Smorodintsev Influenza Research Institute during the epidemic flu season of 2018-2019. For clinical studies, venous blood samples were obtained from 39 volunteers before vaccination, on the 7th and 21st days after vaccination. The subpopulations of B cells were analyzed by flow cytometry using fluorescently labeled antibodies to CD3, CD19, CD20, CD27, CD38, IgD, IgA surface antigens (BioLegend, USA). Cryopreserved mononuclear cells (1 × 106 cells/sample) were used for analysis. The processing of flow cytometry data was carried out with special software (H., Cytexpert, Beckman Coulter, Inc., USA) and Kaluza 2.0 (Beckman Coulter, Inc., USA). The differences with pre-vaccination data were evaluated using the Mann–Whitney U-test and being considered significant at p < 0.05. As a result of the studies, the following subpopulations of B lymphocytes (CD3-CD19+) were specified: naive B cells (CD20+CD27-IgD+), non-switched memory B cells (CD20+CD27+IgD+), switched memory B cells (CD20+CD27+IgD-), effector memory B cells (CD20+CD27-IgD-), plasmablasts (CD20-CD38hiCD27hi). Activation of the B cell immune response was assessed by measuring the relative content of CD38+B cells belonging to subpopulations of naive, effector B lymphocytes, switched and non-switched memory B cells. The analysis of B cell response showed an increase in both the total number of B lymphocytes and their subpopulations including plasmablasts and activated switched memory B cells after immunization. With adjuvant vaccines “Grippol plus” and “Sovigripp”, as compared with the split “Ultrix” vaccine, an early increase in relative counts of plasmablasts was shown on the 7th day of the study. At the same time, all three vaccines equally contributed to an increase in the number of activated memory B cells with a switched antibody isotype. Thus, the assessment of B cell response revealed significant changes in contents of peripheral blood B cell subpopulations in response to vaccination with “Grippol plus”, “Sovigripp”, or “Ultrix”.

The re-emergence of influenza following the COVID-19 pandemic in Victoria, Australia, 2021 to 2022.

BackgroundCOVID-19 pandemic mitigation measures, including travel restrictions, limited global circulation of influenza viruses. In Australia, travel bans for non-residents and quarantine requirements for returned travellers were eased in November 2021, providing pathways for influenza viruses to be re-introduced.AimWe aimed to describe the epidemiological and virological characteristics of the re-emergence of influenza in Victoria, Australia to inform public health interventions.MethodsFrom 1 November 2021 to 30 April 2022, we conducted an epidemiological study analysing case notification data from the Victorian Department of Health to describe case demographics, interviewed the first 200 cases to establish probable routes of virus reintroduction and examined phylogenetic and antigenic data to understand virus diversity and susceptibility to current vaccines.ResultsOverall, 1,598 notifications and 1,064 positive specimens were analysed. The majority of cases (61.4%) occurred in the 15-34 years age group. Interviews revealed a higher incidence of international travel exposure during the first month of case detections, and high levels of transmission in university residential colleges were associated with return to campus. Influenza A(H3N2) was the predominant subtype, with a single lineage predominating despite multiple importations.ConclusionEnhanced testing for respiratory viruses during the COVID-19 pandemic provided a more complete picture of influenza virus transmission compared with previous seasons. Returned international travellers were important drivers of influenza reemergence, as were young adults, a group whose role has previously been under-recognised in the establishment of seasonal influenza epidemics. Targeting interventions, including vaccination, to these groups could reduce future influenza transmission.

Diverse roles of lung macrophages in the immune response to influenza A virus.

Influenza viruses are one of the major causes of human respiratory infections and the newly emerging and re-emerging strains of influenza virus are the cause of seasonal epidemics and occasional pandemics, resulting in a huge threat to global public health systems. As one of the early immune cells can rapidly recognize and respond to influenza viruses in the respiratory, lung macrophages play an important role in controlling the severity of influenza disease by limiting viral replication, modulating the local inflammatory response, and initiating subsequent adaptive immune responses. However, influenza virus reproduction in macrophages is both strain- and macrophage type-dependent, and ineffective replication of some viral strains in mouse macrophages has been observed. This review discusses the function of lung macrophages in influenza virus infection in order to better understand the pathogenesis of the influenza virus.

Convergent trends and spatiotemporal patterns of Aedes-borne arboviruses in Mexico and Central America.

Aedes-borne arboviruses cause both seasonal epidemics and emerging outbreaks with a significant impact on global health. These viruses share mosquito vector species, often infecting the same host population within overlapping geographic regions. Thus, comparative analyses of the virus evolutionary and epidemiological dynamics across spatial and temporal scales could reveal convergent trends. Focusing on Mexico as a case study, we generated novel chikungunya and dengue (CHIKV, DENV-1 and DENV-2) virus genomes from an epidemiological surveillance-derived historical sample collection, and analysed them together with longitudinally-collected genome and epidemiological data from the Americas. Aedes-borne arboviruses endemically circulating within the country were found to be introduced multiple times from lineages predominantly sampled from the Caribbean and Central America. For CHIKV, at least thirteen introductions were inferred over a year, with six of these leading to persistent transmission chains. For both DENV-1 and DENV-2, at least seven introductions were inferred over a decade. Our results suggest that CHIKV, DENV-1 and DENV-2 in Mexico share evolutionary and epidemiological trajectories. The southwest region of the country was determined to be the most likely location for viral introductions from abroad, with a subsequent spread into the Pacific coast towards the north of Mexico. Virus diffusion patterns observed across the country are likely driven by multiple factors, including mobility linked to human migration from Central towards North America. Considering Mexico's geographic positioning displaying a high human mobility across borders, our results prompt the need to better understand the role of anthropogenic factors in the transmission dynamics of Aedes-borne arboviruses, particularly linked to land-based human migration.

Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil.

Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil, our understanding of disease outbreaks is hampered by limited viral genomic data. Here, through a combination of phylogenetic and epidemiological models, we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated Aedes aegypti was able to capture the seasonality of reported human infections. Spatial modeling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centers in the Southeast. Phylodynamic analysis unraveled the circulation of three distinct lineages and provided proof of the directionality of a known spatial corridor that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.

Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.

Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons.

Evolution oftransient RNA structure-RNA polymerase interactions inrespiratory RNA virus genomes.

RNA viruses are important human pathogens that cause seasonal epidemics and occasional pandemics. Examples are influenza A viruses (IAV) and coronaviruses (CoV). When emerging IAV and CoV spill over to humans, they adapt to evade immune responses and optimize their replication and spread in human cells. In IAV, adaptation occurs in all viral proteins, including the viral ribonucleoprotein (RNP) complex. RNPs consist of a copy of the viral RNA polymerase, a double-helical coil of nucleoprotein, and one of the eight segments of the IAV RNA genome. The RNA segments and their transcripts are partially structured to coordinate the packaging of the viral genome and modulate viral mRNA translation. In addition, RNA structures can affect the efficiency of viral RNA synthesis and the activation of host innate immune response. Here, we investigated if RNA structures that modulate IAV replication processivity, so-called template loops (t-loops), vary during the adaptation of pandemic and emerging IAV to humans. Using cell culture-based replication assays and in silico sequence analyses, we find that the sensitivity of the IAV H3N2 RNA polymerase to t-loops increased between isolates from 1968 and 2017, whereas the total free energy of t-loops in the IAV H3N2 genome was reduced. This reduction is particularly prominent in the PB1 gene. In H1N1 IAV, we find two separate reductions in t-loop free energy, one following the 1918 pandemic and one following the 2009 pandemic. No destabilization of t-loops is observed in the influenza B virus genome, whereas analysis of SARS-CoV-2 isolates reveals destabilization of viral RNA structures. Overall, we propose that a loss of free energy in the RNA genome of emerging respiratory RNA viruses may contribute to the adaption of these viruses to the human population.

Dengue Fever Surveillance in Mato Grosso do Sul: Insights from Genomic Analysis and Implications for Public Health Strategies.

Since its discovery in early 1916, dengue fever, a common vector-borne illness in Brazil, has resulted in extensive urban outbreaks and poses a serious threat to the public's health. Understanding the dynamics of Dengue Virus (DENV) serotypes circulating in different regions of Brazil is essential for implementing effective disease control and prevention measures. In response to this urgent need, we conducted an on-site training program in genomic surveillance in collaboration with the Central Laboratory of Health and the Secretary of Health of the Mato Grosso do Sul state. This initiative resulted in the generation of 177 DENV genome sequences collected between May 2021 and May 2022, a period during which over 11,391 dengue fever cases were reported in the state. Through this approach, we were able to identify the co-circulation of two different dengue serotypes (DENV1 and DENV2) as well as the existence of diverse viral lineages within each genotype, suggesting that multiple introduction events of different viral strains occurred in the region. By integrating epidemiological data, our findings unveiled temporal fluctuations in the relative abundance of different serotypes throughout various epidemic seasons, highlighting the complex and changing dynamics of DENV transmission throughout time. These findings demonstrate the value of ongoing surveillance activities in tracking viral transmission patterns, monitoring viral evolution, and informing public health actions.

Assessing the Intense Influenza A(H1N1)pdm09 Epidemic and Vaccine Effectiveness in the Post-COVID Season in the Russian Federation.

The COVID-19 pandemic had a profound impact on influenza activity worldwide. However, as the pandemic progressed, influenza activity resumed. Here, we describe the influenza epidemic of high intensity of the 2022-2023 season. The epidemic had an early start and peaked in week 51.2022. The extremely high intensity of the epidemic may have been due to a significant decrease in herd immunity. The results of PCR-testing of 220,067 clinical samples revealed that the influenza A(H1N1)pdm09 virus dominated, causing 56.4% of positive cases, while A(H3N2) influenza subtype accounted for only 0.6%, and influenza B of Victoria lineage-for 34.3%. The influenza vaccine was found to be highly effective, with an estimated effectiveness of 92.7% in preventing admission with laboratory-confirmed influenza severe acute respiratory illness (SARI) cases and 54.7% in preventing influenza-like illness/acute respiratory illness (ILI/ARI) cases due to antigenic matching of circulated viruses with influenza vaccine strains for the season. Full genome next-generation sequencing of 1723 influenza A(H1N1)pdm09 viruses showed that all of them fell within clade 6B.1A.5.a2; nine of them possessed H275Y substitution in the NA gene, a genetic marker of oseltamivir resistance. Influenza A(H3N2) viruses belonged to subclade 3C.2a1b.2a.2 with the genetic group 2b being dominant. All 433 influenza B viruses belonged to subclade V1A.3a.2 encoding HA1 substitutions A127T, P144L, and K203R, which could be further divided into two subgroups. None of the influenza A(H3N2) and B viruses sequenced had markers of resistance to NA inhibitors. Thus, despite the continuing circulation of Omicron descendant lineages, influenza activity has resumed in full force, raising concerns about the intensity of fore coming seasonal epidemics.

Comparison of genome replication fidelity between SARS-CoV-2 and influenza A virus in cell culture

Since the emergence of COVID-19, several SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variants have emerged and spread widely. These variants are produced through replication errors of the viral genome by viral RNA-dependent RNA polymerase (RdRp). Seasonal epidemics of influenza are also known to occur because of new variants of influenza A virus (IAV), which are generated by the introduction of mutations by viral RdRp with low fidelity. Variants with different antigenicities appear because of mutations in envelope glycoproteins. In this study, we calculated and compared the mutation rates in genome replication of IAV and SARS-CoV-2. Average mutation rates per passage were 9.01 × 10–5 and 3.76 × 10–6 substitutions/site for IAV and SARS-CoV-2, respectively. The mutation rate of SARS-CoV-2 was 23.9-fold lower than that of IAV because of the proofreading activity of the SARS-CoV-2 RdRp complex. Our data could be useful in establishing effective countermeasures against COVID-19.

Impact of meteorological and demographic factors on the influenza epidemic in Japan: a large observational database study

Factors affecting the start date of the influenza epidemic season and total number of infected persons per 1,000,000 population in 47 prefectures of Japan were evaluated. This retrospective observational study (September 2014–August 2019; N = 472,740–883,804) evaluated data from a Japanese health insurance claims database. Single and multiple regression analyses evaluated the time to start of the epidemic or total infected persons per 1,000,000 population with time to absolute humidity (AH) or number of days with AH (≤ 5.5, ≤ 6.0, ≤ 6.5, and ≤ 7.0), total visitors (first epidemic month or per day), and total population. For the 2014/15, 2015/16, and 2016/17 seasons, a weak-to-moderate positive correlation (R2: 0.042–0.417) was observed between time to start of the epidemic and time to first day with AH below the cutoff values. Except in the 2016/17 season (R2: 0.089), a moderate correlation was reported between time to start of the epidemic and the total population (R2: 0.212–0.401). For all seasons, multiple regression analysis showed negative R2 for time to start of the epidemic and total visitors and population density (positive for time to AH ≤ 7.0). The earlier the climate becomes suitable for virus transmission and the higher the human mobility (more visitors and higher population density), the earlier the epidemic season tends to begin.

Revisiting the personal protective equipment components of transmission-based precautions for the prevention of COVID-19 and other respiratory virus infections in healthcare.

The COVID-19 pandemic highlighted some potential limitations of transmission-based precautions. The distinction between transmission through large droplets vs aerosols, which have been fundamental concepts guiding infection control measures, has been questioned, leading to considerable variation in expert recommendations on transmission-based precautions for COVID-19. Furthermore, the application of elements of contact precautions, such as the use of gloves and gowns, is based on low-quality and inconclusive evidence and may have unintended consequences, such as increased incidence of healthcare-associated infections and spread of multidrug-resistant organisms. These observations indicate a need for high-quality studies to address the knowledge gaps and a need to revisit the theoretical background regarding various modes of transmission and the definitions of terms related to transmission. Further, we should examine the implications these definitions have on the following components of transmission-based precautions: (i) respiratory protection, (ii) use of gloves and gowns for the prevention of respiratory virus infections, (iii) aerosol-generating procedures and (iv) universal masking in healthcare settings as a control measure especially during seasonal epidemics. Such a review would ensure that transmission-based precautions are consistent and rationally based on available evidence, which would facilitate decision-making, guidance development and training, as well as their application in practice.

Surveillance of Human Rotaviruses in Wuhan, China (2019-2022): Whole-Genome Analysis of Emerging DS-1-like G8P[8] Rotavirus.

Group A rotaviruses (RVAs) are major etiologic agents of gastroenteritis in infants and young children worldwide. To study the prevalence and genetic characteristics of RVAs, a hospital-based surveillance study was conducted in Wuhan, China from June 2019 through May 2022. The detection rates of RVAs were 19.40% (142/732) and 3.51% (8/228) in children and adults, respectively. G9P[8] was the predominant genotype, followed by G8P[8] and G3P[8]. G8P[8] emerged and was dominant in the 2021-2022 epidemic season. The genome constellation of six G8P[8] strains was assigned to G8-P[8]-I2-R2-C2-M2-A2-N2-T2-E2-H2. Phylogenetic analysis revealed that the VP7, VP4, VP2, VP3, NSP1, NSP2, NSP3, and NSP5 genes of these G8P[8] strains clustered closely with those of the G8P[8] strains in Asia and were distant from those of the P[8] and G2P[4] strains simultaneously detected in Wuhan. In contrast, the VP1, VP6, and NSP4 genes were closely related to the typical G2P[4] rotavirus, including those of G2P[4] strains simultaneously detected in Wuhan. The detection rate of RVAs decreased in the COVID-19 pandemic era. It was deduced that the G8P[8] rotaviruses that emerged in China may be reassortants, carrying the VP6, VP1, and NSP4 genes derived from the G2P[4] rotavirus in the backbone of the neighboring DS-1-like G8P[8] strains represented by CAU17L-103.

Genetic and Biological Characterization of H3N2 Avian Influenza Viruses Isolated from Poultry Farms in China between 2019 and 2021

H3N2 influenza viruses not only cause seasonal epidemics in humans but also circulate widely in animals, posing a threat to both animal and human health. Our previous studies indicate that H3N2 avian influenza viruses (AIVs) are readily detected in live poultry markets (LPMs); however, the evolution and biological characteristics of the H3N2 viruses in poultry farms in China are unclear. In this study, we performed active surveillance and collected 49,135 samples from poultry farms. In total, 21 H3N2 AIVs were isolated and their genetics, receptor-binding properties, and replication in mice were evaluated. Phylogenetic analysis indicated that H3N2 AIVs harbor complicated gene constellations and have undergone extensive reassortment; the viruses derived their genes from 12 different hemagglutinin subtypes of duck viruses, including H1, H2, H4, H5, H6, H7, H8, H9, H10, H11, H12, and H14. The complicated gene constellations indicated that H3N2 viruses may have been introduced into poultry farms from different sources, but none have become dominant in poultry farms. Although the H3N2 AIVs possessed avian-type receptor-binding preference, most of the isolates could replicate without preadaptation and some of H3N2 viruses caused weight loss in mice. Notably, two H3N2 viruses acquired the PB2 627K mutation after a single round of replication in mice, suggesting similar mutations could occur if they replicated in humans. Overall, our study demonstrates that the H3N2 AIVs pose a potential threat to the public health and emphasizes the need for continued surveillance of H3N2 viruses in the both LPMs and poultry farms.