Decreased Influenza Incidence under COVID-19 Control Measures, Singapore

We compared indicators of influenza activity in 2020 before and after public health measures were taken to reduce coronavirus disease (COVID-19) with the corresponding indicators from 3 preceding years. Influenza activity declined substantially, suggesting that the measures taken for COVID-19 were effective in reducing spread of other viral respiratory diseases.

Avian Influenza A Viruses among Occupationally Exposed Populations, China, 2014-2016

To determine the seroprevalence and seroconversion of avian influenza virus (AIV) antibodies in poultry workers, we conducted a seroepidemiologic study in 7 areas of China during December 2014–April 2016. We used viral isolation and reverse transcription PCR to detect AIVs in specimens from live poultry markets. We analyzed 2,124 serum samples obtained from 1,407 poultry workers by using hemagglutination inhibition and microneutralization assays. We noted seroprevalence of AIV antibodies for subtypes H9N2, H7N9, H6N1, H5N1-SC29, H5N6, H5N1-SH199, and H6N6. In serum from participants with longitudinal samples, we noted seroconversion, with >4-fold rise in titers, for H9N2, H7N9, H6N1, H5N1-SC29, H6N6, H5N6, and H5N1-SH199 subtypes. We found no evidence of H10N8 subtype. The distribution of AIV antibodies provided evidence of asymptomatic infection. We found that AIV antibody prevalence in live poultry markets correlated with increased risk for H7N9 and H9N2 infection among poultry workers.

Predicting the short-term success of human influenza virus variants with machine learning

Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.

Introduction of Avian Influenza A(H6N5) Virus into Asia from North America by Wild Birds

An avian influenza A(H6N5) virus with all 8 segments of North American origin was isolated from wild bird feces in South Korea. Phylogenetic analysis suggests that this virus may have been introduced into Asia by wild birds, highlighting the role of wild birds in the dispersal of these viruses.

Characterizing Emerging Canine H3 Influenza Viruses

The continual emergence of novel influenza A strains from non-human hosts requires constant vigilance and the need for ongoing research to identify strains that may pose a human public health risk. Since 1999, canine H3 influenza A viruses (CIVs) have caused many thousands or millions of respiratory infections in dogs in the United States. While no human infections with CIVs have been reported to date, these viruses could pose a zoonotic risk. In these studies, the National Institutes of Allergy and Infectious Diseases (NIAID) Centers of Excellence for Influenza Research and Surveillance (CEIRS) network collaboratively demonstrated that CIVs replicated in some primary human cells and transmitted effectively in mammalian models. While people born after 1970 had little or no pre-existing humoral immunity against CIVs, the viruses were sensitive to existing antivirals and we identified a panel of H3 cross-reactive human monoclonal antibodies (hmAbs) that could have prophylactic and/or therapeutic value. Our data predict these CIVs posed a low risk to humans. Importantly, we showed that the CEIRS network could work together to provide basic research information important for characterizing emerging influenza viruses, although there were valuable lessons learned.

[Occupational exposure to influenza virus of the wild birds]

Wild waterfowl are considered the main natural reservoir of influenza viruses and they have contributed to the reassortment of both pandemic viruses and viruses responsible for outbreaks of avian influenza in wild and domestic species. In order to determinate the factors involved, we reviewed the human cases of avian influenza related to the management of wild birds, the use of personal protective equipment, as well as the basis of surveillance programs of highly pathogenic avian influenza in wild birds in Spain. The direct transmission of influenza virus from wild birds to humans is a rare event. However, our epidemiological context is influenced by climate change and marked by the presence of migratory routes from territories where infection may be present. Thus and due to the clinical, economical and public health implications that such infections may have, the different groups exposed to wild birds (veterinarians, biologists, ornithologists, conservationists, field technicians, environmental officers, falconers, hunters, etc.) should know which are the possible sources of infection and how to handle the personal protective equipment. Besides, it is important that those groups know the current sanitary situation regarding avian influenza so they can consequently adapt their activities and employ proper protective measures, in addition to providing valuable information for surveillance programs.

A systematic review on the causes of the transmission and control measures of outbreaks in long-term care facilities: Back to basics of infection control

BACKGROUND

The unique characteristics of long-term care facilities (LTCFs) including host factors and living conditions contribute to the spread of contagious pathogens. Control measures are essential to interrupt the transmission and to manage outbreaks effectively.

AIM

The aim of this systematic review was to verify the causes and problems contributing to transmission and to identify control measures during outbreaks in LTCFs.

METHODS

Four electronic databases were searched for articles published from 2007 to 2018. Articles written in English reporting outbreaks in LTCFs were included. The quality of the studies was assessed using the risk-of-bias assessment tool for nonrandomized studies.

FINDINGS

A total of 37 studies were included in the qualitative synthesis. The most commonly reported single pathogen was influenza virus, followed by group A streptococcus (GAS). Of the studies that identified the cause, about half of them noted outbreaks transmitted via person-to-person. Suboptimal infection control practice including inadequate decontamination and poor hand hygiene was the most frequently raised issue propagating transmission. Especially, lapses in specific care procedures were linked with outbreaks of GAS and hepatitis B and C viruses. About 60% of the included studies reported affected cases among staff, but only a few studies implemented work restriction during outbreaks.

CONCLUSIONS

This review indicates that the violation of basic infection control practice could be a major role in introducing and facilitating the spread of contagious diseases in LTCFs. It shows the need to promote compliance with basic practices of infection control to prevent outbreaks in LTCFs.

Seroprevalence and risk factors of avian influenza H9 virus among poultry professionals in Rawalpindi, Pakistan

BACKGROUND

Avian influenza H9 is endemic in commercial and backyard poultry in Pakistan and is a serious occupational health hazard to industry workers. This study aimed to determine the seroprevalence of avian influenza H9 infection in people working with poultry in Rawalpindi, Pakistan and assess the measures they took to protect themselves from infection.

METHODS

A cross-sectional study was conducted from December 2016 to May 2017 of 419 people working with poultry in Rawalpindi Division, including farm workers, vaccinators, field veterinarians, butchers and staff working in diagnostic laboratories. Potential participants were randomly approached and gave written consent to participate. Data were collected using a standardized questionnaire and serum samples were processed to detect H9 antibodies using the haemagglutination inhibition test.

RESULTS

Of the 419 participants, 406 (96.9%) were male. The mean age of the participants was 36.4 (SD 10.86) years. A total of 332 participants agreed to a blood test, 167 of whom were positive for A(H9) antibodies, giving an overall seroprevalence of 50.3%. Laboratory staff had the highest seroprevalence (100%) and veterinarians the lowest (38.5%). Vaccinators, butchers and farm workers had a seroprevalence of 83.3%, 52.4% and 45.5% respectively. Personals who used facemasks had significantly lower (P<0.002) seroprevalence (29.6%) than those who never used them (90.6%). Similarly, those who always used gloves and washed their hands with soap had a seroprevalence of 32.8% compared with 89.0% in those who never took these precautions. Of the participants who handled antigens, 92.3% were seropositive.

CONCLUSION

Laboratory staff and vaccinators are exposed to viral cultures and influenza vaccines respectively which may explain their high seroprevalence.

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

Implementing the World Health Organization Pandemic Influenza Severity Assessment framework-Singapore's experience

BACKGROUND

We report our experience in evaluating the severity of local influenza epidemics using the World Health Organization Pandemic Influenza Severity Assessment framework.

METHODS

We assessed the severity of influenza by monitoring indicators of influenza transmissibility, seriousness of disease and impact on healthcare resource utilisation. Indicators were described by various parameters collected weekly from eight government hospitals, 20 government and 30 private primary care clinics, and the national public health laboratory. Transmissibility and seriousness of disease indicators were each represented by multiple parameters, and alert thresholds were set at the 70th and 90th percentile of a parameter's past 2-year surveillance data. We derived a collective measure for each indicator using the average percentile rank of the related parameters. Alert thresholds for the single impact parameter were set at predefined values and evaluated for its sensitivity, specificity and positive predictive value.

RESULTS

For the transmissibility and seriousness of disease parameters, calculation of the percentile rank was simple and independent of a parameter's underlying distribution. For the impact parameter, predefined alert thresholds had high sensitivity and specificity (>80%) but low positive predictive value (15%-30%). Assessment scales were used to qualitatively classify the activity of an indicator as low, moderate or high together with a confidence level.

CONCLUSION

We applied different methods for threshold setting depending on the attributes of each parameter and indicator. For indicators represented by multiple parameters, an aggregated assessment of the indicator's level of activity and confidence level of the assessment was needed for effective reporting.

Avian influenza viruses in humans: lessons from past outbreaks

BACKGROUND

Human infections with avian influenza viruses (AIV) represent a persistent public health threat. The principal risk factor governing human infection with AIV is from direct contact with infected poultry and is primarily observed in Asia and Egypt where live-bird markets are common.

AREAS OF AGREEMENT

Changing patterns of virus transmission and a lack of obvious disease manifestations in avian species hampers early detection and efficient control of potentially zoonotic AIV.

AREAS OF CONTROVERSY

Despite extensive studies on biological and environmental risk factors, the exact conditions required for cross-species transmission from avian species to humans remain largely unknown.

GROWING POINTS

The development of a universal ('across-subtype') influenza vaccine and effective antiviral therapeutics are a priority.

AREAS TIMELY FOR DEVELOPING RESEARCH

Sustained virus surveillance and collection of ecological and physiological parameters from birds in different environments is required to better understand influenza virus ecology and identify risk factors for human infection.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Airborne Transmission of Influenza Virus in a Hospital of Qinhuangdao During 2017-2018 Flu Season

The 2017-2018 flu season is considered to be one of the most severe, with numerous influenza outbreaks worldwide. In an infectious disease hospital of Qinhuangdao, air samples were collected daily from outpatient hall, clinical laboratory, fever clinic, children's ward (Children's Ward I/Children's Ward II), and adult ward during 23-29 January 2018 (peak flu activity) and 9-15 April 2018 (low flu activity). The air samples were collected with SLC-SiOH magnetic beads using impingement samplers. Real-time PCR assay was used to detect the RNA of airborne influenza (IFVA and IFVB) in the 91 collected aerosol samples. The results indicated that the air samples collected from the children's wards, adult ward and fever clinic were detected with airborne influenza viruses. However, the samples collected from outpatient hall and clinical laboratory were absence of influenza viruses. In addition, the subtypes of pH1N1/IFVA, H3N2/IFVA, yamagata/IFVB, and victoria/IFVB were detected among the samples with positive IFVA and IFVB. Notably, a new developed subtype of pH1N1 (an epidemic in 2018) was detected in the aerosol samples. In summary, this study profiled the distribution of airborne influenza in an infectious hospital in Qinhuangdao during 2017-2018 flu season. Patients infected with influenza could release airborne particles containing the virus into their environment. Healthcare workers and visitors in those places might have frequent exposure to airborne influenza virus. Therefore, we recommend some protective measures such as air disinfection and mask wearing to prevent and control the transmission of airborne influenza in hospital.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Spatiotemporal Analysis of Influenza Morbidity and Its Association with Climatic and Housing Conditions in Ecuador

The external environment directly influences human health. However, what happens inside? This work deals with the effect that the interior thermal variables have on the propagation of respiratory diseases and focused on the relation of the temperature and relative humidity inside social housing in the 1040 parishes of Ecuador and the transmission of influenza. On the one hand, historical weather-related variables were used to simulate and estimate the interior conditions, and thresholds on temperature and humidity were determined. On the other hand, the health-related variable was determined by analyzing the statistics corresponding to the influenza and viral pneumonia in 2009 since that year was critical for these diseases; the data were divided by month for each parish. Finally, the correlation of these variables determines the relative importance of the interior conditions on the respiratory health of its inhabitants. The preliminary results indicate that the places with the lowest temperatures and relative humidity could favor the virus transmission. Also, the analysis indicated that respiratory diseases increase in August and October. In this way, it is clear that social housing projects in Ecuador require a study which guarantees not only energy efficiency and sustainability related issues but also the well-being of their inhabitants.

Human-to-Human Transmission of Influenza A(H3N2) Virus with Reduced Susceptibility to Baloxavir, Japan, February 2019

In 2019, influenza A(H3N2) viruses carrying an I38T substitution in the polymerase acidic gene, which confers reduced susceptibility to baloxavir, were detected in Japan in an infant without baloxavir exposure and a baloxavir-treated sibling. These viruses’ whole-genome sequences were identical, indicating human-to-human transmission. Influenza virus isolates should be monitored for baloxavir susceptibility.

Comparative Pathogenicity and Transmissibility of the H7N9 Highly Pathogenic Avian Influenza Virus and the H7N9 Low Pathogenic Avian Influenza Virus in Chickens

There were five outbreaks of H7N9 influenza virus in humans in China since it emerged in 2013, infecting >1000 people. The H7N9 low pathogenic influenza virus was inserted into four amino acids in the HA protein cleavage site to mutate into the H7N9 highly pathogenic virus. This emerging virus caused 15 outbreaks in chickens from the end of 2016 to date. Two H7N9 avian influenza virus (AIV) strains, A/chicken/Guangdong/A46/2013 (LPAIV) and A/chicken/Guangdong/Q29/2017 (HPAIV), were selected to compare the pathogenicity and transmissibility between H7N9 LPAIVs and HPAIVs in chickens. We inoculated 3- to 4-week-old specific-pathogen-free (SPF) chickens with 6 log₁₀EID₅₀/0.1 mL viruses via the ocular-nasal route and co-housed four chickens in each group. The inoculated chicken mortality rate in the A46 and Q29 groups was 1/5 and 5/5, respectively. Q29 virus replication was more efficient compared to the A46 virus in inoculated chickens. Infected chickens initiated viral shedding to naïve contact chickens through respiratory and digestive routes. Both viruses transmitted between chickens by naïve contact, but the Q29 virus had a higher pathogenicity in contact chickens than the A46 virus. Compared with early H7N9 LPAIVs, the pathogenicity and transmissibility of the emerging H7N9 HPAIV was stronger in chickens, indicating that H7N9 influenza virus may continue to threaten human and poultry health.

Nosocomial transmission of influenza: A retrospective cross-sectional study using next generation sequencing at a hospital in England (2012-2014)

BACKGROUND

The extent of transmission of influenza in hospital settings is poorly understood. Next generation sequencing may improve this by providing information on the genetic relatedness of viral strains.

OBJECTIVES

We aimed to apply next generation sequencing to describe transmission in hospital and compare with methods based on routinely-collected data.

METHODS

All influenza samples taken through routine care from patients at University College London Hospitals NHS Foundation Trust (September 2012 to March 2014) were included. We conducted Illumina sequencing and identified genetic clusters. We compared nosocomial transmission estimates defined using classical methods (based on time from admission to sample) and genetic clustering. We identified pairs of cases with space-time links and assessed genetic relatedness.

RESULTS

We sequenced influenza sampled from 214 patients. There were 180 unique genetic strains, 16 (8.8%) of which seeded a new transmission chain. Nosocomial transmission was indicated for 32 (15.0%) cases using the classical definition and 34 (15.8%) based on genetic clustering. Of the 50 patients in a genetic cluster, 11 (22.0%) had known space-time links with other cases in the same cluster. Genetic distances between pairs of cases with space-time links were lower than for pairs without spatial links (P < .001).

CONCLUSIONS

Genetic data confirmed that nosocomial transmission contributes significantly to the hospital burden of influenza and elucidated transmission chains. Prospective next generation sequencing could support outbreak investigations and monitor the impact of infection and control measures.

Estimated Incubation Period and Serial Interval for Human-to-Human Influenza A(H7N9) Virus Transmission

We estimated the incubation period and serial interval for human-to-human–transmitted avian influenza A(H7N9) virus infection using case-patient clusters from epidemics in China during 2013–2017. The median incubation period was 4 days and serial interval 9 days. China’s 10-day monitoring period for close contacts of case-patients should detect most secondary infections.

Seasonal influenza: Modelling approaches to capture immunity propagation

Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.

Improving risk assessment of the emergence of novel influenza A viruses by incorporating environmental surveillance

Reassortment is an evolutionary mechanism by which influenza A viruses (IAV) generate genetic novelty. Reassortment is an important driver of host jumps and is widespread according to retrospective surveillance studies. However, predicting the epidemiological risk of reassortant emergence in novel hosts from surveillance data remains challenging. IAV strains persist and co-occur in the environment, promoting co-infection during environmental transmission. These conditions offer opportunity to understand reassortant emergence in reservoir and spillover hosts. Specifically, environmental RNA could provide rich information for understanding the evolutionary ecology of segmented viruses, and transform our ability to quantify epidemiological risk to spillover hosts. However, significant challenges with recovering and interpreting genomic RNA from the environment have impeded progress towards predicting reassortant emergence from environmental surveillance data. We discuss how the fields of genomics, experimental ecology and epidemiological modelling are well positioned to address these challenges. Coupling quantitative disease models and natural transmission studies with new molecular technologies, such as deep-mutational scanning and single-virus sequencing of environmental samples, should dramatically improve our understanding of viral co-occurrence and reassortment. We define observable risk metrics for emerging molecular technologies and propose a conceptual research framework for improving accuracy and efficiency of risk prediction. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.