Wave Of Influenza Research Articles

National Influenza Annual Report 2023-2024: A focus on influenza B and public health implications.

The 2023-2024 influenza epidemic saw the return of typical late-season influenza B circulation. The epidemic was declared in week 45 (week ending November 11, 2023) due to the predominant circulation of influenza A(H1N1) and peaked in week 52 (week ending December 30, 2023); however, as influenza A circulation decreased, influenza B detections and the percentage of tests positive increased, reaching its peak in week 14 (week ending April 6, 2024). Influenza B/Victoria dominated this wave of activity, contributing to the ongoing discussion about the apparent disappearance of influenza B/Yamagata. With the recommendation for the removal of influenza B/Yamagata lineages from the recommended seasonal influenza vaccine components, the influenza surveillance community is preparing for the possibility of a new seasonal pattern dominated by influenza B/Victoria circulation. This season, as a result of influenza B/Victoria's overwhelming predominance, younger age groups were primarily affected by the wave of influenza B activity. Over the course of the season, among all influenza B detections, 52% occurred in children aged 0-19 years. Among all influenza B-associated hospitalizations, 46.4% were in children aged 0-19 years, and the highest cumulative hospitalization rates for influenza B were among children younger than five years (n=37 per 100,000 population) and children between the ages of 5-19 years (n=15 per 100,000 population). Continued vigilance and surveillance around influenza B trends and epidemiology is required to contribute to effective epidemic preparedness.

Neural network based estimates of the climate impact on mortality in Germany: application to storyline climate simulations

The aim of this work is the prediction of heat-related mortality for Germany under future, i.e. hotter, climate conditions. The prediction is made based on 2m temperature data from climate storyline simulations using machine learning techniques. We use an echo state network for linking the outputs of storyline climate simulations to the target data. The target data are all-cause mortality rates of Germany for all ages. The network is trained with present day climate model outputs. Model outputs of future, i.e. 2K and 4K warmer, storylines are used to predict mortality rates under such climatic conditions. We find that we can train an echo state network with recent temperature data and mortality and make plausible predictions about expected developments of mortality in Germany based on future climate storylines. The trained network can successfully predict mortality rates for future climate conditions. We find increased mortality during the summer months which is attributed to the presence of more severe heat waves. The mortality decrease found during winter can be explained milder winters leading to fewer deaths caused by respiratory diseases. However, mortality in winter is largely influenced by other factors such as influenza waves or vaccination rate and explainability due to temperature is limited.

Lifting COVID-19-associated non-pharmaceutical interventions: potential impact on notifications of infectious diseases transmitted from person to person in 2022 in Bavaria, Germany.

The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) have led to substantial decreases in case numbers of infectious diseases in several countries worldwide. As NPIs were gradually lifted, intense or out-of-season outbreaks of respiratory and gastrointestinal diseases were reported, raising the hypothesis of a potential catch-up effect of infections. By analysing surveillance data from the federal reporting system for notifiable infectious diseases, we aimed to assess the potential impact of lifting COVID-19 associated NPIs on notifications of selected infectious diseases in Bavaria, 2022. We compared influenza, chickenpox, norovirus gastroenteritis, rotavirus gastroenteritis weekly case numbers in a pre-pandemic period (2016-2019) and 2022 using two time series analyses approaches: (i) a predictive model forecasting weekly case numbers for the pandemic years 2020-2022, based on 2016-2019 data, (ii) interrupted time series model, based on 2016-2022 data, including a term per pandemic period. In 2022, incidence rates were higher compared to pre-pandemic period for influenza (IRR = 3.47, 95%CI: 1.49-7.94) and rotavirus gastroenteritis (IRR = 1.36, 95%CI: 0.95-1.93), though not significant for rotavirus gastroenteritis. Conversely, case numbers remained significantly below pre-pandemic levels for chickenpox (IRR = 0.52, 95%CI: 0.41-0.65) and norovirus gastroenteritis (IRR = 0.59, 95%CI: 0.42-0.82). Seasonality changed notably for influenza, showing an earlier influenza wave compared to pre-pandemic periods. The lifting of NPIs was associated with heterogenic epidemiological patterns depending on the selected disease. The full impact of NPIs and their discontinuation may only become clear with continued monitoring and assessment of potential additional contributing factors.

Exploring viral aetiology in upper respiratory tract infections: insights from metagenomic next-generation sequencing in Swiss outpatients before and during the SARS-CoV-2 pandemic.

Upper respiratory tract infections are among the most common reasons for primary care consultations. They are diagnosed predominantly based on clinical assessment. Here, we investigated the benefit of viral metagenomic next-generation sequencing (mNGS) in an outpatient setting. This prospective cross-sectional study included immunocompetent patients with acute upper respiratory tract infections. General practitioners collected pharyngeal swabs and demographic and clinical data. Specimens were analysed using viral mNGS and conventional tests. Two hundred seventy-seven patients were recruited by 21 general practitioners between 10/2019 and 12/2020, of which 91% had a suspected viral aetiology. For 138 patients (49.8%), mNGS identified one or more respiratory viruses. The mNGS showed a high overall agreement with conventional routine diagnostic tests. Rhinoviruses were the most frequently detected respiratory viruses (20.2% of patients). Viral mNGS reflected the influenza wave in early 2020 and the SARS-CoV-2 pandemic outbreak in Switzerland in March 2020. Notably, rhinoviruses continued to circulate despite non-pharmaceutical hygiene measures. Viral mNGS allowed the initial diagnosis to be retrospectively re-evaluated. Assuming reduced turnaround times, mNGS has the potential to directly guide the treatment of upper respiratory tract infections. On an epidemiological level, our study highlights the utility of mNGS in respiratory infection surveillance, allowing early detection of epidemics and providing information crucial for prevention.

‘[O]ne of the year’s difficult problems’: the UK cinema industry and the influenza pandemic of 1918–1919

ABSTRACT The Covid-19 pandemic posed fundamental challenges for the cinema industry, encouraging potentially lasting changes in the ways films were delivered to and consumed by audiences. In light of this, this article evaluates the impact of an earlier pandemic, the waves of influenza that affected the United Kingdom in 1918–1919. The flu struck as cinema was establishing itself as a distinct entertainment form, although still subject to criticism for its perceived effects on the moral and physical health of those attending. Consequently, the exhibition of film became a prime focus of ameliorative action, many health professionals and local councillors seeing it as a prime vehicle for the transmission of disease. For all that, the impact of any regulations proved to be uneven: attention centred primarily on the threat to children and servicemen in uniform, and what restrictions were imposed proved highly localised, reflecting systems of health administration inherited from the previous century. Finally, compared to experiences elsewhere, the duration of controls proved brief. An assessment of the flu’s impact is offered for both the short term, reflected in the finances of individual businesses and aggregated tax returns, and the longer term, in challenging or reinforcing perceptions of cinema’s social worth.

EPIDEMIOLOGICAL COMPARISON OF INFLUENZA, ARI AND COVID-19 PREVALENCE IN BULGARIA FOR THE PERIOD 2017-2022

Introduction: The report presents a comparison of the incidence of respiratory infections - influenza, other acute respiratory infections and COVID-19 for the period 2017-2022. A comparative analysis of the epidemiological dynamics in different areas of the country is made and factors such as demography and vaccine coverage are also analyzed. Materials and Methods: A comparative analysis of the prevalence of Influenza/ARI, and COVID-19 was made for the years of the studied period. Regional values were compared to the country total in order to rank regions according to the experienced disease incidence burden at a regional level. The percentile method was used to identify and filter the regions where Influenza/ARI waves appeared with the highest intensity in the country. To compare intensity of COVID-19 waves between the selected regions we used the maximal weekly incidence values per 100 000 population reached for the different periods of the pandemic. Information on the age structure of the population in the respective regions and the vaccine uptake was retrieved and compared. Results: The regions of Blagoevgrad, Montana, Haskovo, Razgrad, Kardzhali, Sliven and Targovishte were filtered among all regions as the ones with the most intensive Influenza waves for the period 2017-2022. Blagoevgrad and Sliven were the regions with higher maximal COVID-19 incidence values before the Delta circulation and during the Omicron predominance. When the Delta variant was predominant, Blagoevgrad and Montana surpassed the incidence recorded for the country. No data supporting the initial hypothesis that demographic structure at regional level determines intensity of spread of the two viral respiratory infections was found. However, the regions with higher COVID-19 vaccines coverage were found to have the lowest incidence levels of the infection. Conclusion: Although COVID-19 and Influenza/ARI are both respiratory infections, they differ in their epidemiology and no specific pattern can be found. We recommend that anti-epidemic measures should be followed to limit incidence regardless of the circulating respiratory pathogen.

Epidemics In The 21st Century: A Multifaceted Challenge

In the intricate tapestry of human history, the 21st century stands as a chapter marked by the recurring emergence of epidemics, each weaving its unique thread into the fabric of global health. From the haunting reappearances of familiar adversaries like influenza to the unexpected onslaught of novel viruses such as SARS-CoV-2, this era has borne witness to a diverse array of health crises that have tested the resilience of societies worldwide. The canvas of epidemics in the 21st century encompasses a spectrum of formidable adversaries. Changes in host population ecology, genetic mutations in pathogen reservoirs, the lack of disease control, the growing population, overcrowded urban areas, climate change, and globalization have resulted in the development of several epidemics and pandemics. The intermittent waves of influenza, with their ability to swiftly mutate, present an enduring challenge to containment and mitigation efforts. The advent of novel viruses like SARS-CoV, MERS-CoV, and most notably, the unprecedented COVID-19 pandemic, underscores the unpredictability and rapid global spread inherent in emerging infectious diseases. Monkeypox, yet another disfiguring and deadly infection, is reported in 20 countries and carries the potential to become another pandemic. Dengue is yet another significant challenge for the healthcare system of many under-developed countries, especially Pakistan, affecting many people each year. Pakistan is experiencing major public health threats in the face of the current massive flood that facilitates the transmission of this deadly disease. Almost one-third of Pakistan’s population is suffering from life-threatening infections, including cholera, malaria, typhoid, etc., due to flood consequences. The silent yet pervasive threat posed by antimicrobial resistance looms large, rendering conventional treatments ineffective and elevating the urgency for novel therapeutic strategies. The surge in non-communicable diseases, including obesity, diabetes, and cardiovascular ailments, amplifies the burden on healthcare systems and demands multifaceted preventive measures. Amidst the convoluted landscape of epidemics, medical professionals confront an amalgamation of challenges. The imperative for robust preparedness, encompassing early detection, rapid response mechanisms, and efficient resource allocation, forms the cornerstone of epidemic management. Strengthening public health infrastructure remains pivotal, advocating for improved surveillance systems, enhanced laboratory capacities, and a resilient healthcare workforce. Addressing health disparities and ensuring equitable access to healthcare resources, especially in underserved communities, emerges as a fundamental tenet in epidemic management. The interconnectedness of our world mandates cohesive international collaborations, fostering information-sharing, research partnerships, and coordinated responses to combat epidemics transcending geographical boundaries. The crucible of epidemics in the 21st century compels us to acknowledge the imperativeness of collective resilience and concerted efforts. As we navigate the uncharted territories of emerging infectious threats and persisting health challenges, the amalgamation of scientific innovation, proactive policy interventions, and unwavering global solidarity becomes indispensable. In the pursuit of a healthier, more resilient future, the role of medical professionals stands as a beacon of hope, steering us through the turbulent seas of epidemics with unwavering dedication and expertise. Let us, as a global community, harness our collective strength and fortitude to confront these challenges, transcending boundaries, and fostering a world where health equity and preparedness prevail.

Spatial comparison of London's three waves of Spanish Flu.

England and Wales experienced three waves of influenza during the 1918/19 Spanish Flu pandemic. A previous analysis showed that these three waves had fundamentally different spatial and temporal characteristics. This present study compares London's experience of the three waves to discern possible geographic differences on a metropolitan level. Borough mortality data for each wave were normalized and then scaled, with spatial autocorrelation techniques displayed by GIS software and analysed for each wave. Registrar General in England and Wales reporting provided data concerning measures of 'health' and 'wealth' for each metropolitan borough. Spearman's rank correlation determined the correlation of each wave's mortality to each of the other waves including the 'health,' 'wealth' and population density factors. The comparisons showed that there is a spatial difference among the waves. The first two are spatially similar, with both exhibiting 'random' autocorrelation patterns, while the third wave exhibits a 'clustered' pattern. The borough mortality of the first two waves strongly correlated with each other, with both having similar 'health,' 'wealth' and population density factors. However, the third wave's mortality did not correlate with any of the first two and actually behaved in an opposite manner with regard to the 'health,' 'wealth,' and population density factors. These results do not appear in the literature and create new opportunities for research to explain London's mortality during the Spanish Flu pandemic of 1918/19.

Accurately summarizing an outbreak using epidemiological models takes time.

Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

The interplay between susceptibility and vaccine effectiveness control the timing and size of an emerging seasonal influenza wave in England

Relaxing social distancing measures and reduced level of influenza over the last two seasons may lead to a winter 2022 influenza wave in England. We used an established model for influenza transmission and vaccination to evaluate the rolled out influenza immunisation programme over October to December 2022. Specifically, we explored how the interplay between pre-season population susceptibility and influenza vaccine efficacy control the timing and the size of a possible winter influenza wave. Our findings suggest that susceptibility affects the timing and the height of a potential influenza wave, with higher susceptibility leading to an earlier and larger influenza wave while vaccine efficacy controls the size of the peak of the influenza wave. With pre-season susceptibility higher than pre-COVID-19 levels, under the planned vaccine programme an early influenza epidemic wave is possible, its size dependent on vaccine effectiveness against the circulating strain. If pre-season susceptibility is low and similar to pre-COVID levels, the planned influenza vaccine programme with an effective vaccine could largely suppress a winter 2022 influenza outbreak in England.

Respiratory infections in children and adolescents in Germany during the COVID-19 pandemic.

Before the COVID-19 pandemic, acute respiratory infections (ARIs) in children were mainly characterised by three pathogens: respiratory syncytial viruses (RSV), influenza viruses and rhinoviruses. The impact of the COVID-19 pandemic and the measures taken in Germany (especially until the end of 2021) on the incidence of ARI in children and adolescents aged 0 to 14 years and the pathogens causing them has not yet been comprehensively analysed. The evaluation is based on data from population-based, virological and hospital-based surveillance instruments up to the end of 2022. After the onset of the COVID-19 pandemic in early 2020, ARI rates remained almost consistently below prepandemic levels until autumn 2021, with only rhinoviruses continuously continuing to cause ARI. Only when the Omicron variant became predominant in 2022, there were measurable COVID-19 rates at population level in children, although COVID-19 hospitalisation rates remained comparatively low. RSV and influenza waves were initially absent and then occurred 'out of season', but were more severe than usual. While the measures taken were effective in inhibiting the number of respiratory infections for almost 1.5 years, moderately frequent but rather mild COVID-19 cases occurred when measures were lifted. When Omicron emerged in 2022 COVID-19 became moderately frequent but led predominantly to mild illnesses. For RSV and influenza, the measures resulted in changes in their annual timing and intensity.

Managing the Next Wave of Influenza and/or SARS-CoV-2 in the ICU-Practical Recommendations from an Expert Group for CAPA/IAPA Patients.

The aim of this study was to establish practical recommendations for the diagnosis and treatment of influenza-associated invasive aspergillosis (IAPA) based on the available evidence and experience acquired in the management of patients with COVID-19-associated pulmonary aspergillosis (CAPA). The CAPA/IAPA expert group defined 14 areas in which recommendations would be made. To search for evidence, the PICO strategy was used for both CAPA and IAPA in PubMed, using MeSH terms in combination with free text. Based on the results, each expert developed recommendations for two to three areas that they presented to the rest of the group in various meetings in order to reach consensus. As results, the practical recommendations for the management of CAPA/IAPA patients have been grouped into 12 sections. These recommendations are presented for both entities in the following situations: when to suspect fungal infection; what diagnostic methods are useful to diagnose these two entities; what treatment is recommended; what to do in case of resistance; drug interactions or determination of antifungal levels; how to monitor treatment effectiveness; what action to take in the event of treatment failure; the implications of concomitant corticosteroid administration; indications for the combined use of antifungals; when to withdraw treatment; what to do in case of positive cultures for Aspergillus spp. in a patient with severe viral pneumonia or Aspergillus colonization; and how to position antifungal prophylaxis in these patients. Available evidence to support the practical management of CAPA/IAPA patients is very scarce. Accumulated experience acquired in the management of CAPA patients can be very useful for the management of IAPA patients. The expert group presents eminently practical recommendations for the management of CAPA/IAPA patients.

Effects of process changes on emergency department crowding in a changing world: an interrupted time-series analysis

BackgroundDuring a 6-year period, several process changes were introduced at the emergency department (ED) to decrease crowding, such as the implementation of a general practitioner cooperative (GPC) and additional medical staff during peak hours. In this study, we assessed the effects of these process changes on three crowding measures: patients’ length of stay (LOS), the modified National ED OverCrowding Score (mNEDOCS), and exit block while taking into account changing external circumstances, such as the COVID-19 pandemic and centralization of acute care.MethodsWe determined time points of the various interventions and external circumstances and built an interrupted time-series (ITS) model per outcome measure. We analyzed changes in level and trend before and after the selected time points using ARIMA modeling, to account for autocorrelation in the outcome measures.ResultsLonger patients’ ED LOS was associated with more inpatient admissions and more urgent patients. The mNEDOCS decreased with the integration of the GPC and the expansion of the ED to 34 beds and increased with the closure of a neighboring ED and ICU. More exit blocks occurred when more patients with shortness of breath and more patients > 70 years of age presented to the ED. During the severe influenza wave of 2018–2019, patients’ ED LOS and the number of exit blocks increased.ConclusionsIn the ongoing battle against ED crowding, it is pivotal to understand the effect of interventions, corrected for changing circumstances and patient and visit characteristics. In our ED, interventions which were associated with decreased crowding measures included the expansion of the ED with more beds and the integration of the GPC on the ED.

Preparing for the Next Influenza Season: Monitoring the Emergence and Spread of Antiviral Resistance.

The relaxation of pandemic restrictions in 2022 has led to a reemergence of respiratory virus circulation worldwide and anticipation of substantial influenza waves for the 2022/2023 Northern Hemisphere winter. Therefore, the antiviral susceptibility profiles of human influenza viruses circulating in Germany were characterized. Between October 2019 (week 40/2019) and March 2022 (week 12/2022), nasal swabs from untreated patients with acute respiratory symptoms were collected in the national German influenza surveillance system. A total of 598 influenza viruses were isolated and analyzed for susceptibility to oseltamivir, zanamivir and peramivir, using a neuraminidase (NA) inhibition assay. In addition, next-generation sequencing was applied to assess molecular markers of resistance to NA, cap-dependent endonuclease (PA) and M2 ion channel inhibitors (NAI, PAI, M2I) in 367 primary clinical samples. Furthermore, a genotyping assay based on RT-PCR and pyrosequencing to rapidly assess the molecular resistance marker PA-I38X in PA genes was designed and established. While NAI resistance in the strict sense, defined by a ≥ 10-fold (influenza A) or ≥5-fold (influenza B) increase of NAI IC50, was not detected, a subtype A(H1N1)pdm09 isolate displayed 2.3- to 7.5-fold IC50 increase for all three NAI. This isolate carried the NA-S247N substitution, which is known to enhance NAI resistance induced by NA-H275Y. All sequenced influenza A viruses carried the M2-S31N substitution, which confers resistance to M2I. Of note, one A(H3N2) virus displayed the PA-I38M substitution, which is associated with reduced susceptibility to the PAI baloxavir marboxil. Pyrosequencing analysis confirmed these findings in the original clinical specimen and in cultured virus isolate, suggesting sufficient replicative fitness of this virus mutant. Over the last three influenza seasons, the vast majority of influenza viruses in this national-level sentinel were susceptible to NAIs and PAIs. These findings support the use of antivirals in the upcoming influenza season.

Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023.

Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023. Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023. We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China. The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

2202. Influenza Surveillance of Families in an Observational Household Study 2019-2021

Abstract Background Families with children may be at higher risk for influenza infection. Community transmission can suffer from underreporting as testing is often not performed. We studied the epidemiology of influenza in households with school-aged children using home-based sample collection. Methods We conducted a remote household study surveilling respiratory viruses from November 2019-June 2021, in King County, Washington (WA), USA. Households with school-aged children were enrolled, mailed home specimen collection kits, and asked to self-assess for weekly acute respiratory illness (ARI) using remote survey platforms. Participants with ARI symptoms were prompted to complete serial illness surveys and self-collect/parent collect mid-turbinate nasal swabs. Samples were sent to a University of Washington study laboratory for RT-PCR influenza testing. Influenza rates were compared to WA Department of Health (DOH) reporting. Results A total of 1861 ARI events were reported among 992 adults and 869 children in 470 households; 75 influenza cases were detected (36 influenza A and 39 influenza B). The study participant median age was 32 years (0-84), 10 years (1-49) for influenza A, and 11 years (3-49) for influenza B cases. Overall 13% of households had an influenza case, of which 13 (22%) reported >1 case. A total of 81% of participants reported receipt of one dose of the 2019-2020 influenza vaccine, including 91% of influenza A and 90% of influenza B cases, and 84% received the 2020-2021 influenza vaccine. Like WA DOH, we observed a wave of influenza B cases followed by influenza A in 2019-2020. During influenza season 2020-2021, WA DOH reported 9 positive influenza tests and none observed in our study. Commonly, influenza case-patients reported were fever, cough, rhinorrhea, and fatigue. GI symptoms were more common in children than adults. Of the cases, 92% of influenza A and 78% of influenza B occurred in children. Figure 1.Influenza A and B cases from 2019-2021Figure 2A.Reported Symptoms of Influenza A D0-Day of reported onset, D7-7 days after reported illness onset. No participants >49 years were positive for influenza. D0: 30 participants responded and of respondents, 13% <5 years, 47% 5-12 years, 3% 13-17 years, and 37% 18-49 years. D7: 31 participants responded and of respondents 13% <5 years, 48% 5-11 years, 3% 12-17 years, and 36% 18-49 years. Figure 2B.Reported Symptoms of Influenza B D0-Day of reported onset, D7-7 days after reported illness onset. No participants >49 years were positive for influenza. D0: 28 participants responded and of respondents, 4% <5 years, 57% 5-12 years, 14% 13-17 years, and 25% 18-49 years. D7: 28 participants responded and of respondents, 4% <5 years, 57% 5-11 years, 18% 12-17 years, and 21% 18-49 years. Conclusion Influenza illness in 2019-2020 was initially influenza B, and subsequently replaced by influenza A. Most cases were in children and adolescents, despite at least one dose of influenza vaccine. Symptoms were widely distributed and similar between influenza A and B. Influenza incidence in our cohort declined to zero with the rise of SARS-CoV-2 cases and widespread mitigation efforts. Disclosures Janet A. Englund, MD, AstraZeneca: Advisor/Consultant|AstraZeneca: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Meissa Vaccines: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Advisor/Consultant Helen Y. Chu, MD, MPH, Cepheid: Reagents|Ellume: Advisor/Consultant|Gates Ventures: Grant/Research Support|Merck: Advisor/Consultant|Pfizer: Advisor/Consultant.

Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs.

The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.

North to south gradient and local waves of influenza in Chile

Influenza seasonality is caused by complex interactions between environmental factors, viral mutations, population crowding, and human travel. To date, no studies have estimated the seasonality and latitudinal patterns of seasonal influenza in Chile. We obtained influenza-like illness (ILI) surveillance data from 29 Chilean public health networks to evaluate seasonality using wavelet analysis. We assessed the relationship between the start, peak, and latitude of the ILI epidemics using linear and piecewise regression. To estimate the presence of incoming and outgoing traveling waves (timing vs distance) between networks and to assess the association with population size, we used linear and logistic regression. We found a north to south gradient of influenza and traveling waves that were present in the central, densely populated region of Chile. Our findings suggest that larger populations in central Chile drive seasonal influenza epidemics.

Historically High Excess Mortality During the COVID-19 Pandemic in Switzerland, Sweden, and Spain.

Excess mortality quantifies the overall mortality impact of a pandemic. Mortality data have been accessible for many countries in recent decades, but few continuous data have been available for longer periods. To assess the historical dimension of the COVID-19 pandemic in 2020 for 3 countries with reliable death count data over an uninterrupted span of more than 100 years. Observational study. Switzerland, Sweden, and Spain, which were militarily neutral and not involved in combat during either world war and have not been affected by significant changes in their territory since the end of the 19th century. Complete populations of these 3 countries. Continuous series of recorded deaths (from all causes) by month from the earliest available year (1877 for Switzerland, 1851 for Sweden, and 1908 for Spain) were jointly modeled with annual age group-specific death and total population counts using negative binomial and multinomial models, which accounted for temporal trends and seasonal variability of prepandemic years. The aim was to estimate the expected number of deaths in a pandemic year for a nonpandemic scenario and the difference in observed and expected deaths aggregated over the year. In 2020, the number of excess deaths recorded per 100000 persons was 100 (95% credible interval [CrI], 60 to 135) for Switzerland, 75 (CrI, 40 to 105) for Sweden, and 155 (CrI, 110 to 195) for Spain. In 1918, excess mortality was 6 to 7 times higher. In all 3 countries, the peaks of monthly excess mortality in 2020 were greater than most monthly excess mortality since 1918, including many peaks due to seasonal influenza and heat waves during that period. Historical vital statistics might be affected by minor completeness issues before the beginning of the 20th century. In 2020, the COVID-19 pandemic led to the second-largest infection-related mortality disaster in Switzerland, Sweden, and Spain since the beginning of the 20th century. Foundation for Research in Science and the Humanities at the University of Zurich, Swiss National Science Foundation, and National Institute of Allergy and Infectious Diseases.

Molecular epidemiologic characteristics of hemagglutinin from five waves of avian influenza A (H7N9) virus infection, from 2013 to 2017, in Zhejiang Province, China

There have been five waves of influenza A (H7N9) epidemics in Zhejiang Province between 2013 and 2017. Although the epidemiological characteristics of the five waves have been reported, the molecular genetics aspects, including the phylogeny, evolution, and mutation of hemagglutinin (HA), have not been systematically investigated. A total of 154 H7N9 samples from Zhejiang Province were collected between 2013 and 2017 and sequenced using an Ion Torrent Personal Genome Machine. The starting dates of the waves were 16 March 2013, 1 July 2013, 1 July 2014, 1 July 2015, and 1 July 2016. Single-nucleotide polymorphisms (SNPs) and amino acid mutations were counted after the HA sequences were aligned. The evolution of H7N9 matched the temporal order of the five waves, among which wave 3 played an important role. The 55 SNPs and 14 amino acid mutations with high frequency identified among the five waves revealed the dynamic occurrence of mutation in the process of viral dissemination. Wave 3 contributed greatly to the subsequent epidemic of waves 4 and 5 of H7N9. Compared with wave 1, wave 5 was characterized by more mutations, including A143V and R148K, two mutations that have been reported to weaken the immune response. In addition, some amino acid mutations were observed in wave 5 that led to more lineages. It is necessary to strengthen the surveillance of subsequent H7N9 influenza outbreaks.