Systematic Assessment of Multiple Routine and Near Real-Time Indicators to Classify the Severity of Influenza Seasons and Pandemics in the United States, 2003-2004 Through 2015-2016

Risk of healthcare visits from influenza in subjects with diabetes and impacts of early vaccination

INTRODUCTION

The objective of this study was to determine the burden of influenza disease in patients with or without diabetes in a population of American adults to understand the benefits of seasonal vaccination.

RESEARCH DESIGN AND METHODS

We performed a retrospective cohort study using electronic medical records totaling 1,117,263 from two Louisiana healthcare providers spanning January 2012 through December 2017. Adults 18 years or older with two or more records within the study period were included. The primary outcome quantified was influenza-related diagnosis during inpatient (IP) or emergency room (ER) visits and risk reduction with the timing of immunization.

RESULTS

Influenza-related IP or ER visits totaled 0.0122-0.0169 events per person within the 2013-2016 influenza seasons. Subjects with diabetes had a 5.6-fold more frequent influenza diagnosis for IP or ER visits than in subjects without diabetes or 3.7-fold more frequent when adjusted for demographics. Early immunization reduced the risk of influenza healthcare utilization by 66% for subjects with diabetes or 67% for subjects without diabetes when compared with later vaccination for the 2013-2016 influenza seasons. Older age and female sex were associated with a higher incidence of influenza, but not a significant change in risk reduction from vaccination.

CONCLUSIONS

The risk for influenza-related healthcare utilization was 3.7-fold higher if patients had diabetes during 2013-2016 influenza seasons. Early immunization provides a significant benefit to adults irrespective of a diabetes diagnosis. All adults, but particularly patients with diabetes, should be encouraged to get the influenza vaccine at the start of the influenza season.

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic

To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.

United States Influenza 2022-2023 Season Characteristics as Inferred from Wastewater Solids, Influenza Hospitalization, and Syndromic Data

Influenza A virus (IAV) causes significant morbidity and mortality in the United States and has pandemic potential. Identifying IAV epidemic patterns is essential to inform the timing of vaccinations and nonpharmaceutical interventions. In a prospective, longitudinal study design, we measured IAV RNA in wastewater settled solids at 163 wastewater treatment plants across 33 states to characterize the 2022-2023 influenza season at the state, health and human services (HHS) regional, and national scales. Influenza season onset, offset, duration, peak, and intensity using IAV RNA in wastewater were compared with those determined using laboratory-confirmed influenza hospitalization rates and outpatient visits for influenza-like illness (ILI). The onset for HHS regions as determined by IAV RNA in wastewater roughly corresponded with those determined using ILI when the annual geometric mean of IAV RNA concentration was used as a baseline (i.e., the threshold that triggers onset), although offsets between the two differed. IAV RNA in wastewater provided early warning of onset, compared to the ILI estimate, when the baseline was set at twice the limit of IAV RNA detection in wastewater. Peak when determined by IAV RNA in wastewater generally preceded peak determined by IAV hospitalization rate by 2 weeks or less. IAV RNA in wastewater settled solids is an IAV-specific indicator that can be used to augment clinical surveillance for seasonal influenza epidemic timing and intensity.

Trends in Outpatient Influenza Antiviral Use Among Children and Adolescents in the United States

BACKGROUND

Influenza antivirals improve outcomes in children with duration of symptoms <2 days and those at high risk for influenza complications. Real-world prescribing of influenza antivirals in the pediatric population is unknown.

METHODS

We performed a cross-sectional study of outpatient and emergency department prescription claims in individuals <18 years of age included in the IBM Marketscan Commercial Claims and Encounters Database between July 1, 2010 and June 30, 2019. Influenza antiviral use was defined as any dispensing of oseltamivir, baloxavir, or zanamivir. The primary outcome was the rate of antiviral dispensing per 1000 enrolled children. Secondary outcomes included antiviral dispensing per 1000 influenza diagnoses and inflation-adjusted costs of antiviral agents. Outcomes were calculated and stratified by age, acute versus prophylactic treatment, influenza season, and geographic region.

RESULTS

The analysis included 1 416 764 unique antiviral dispensings between 2010 and 2019. Oseltamivir was the most frequently prescribed antiviral (99.8%). Dispensing rates ranged from 4.4 to 48.6 per 1000 enrolled children. Treatment rates were highest among older children (12-17 years of age), during the 2017 to 2018 influenza season, and in the East South Central region. Guideline-concordant antiviral use among young children (<2 years of age) at a high risk of influenza complications was low (<40%). The inflation-adjusted cost for prescriptions was $208 458 979, and the median cost ranged from $111 to $151.

CONCLUSIONS

There is wide variability and underuse associated with influenza antiviral use in children. These findings reveal opportunities for improvement in the prevention and treatment of influenza in children.

Severity of influenza-associated hospitalisations by influenza virus type and subtype in the USA, 2010-19: a repeated cross-sectional study

BACKGROUND

Influenza burden varies across seasons, partly due to differences in circulating influenza virus types or subtypes. Using data from the US population-based surveillance system, Influenza Hospitalization Surveillance Network (FluSurv-NET), we aimed to assess the severity of influenza-associated outcomes in individuals hospitalised with laboratory-confirmed influenza virus infections during the 2010-11 to 2018-19 influenza seasons.

METHODS

To evaluate the association between influenza virus type or subtype causing the infection (influenza A H3N2, A H1N1pdm09, and B viruses) and in-hospital severity outcomes (intensive care unit [ICU] admission, use of mechanical ventilation or extracorporeal membrane oxygenation [ECMO], and death), we used FluSurv-NET to capture data for laboratory-confirmed influenza-associated hospitalisations from the 2010-11 to 2018-19 influenza seasons for individuals of all ages living in select counties in 13 US states. All individuals had to have an influenza virus test within 14 days before or during their hospital stay and an admission date between Oct 1 and April 30 of an influenza season. Exclusion criteria were individuals who did not have a complete chart review; cases from sites that contributed data for three or fewer seasons; hospital-onset cases; cases with unidentified influenza type; cases of multiple influenza virus type or subtype co-infection; or individuals younger than 6 months and ineligible for the influenza vaccine. Logistic regression models adjusted for influenza season, influenza vaccination status, age, and FluSurv-NET site compared odds of in-hospital severity by virus type or subtype. When missing, influenza A subtypes were imputed using chained equations of known subtypes by season.

FINDINGS

Data for 122 941 individuals hospitalised with influenza were captured in FluSurv-NET from the 2010-11 to 2018-19 seasons; after exclusions were applied, 107 941 individuals remained and underwent influenza A virus imputation when missing A subtype (43·4%). After imputation, data for 104 969 remained and were included in the final analytic sample. Averaging across imputed datasets, 57·7% (weighted percentage) had influenza A H3N2, 24·6% had influenza A H1N1pdm09, and 17·7% had influenza B virus infections; 16·7% required ICU admission, 6·5% received mechanical ventilation or ECMO, and 3·0% died (95% CIs had a range of less than 0·1% and are not displayed). Individuals with A H1N1pdm09 had higher odds of in-hospital severe outcomes than those with A H3N2: adjusted odds ratios (ORs) for A H1N1pdm09 versus A H3N2 were 1·42 (95% CI 1·32-1·52) for ICU admission; 1·79 (1·60-2·00) for mechanical ventilation or ECMO use; and 1·25 (1·07-1·46) for death. The adjusted ORs for individuals infected with influenza B versus influenza A H3N2 were 1·06 (95% CI 1·01-1·12) for ICU admission, 1·14 (1·05-1·24) for mechanical ventilation or ECMO use, and 1·18 (1·07-1·31) for death.

INTERPRETATION

Despite a higher burden of hospitalisations with influenza A H3N2, we found an increased likelihood of in-hospital severe outcomes in individuals hospitalised with influenza A H1N1pdm09 or influenza B virus. Thus, it is important for individuals to receive an annual influenza vaccine and for health-care providers to provide early antiviral treatment for patients with suspected influenza who are at increased risk of severe outcomes, not only when there is high influenza A H3N2 virus circulation but also when influenza A H1N1pdm09 and influenza B viruses are circulating.

FUNDING

The US Centers for Disease Control and Prevention.

High Influenza Incidence and Disease Severity Among Children and Adolescents Aged <18 Years - United States, 2022-23 Season

During the 2022-23 influenza season, early increases in influenza activity, co-circulation of influenza with other respiratory viruses, and high influenza-associated hospitalization rates, particularly among children and adolescents, were observed. This report describes the 2022-23 influenza season among children and adolescents aged <18 years, including the seasonal severity assessment; estimates of U.S. influenza-associated medical visits, hospitalizations, and deaths; and characteristics of influenza-associated hospitalizations. The 2022-23 influenza season had high severity among children and adolescents compared with thresholds based on previous seasons' influenza-associated outpatient visits, hospitalization rates, and deaths. Nationally, the incidences of influenza-associated outpatient visits and hospitalization for the 2022-23 season were similar for children aged <5 years and higher for children and adolescents aged 5-17 years compared with previous seasons. Peak influenza-associated outpatient and hospitalization activity occurred in late November and early December. Among children and adolescents hospitalized with influenza during the 2022-23 season in hospitals participating in the Influenza Hospitalization Surveillance Network, a lower proportion were vaccinated (18.3%) compared with previous seasons (35.8%-41.8%). Early influenza circulation, before many children and adolescents had been vaccinated, might have contributed to the high hospitalization rates during the 2022-23 season. Among symptomatic hospitalized patients, receipt of influenza antiviral treatment (64.9%) was lower than during pre-COVID-19 pandemic seasons (80.8%-87.1%). CDC recommends that all persons aged ≥6 months without contraindications should receive the annual influenza vaccine, ideally by the end of October.

Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study

Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.

Timing of seasonal influenza epidemics for 25 countries in Africa during 2010-19: a retrospective analysis

BACKGROUND

Using country-specific surveillance data to describe influenza epidemic activity could inform decisions on the timing of influenza vaccination. We analysed surveillance data from African countries to characterise the timing of seasonal influenza epidemics to inform national vaccination strategies.

METHODS

We used publicly available sentinel data from African countries reporting to the WHO Global Influenza Surveillance and Response FluNet platform that had 3-10 years of data collected during 2010-19. We calculated a 3-week moving proportion of samples positive for influenza virus and assessed epidemic timing using an aggregate average method. The start and end of each epidemic were defined as the first week when the proportion of positive samples exceeded or went below the annual mean, respectively, for at least 3 consecutive weeks. We categorised countries into five epidemic patterns: northern hemisphere-dominant, with epidemics occurring in October-March; southern hemisphere-dominant, with epidemics occurring in April-September; primarily northern hemisphere with some epidemic activity in southern hemisphere months; primarily southern hemisphere with some epidemic activity in northern hemisphere months; and year-round influenza transmission without a discernible northern hemisphere or southern hemisphere predominance (no clear pattern).

FINDINGS

Of the 34 countries reporting data to FluNet, 25 had at least 3 years of data, representing 46% of the countries in Africa and 89% of Africa's population. Study countries reported RT-PCR respiratory virus results for a total of 503 609 specimens (median 12 971 [IQR 9607-20 960] per country-year), of which 74 001 (15%; median 2078 [IQR 1087-3008] per country-year) were positive for influenza viruses. 248 epidemics occurred across 236 country-years of data (median 10 [range 7-10] per country). Six (24%) countries had a northern hemisphere pattern (Algeria, Burkina Faso, Egypt, Morocco, Niger, and Tunisia). Eight (32%) had a primarily northern hemisphere pattern with some southern hemisphere epidemics (Cameroon, Ethiopia, Mali, Mozambique, Nigeria, Senegal, Tanzania, and Togo). Three (12%) had a primarily southern hemisphere pattern with some northern hemisphere epidemics (Ghana, Kenya, and Uganda). Three (12%) had a southern hemisphere pattern (Central African Republic, South Africa, and Zambia). Five (20%) had no clear pattern (Côte d'Ivoire, DR Congo, Madagascar, Mauritius, and Rwanda).

INTERPRETATION

Most countries had identifiable influenza epidemic periods that could be used to inform authorities of non-seasonal and seasonal influenza activity, guide vaccine timing, and promote timely interventions.

FUNDING

None.

TRANSLATIONS

For the Berber, Luganda, Xhosa, Chewa, Yoruba, Igbo, Hausa and Afan Oromo translations of the abstract see Supplementary Materials section.

Evaluating Potential Impacts of a Preferential Vaccine Recommendation for Adults 65 Years of Age and Older on US Influenza Burden

BACKGROUND

High-dose, adjuvanted, and recombinant influenza vaccines may offer improved effectiveness among older adults compared with standard-dose, unadjuvanted, inactivated vaccines. However, the Advisory Committee on Immunization Practices (ACIP) only recently recommended preferential use of these "higher-dose or adjuvanted" vaccines. One concern was that individuals might delay or decline vaccination if a preferred vaccine is not readily available.

METHODS

We mathematically model how a recommendation for preferential use of higher-dose or adjuvanted vaccines in adults ≥65 years might impact influenza burden in the United States during exemplar "high-" and "low-"severity seasons. We assume higher-dose or adjuvanted vaccines are more effective than standard vaccines and that such a recommendation would increase uptake of the former but could cause (i) delays in administration of additional higher-dose or adjuvanted vaccines relative to standard vaccines and/or (ii) reductions in overall coverage if individuals only offered standard vaccines forego vaccination.

RESULTS

In a best-case scenario, assuming no delay or coverage reduction, a new recommendation could decrease hospitalizations and deaths in adults ≥65 years by 0%-4% compared with current uptake. However, intermediate and worst-case scenarios, with assumed delays of 3 or 6 weeks and/or 10% or 20% reductions in coverage, included projections in which hospitalizations and deaths increased by over 7%.

CONCLUSIONS

We estimate that increased use of higher-dose or adjuvanted vaccines could decrease influenza burden in adults ≥65 in the United States provided there is timely and adequate access to these vaccines, and that standard vaccines are administered when they are unavailable.

Early and Increased Influenza Activity Among Children - Tennessee, 2022-23 Influenza Season

Influenza seasons typically begin in October and peak between December and February (1); however, the 2022-23 influenza season in Tennessee began in late September and was characterized by high pediatric hospitalization rates during November. This report describes a field investigation conducted in Tennessee during November 2022, following reports of increasing influenza hospitalizations. Data from surveillance networks, patient surveys, and whole genome sequencing of influenza virus specimens were analyzed to assess influenza activity and secondary illness risk. Influenza activity increased earlier than usual among all age groups, and rates of influenza-associated hospitalization among children were high in November, reaching 12.6 per 100,000 in children aged <5 years, comparable to peak levels typically seen in high-severity seasons. Circulating influenza viruses were genetically similar to vaccine components. Among persons who received testing for influenza at outpatient clinics, children were twice as likely to receive a positive influenza test result as were adults. Among household contacts exposed to someone with influenza, children were more than twice as likely to become ill compared with adults. As the influenza season continues, it is important for all persons, especially those at higher risk for severe disease, to protect themselves from influenza. To prevent influenza and severe influenza complications, all persons aged ≥6 months should get vaccinated, avoid contact with ill persons, and take influenza antivirals if recommended and prescribed.

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Defining the South African Acute Respiratory Infectious Disease Season

The acute respiratory infectious disease season, or colloquially the "flu season", is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare.

Influenza During the 2010-2020 Decade in the United States: Seasonal Outbreaks and Vaccine Interventions

The 10 years between the last influenza pandemic and start of the severe acute respiratory syndrome coronavirus 2 pandemic have been marked by great advances in our ability to follow influenza occurrence and determine vaccine effectiveness (VE), largely based on widespread use of the polymerase chain reaction assay. We examine the results, focusing mainly on data from the United States and inactivated vaccines. Surveillance has expanded, resulting in increased ability to characterize circulating viruses and their impact. The surveillance has often confirmed previous observations on timing of outbreaks and age groups affected, which can now be examined in greater detail. Selection of strains for vaccines is now based on enhanced viral characterization using immunologic, virologic, and computational techniques not previously available. Vaccine coverage has been largely stable, but VE has remained modest and, in some years, very low. We discuss ways to improve VE based on existing technology while we work toward supraseasonal vaccines.

Antigenic drift and epidemiological severity of seasonal influenza in Canada

Seasonal influenza epidemics circulate globally every year with varying levels of severity. One of the major drivers of this seasonal variation is thought to be the antigenic drift of influenza viruses, resulting from the accumulation of mutations in viral surface proteins. In this study, we aimed to investigate the association between the genetic drift of seasonal influenza viruses (A/H1N1, A/H3N2 and B) and the epidemiological severity of seasonal epidemics within a Canadian context. We obtained hemagglutinin protein sequences collected in Canada between the 2006/2007 and 2019/2020 flu seasons from GISAID and calculated Hamming distances in a sequence-based approach to estimating inter-seasonal antigenic differences. We also gathered epidemiological data on cases, hospitalizations and deaths from national surveillance systems and other official sources, as well as vaccine effectiveness estimates to address potential effect modification. These aggregate measures of disease severity were integrated into a single seasonal severity index. We performed linear regressions of our severity index with respect to the inter-seasonal antigenic distances, controlling for vaccine effectiveness. We did not find any evidence of a statistical relationship between antigenic distance and seasonal influenza severity in Canada. Future studies may need to account for additional factors, such as co-circulation of other respiratory pathogens, population imprinting, cohort effects and environmental parameters, which may drive seasonal influenza severity.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Exploring Impacts to COVID-19 Herd Immunity Thresholds Under Demographic Heterogeneity that Lowers Vaccine Effectiveness

The COVID-19 pandemic has caused severe health, economic, and societal impacts across the globe. Although highly efficacious vaccines were developed at an unprecedented rate, the heterogeneity in vaccinated populations has reduced the ability to achieve herd immunity. Specifically, as of Spring 2022, the 0-4 year-old population is still unable to be vaccinated and vaccination rates across 5-11 year olds are low. Additionally, vaccine hesitancy for older populations has further stalled efforts to reach herd immunity thresholds. This heterogeneous vaccine landscape increases the challenge of anticipating disease spread in a population. We developed an age-structured Susceptible-Infectious-Recovered-type mathematical model to investigate the impacts of unvaccinated subpopulations on herd immunity. The model considers two types of undervaccination - age-related and behavior-related - by incorporating four age groups based on available FDA-approved vaccines. The model accounts for two different types of vaccines, mRNA (e.g., Pfizer, Moderna) and vector (e.g., Johnson and Johnson), as well as their effectiveness. Our goal is to analyze different scenarios to quantify which subpopulations and vaccine characteristics (e.g., rate or efficacy) most impact infection levels in the United States, using the state of New Mexico as an example.

Rates of Severe Influenza-Associated Outcomes Among Older Adults Living With Diabetes-Influenza Hospitalization Surveillance Network (FluSurv-NET), 2012-2017

BACKGROUND

Diabetes mellitus (DM) is common among older adults hospitalized with influenza, yet data are limited on the impact of DM on risk of severe influenza-associated outcomes.

METHODS

We included adults aged ≥65 years hospitalized with influenza during 2012-2013 through 2016-2017 from the Influenza Hospitalization Surveillance Network (FluSurv-NET), a population-based surveillance system for laboratory-confirmed influenza-associated hospitalizations conducted in defined counties within 13 states. We calculated population denominators using the Centers for Medicare and Medicaid Services county-specific DM prevalence estimates and National Center for Health Statistics population data. We present pooled rates and rate ratios (RRs) of intensive care unit (ICU) admission, pneumonia diagnosis, mechanical ventilation, and in-hospital death for persons with and without DM. We estimated RRs and 95% confidence intervals (CIs) using meta-analysis with site as a random effect in order to control for site differences in the estimates.

RESULTS

Of 31 934 hospitalized adults included in the analysis, 34% had DM. Compared to those without DM, adults with DM had higher rates of influenza-associated hospitalization (RR, 1.57 [95% CI, 1.43-1.72]), ICU admission (RR, 1.84 [95% CI, 1.67-2.04]), pneumonia (RR, 1.57 [95% CI, 1.42-1.73]), mechanical ventilation (RR, 1.95 [95% CI, 1.74-2.20]), and in-hospital death (RR, 1.48 [95% CI, 1.23-1.80]).

CONCLUSIONS

Older adults with DM have higher rates of severe influenza-associated outcomes compared to those without DM. These findings reinforce the importance of preventing influenza virus infections through annual vaccination, and early treatment of influenza illness with antivirals in older adults with DM.

Nuclear magnetic resonance reveals a two hairpin equilibrium near the 3'-splice site of influenza A segment 7 mRNA that can be shifted by oligonucleotides

Influenza A kills hundreds of thousands of people globally every year and has the potential to generate more severe pandemics. Influenza A's RNA genome and transcriptome provide many potential therapeutic targets. Here, nuclear magnetic resonance (NMR) experiments suggest that one such target could be a hairpin loop of 8 nucleotides in a pseudoknot that sequesters a 3' splice site in canonical pairs until a conformational change releases it into a dynamic 2 × 2-nt internal loop. NMR experiments reveal that the hairpin loop is dynamic and able to bind oligonucleotides as short as pentamers. A 3D NMR structure of the complex contains 4 and likely 5 bp between pentamer and loop. Moreover, a hairpin sequence was discovered that mimics the equilibrium of the influenza hairpin between its structure in the pseudoknot and upon release of the splice site. Oligonucleotide binding shifts the equilibrium completely to the hairpin secondary structure required for pseudoknot folding. The results suggest this hairpin can be used to screen for compounds that stabilize the pseudoknot and potentially reduce splicing.

Impact of Low Rates of Influenza on Next-Season Influenza Infections

INTRODUCTION

Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown.

METHODS

An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons.

RESULTS

When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one.

CONCLUSIONS

Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.

A Real-World Clinical and Economic Analysis of Cell-Derived Quadrivalent Influenza Vaccine Compared to Standard Egg-Derived Quadrivalent Influenza Vaccines During the 2019-2020 Influenza Season in the United States

Background

Cell-derived influenza vaccines are not subject to egg-adaptive mutations that have potential to decrease vaccine effectiveness. This retrospective analysis estimated the relative vaccine effectiveness (rVE) of cell-derived quadrivalent influenza vaccine (IIV4c) compared to standard egg-derived quadrivalent influenza vaccines (IIV4e) among recipients aged 4-64 years in the United States during the 2019-2020 influenza season.

Methods

The IQVIA PharMetrics Plus administrative claims database was utilized. Study outcomes were assessed postvaccination through the end of the study period (7 March 2020). Inverse probability of treatment weighting (IPTW) was implemented to adjust for covariate imbalance. Adjusted rVE against influenza-related hospitalizations/emergency room (ER) visits and other clinical outcomes was estimated through IPTW-weighted Poisson regression models for the IIV4c and IIV4e cohorts and for the subgroup with ≥1 high-risk condition. Sensitivity analyses modifying the outcome assessment period as well as a doubly-robust analysis were also conducted. IPTW-weighted generalized linear models were used to estimate predicted annualized all-cause costs.

Results

The final sample comprised 1 150 134 IIV4c and 3 924 819 IIV4e recipients following IPTW adjustment. IIV4c was more effective in preventing influenza-related hospitalizations/ER visits as well as respiratory-related hospitalizations/ER visits compared to IIV4e. IIV4c was also more effective for the high-risk subgroup and across the sensitivity analyses. IIV4c was also associated with significantly lower annualized all-cause total costs compared to IIV4e (-$467), driven by lower costs for outpatient medical services and inpatient hospitalizations.

Conclusions

IIV4c was significantly more effective in preventing influenza-related hospitalizations/ER visits compared to IIV4e and was associated with significantly lower all-cause costs.

Adaptively stacking ensembles for influenza forecasting

Seasonal influenza infects between 10 and 50 million people in the United States every year. Accurate forecasts of influenza and influenza-like illness (ILI) have been named by the CDC as an important tool to fight the damaging effects of these epidemics. Multi-model ensembles make accurate forecasts of seasonal influenza, but current operational ensemble forecasts are static: they require an abundance of past ILI data and assign fixed weights to component models at the beginning of a season, but do not update weights as new data on component model performance is collected. We propose an adaptive ensemble that (i) does not initially need data to combine forecasts and (ii) finds optimal weights which are updated week-by-week throughout the influenza season. We take a regularized likelihood approach and investigate this regularizer's ability to impact adaptive ensemble performance. After finding an optimal regularization value, we compare our adaptive ensemble to an equal-weighted and static ensemble. Applied to forecasts of short-term ILI incidence at the regional and national level, our adaptive model outperforms an equal-weighted ensemble and has similar performance to the static ensemble using only a fraction of the data available to the static ensemble. Needing no data at the beginning of an epidemic, an adaptive ensemble can quickly train and forecast an outbreak, providing a practical tool to public health officials looking for a forecast to conform to unique features of a specific season.

Distinct influenza surveillance networks and their agreement in recording regional influenza circulation: Experience from Southeast Michigan

INTRODUCTION

In Southeast Michigan, active surveillance studies monitor influenza activity in hospitals, ambulatory clinics, and community households. Across five respiratory seasons, we assessed the contribution of data from each of the three networks towards improving our overall understanding of regional influenza circulation.

METHODS

All three networks used case definitions for acute respiratory illness (ARI) and molecularly tested for influenza from research-collected respiratory specimens. Age- and network-stratified epidemic curves were created for influenza A and B. We compared stratified epidemic curves visually and by centering at seasonal midpoints.

RESULTS

Across all seasons (from 2014/2015 through 2018/2019), epidemic curves from each of the three networks were comparable in terms of both timing and magnitude. Small discrepancies in epidemics recorded by each network support previous conclusions about broader characteristics of particular influenza seasons.

CONCLUSION

Influenza surveillance systems based in hospital, ambulatory clinic, and community household settings appear to provide largely similar information regarding regional epidemic activity. Together, multiple levels of influenza surveillance provide a detailed view of regional influenza epidemics, but a single surveillance system-regardless of population subgroup monitored-appears to be sufficient in providing vital information regarding community influenza epidemics.

Influenza Vaccine Effectiveness for Prevention of Severe Influenza-Associated Illness Among Adults in the United States, 2019-2020: A Test-Negative Study

BACKGROUND

Influenza vaccine effectiveness (VE) against a spectrum of severe disease, including critical illness and death, remains poorly characterized.

METHODS

We conducted a test-negative study in an intensive care unit (ICU) network at 10 US hospitals to evaluate VE for preventing influenza-associated severe acute respiratory infection (SARI) during the 2019-2020 season, which was characterized by circulation of drifted A/H1N1 and B-lineage viruses. Cases were adults hospitalized in the ICU and a targeted number outside the ICU (to capture a spectrum of severity) with laboratory-confirmed, influenza-associated SARI. Test-negative controls were frequency-matched based on hospital, timing of admission, and care location (ICU vs non-ICU). Estimates were adjusted for age, comorbidities, and other confounders.

RESULTS

Among 638 patients, the median (interquartile) age was 57 (44-68) years; 286 (44.8%) patients were treated in the ICU and 42 (6.6%) died during hospitalization. Forty-five percent of cases and 61% of controls were vaccinated, which resulted in an overall VE of 32% (95% CI: 2-53%), including 28% (-9% to 52%) against influenza A and 52% (13-74%) against influenza B. VE was higher in adults 18-49 years old (62%; 95% CI: 27-81%) than those aged 50-64 years (20%; -48% to 57%) and ≥65 years old (-3%; 95% CI: -97% to 46%) (P = .0789 for interaction). VE was significantly higher against influenza-associated death (80%; 95% CI: 4-96%) than nonfatal influenza illness.

CONCLUSIONS

During a season with drifted viruses, vaccination reduced severe influenza-associated illness among adults by 32%. VE was high among young adults.

Recommendations for Prevention and Control of Influenza in Children, 2021-2022

This technical report accompanies the recommendations of the American Academy of Pediatrics for the routine use of the influenza vaccine and antiviral medications in the prevention and treatment of influenza in children during the 2021-2022 season. Influenza vaccination is an important intervention to protect vulnerable populations and reduce the burden of respiratory illnesses during circulation of severe acute respiratory syndrome coronavirus 2, which is expected to continue during this influenza season. In this technical report, we summarize recent influenza seasons, morbidity and mortality in children, vaccine effectiveness, vaccination coverage, and detailed guidance on storage, administration, and implementation. We also provide background on inactivated and live attenuated influenza vaccine recommendations, vaccination during pregnancy and breastfeeding, diagnostic testing, and antiviral medications for treatment and chemoprophylaxis.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons

Background

Children are important in community‐level influenza transmission. School‐based monitoring may inform influenza surveillance.

Methods

We used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010‐2015 influenza seasons using Pennsylvania's Allegheny County Health Department all‐age influenza cases from health facilities, and all‐cause and influenza‐like illness (ILI)‐specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all‐cause and illness‐specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross‐validations.

Results

School districts reported 2 184 220 all‐cause absences (2010‐2015). Three one‐season studies reported 19 577 all‐cause and 3012 ILI‐related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K‐5 grade‐specific absence models had lowest mean absolute errors (MAE) in cross‐validations. ILI‐specific absences performed marginally better than all‐cause absences in two years, adjusting for other covariates, but markedly worse one year.

Conclusions

Our findings suggest seasonal models including K‐5th grade absences predict all‐age‐confirmed influenza and may serve as a useful surveillance tool.

A retrospective cohort study assessing relative effectiveness of adjuvanted versus high-dose trivalent influenza vaccines among older adults in the United States during the 2018-19 influenza season

PURPOSE

To evaluate the relative vaccine effectiveness (rVE) against influenza-related hospitalizations/emergency room (ER) visits, influenza-related office visits, and cardio-respiratory disease (CRD)-related hospitalizations/ER visits and compare all-cause and influenza-related costs associated with two vaccines specifically indicated for older adults (≥65 years), adjuvanted (aTIV) and high-dose trivalent influenza vaccine (TIV-HD), for the 2018-19 influenza season.

METHODS

A retrospective analysis of older adults was conducted using claims and hospital data in the United States. For clinical evaluations, adjusted analyses were conducted following inverse probability of treatment weighting (IPTW) to control for selection bias. Poisson regression was used to estimate the adjusted rVE against influenza-related hospitalizations/ER visits, influenza-related office visits, and any CRD-related hospitalizations/ER visits. For the economic evaluation, treatment selection bias was adjusted through 1:1 propensity score matching (PSM). All-cause and influenza-related costs associated with hospitalizations/ER, physician office and pharmacy visits were adjusted using generalized estimating equation (GEE) models.

RESULTS

After IPTW and Poisson regression, aTIV (n = 561,315) was slightly more effective in reducing influenza-related office visits compared to TIV-HD (n = 1,672,779) (6.6%; 95% CI: 2.8-10.3%). aTIV was statistically comparable to TIV-HD (2.0%; 95% CI: -3.7%-7.3%) in preventing influenza-related hospitalizations/ER visits but more effective in reducing hospitalizations/ER visits for any CRD (2.6%; 95% CI: 2.0-3.2%). In the PSM-adjusted cohorts (n = 561,243 pairs), following GEE adjustments, predicted mean annualized all-cause and influenza-related total costs per patient were statistically similar between aTIV and TIV-HD (US$9676 vs. US$9625 and US$18.74 vs. US$17.28, respectively; both p > 0.05). Finally, influenza-related pharmacy costs were slightly lower for aTIV as compared to TIV-HD ($1.75 vs $1.85; p < 0.0001).

CONCLUSIONS

During the 2018-19 influenza season, influenza-related hospitalization/ER visits and associated costs among people aged ≥ 65 were comparable between aTIV and TIV-HD. aTIV was slightly more effective in preventing influenza-related office visits and any CRD event as compared to TIV-HD in this population.

Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic

The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8-28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6-7) including 1,307 participants was 14.9% (95% CI 11.5-19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7-17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3-24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2-48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5-92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population.

The moving epidemic method applied to influenza surveillance in Guangdong, China

OBJECTIVES

The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China.

METHODS

Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure.

RESULTS

For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%.

CONCLUSIONS

Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.

Clinical and Economic Outcomes Associated with Cell-Based Quadrivalent Influenza Vaccine vs. Standard-Dose Egg-Based Quadrivalent Influenza Vaccines during the 2018-19 Influenza Season in the United States

Non-egg-based influenza vaccines eliminate the potential for egg-adapted mutations and potentially increase vaccine effectiveness. This retrospective study compared hospitalizations/emergency room (ER) visits and all-cause annualized healthcare costs among subjects aged 4–64 years who received cell-based quadrivalent (QIVc) or standard-dose egg-based quadrivalent (QIVe-SD) influenza vaccine during the 2018–19 influenza season. Administrative claims data (IQVIA PharMetrics^® Plus, IQVIA, USA) were utilized to evaluate clinical and economic outcomes. Adjusted relative vaccine effectiveness (rVE) of QIVc vs. QIVe-SD among overall cohort, as well as for three subgroups (age 4–17 years, age 18–64 years, and high-risk) was evaluated using inverse probability of treatment weighting (IPTW) and Poisson regression models. Generalized estimating equation models among the propensity score matched sample were used to estimate annualized all-cause costs. A total of 669,030 recipients of QIVc and 3,062,797 of QIVe-SD were identified after IPTW adjustments. Among the overall cohort, QIVc had higher adjusted rVEs against hospitalizations/ER visits related to influenza, all-cause hospitalizations, and hospitalizations/ER visits associated with any respiratory event compared to QIVe-SD. The adjusted annualized all-cause total costs were higher for QIVe-SD compared to QIVc ((+$461); p < 0.05).

SARS-CoV-2 Testing for Asymptomatic Patients with Cancer Prior and during Treatment: A Single Centre Experience

Patients with cancer are more vulnerable to severe COVID-19. As a result, routine SARS-CoV-2 testing of asymptomatic patients with cancer is recommended prior to treatment. However, there is limited evidence of its clinical usefulness. The objective of this study is to evaluate the value of routine testing of asymptomatic patients with cancer. Asymptomatic patients with cancer attending Odette Cancer Centre (Toronto, ON, Canada) were tested for SARS-CoV-2 prior to and during treatment cycles. Results were compared to positivity rates of SARS-CoV-2 locally and provincially. All 890 asymptomatic patients tested negative. Positivity rates in the province were 1.5%, in hospital were 1.0%, and among OCC's symptomatic cancer patients were 0% over the study period. Given our findings and the low SARS-CoV-2 community positivity rates, we recommend a dynamic testing model of asymptomatic patients that triggers testing during increasing community positivity rates of SARS-CoV-2.

Influenza and Influenza Vaccine: A Review

Influenza is a highly contagious, deadly virus, killing nearly half a million people yearly worldwide. The classic symptoms of influenza are fever, fatigue, cough, and body aches. In the outpatient setting, diagnosis can be made by clinical presentation with optional confirmatory diagnostic testing. Antiviral medications should be initiated as soon as possible, preferably within 24 hours of initiation of symptoms. The primary preventive measure against influenza is vaccination, which is recommended for all people 6 months of age or older, including pregnant and postpartum women, unless the individual has a contraindication. Vaccination should occur at the beginning of flu season, which typically begins in October. It takes approximately 14 days after vaccination for a healthy adult to reach peak antibody protection. There are challenges associated with vaccine composition and vaccine uptake. It takes approximately 6 to 8 months to identify and predict which influenza strains to include in the upcoming season's vaccine. During this time, the influenza virus may undergo antigenic drift, that is, mutating to avoid a host immune response. Antigenic drift makes the vaccine less effective in some seasons. The influenza virus occasionally undergoes antigenic shift, in which it changes to a novel virus, creating potential for a pandemic. There are also barriers to vaccine uptake, including lack of or limited access to care and misconceptions about receiving the vaccine. Interventions that improve access to and uptake of the influenza vaccine must be initiated, targeting multiple levels, including health care policy, patients, health care systems, and the health care team. This article reviews information about influenza identification, management, and prevention.

Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18

BackgroundUnderstanding influenza seasonality is necessary for determining policies for influenza control.AimWe characterised transmissibility during seasonal influenza epidemics, including one influenza pandemic, in Spain during the 21th century by using the moving epidemic method (MEM) to calculate intensity levels and estimate differences across seasons and age groups.MethodsWe applied the MEM to Spanish Influenza Sentinel Surveillance System data from influenza seasons 2001/02 to 2017/18. A modified version of Goldstein's proxy was used as an epidemiological-virological parameter. We calculated the average starting week and peak, the length of the epidemic period and the length from the starting week to the peak of the epidemic, by age group and according to seasonal virus circulation.ResultsIndividuals under 15 years of age presented higher transmissibility, especially in the 2009 influenza A(H1N1) pandemic. Seasons with dominance/co-dominance of influenza A(H3N2) virus presented high intensities in older adults. The 2004/05 influenza season showed the highest influenza-intensity level for all age groups. In 12 seasons, the epidemic started between week 50 and week 3. Epidemics started earlier in individuals under 15 years of age (-1.8 weeks; 95% confidence interval (CI):-2.8 to -0.7) than in those over 64 years when influenza B virus circulated as dominant/co-dominant. The average time from start to peak was 4.3 weeks (95% CI: 3.6-5.0) and the average epidemic length was 8.7 weeks (95% CI: 7.9-9.6).ConclusionsThese findings provide evidence for intensity differences across seasons and age groups, and can be used guide public health actions to diminish influenza-related morbidity and mortality.

Projected Population Benefit of Increased Effectiveness and Coverage of Influenza Vaccination on Influenza Burden in the United States

BACKGROUND

Vaccination is the best way to prevent influenza; however, greater benefits could be achieved. To help guide research and policy agendas, we aimed to quantify the magnitude of influenza disease that would be prevented through targeted increases in vaccine effectiveness (VE) or vaccine coverage (VC).

METHODS

For 3 influenza seasons (2011-12, 2015-16, and 2017-18), we used a mathematical model to estimate the number of prevented influenza-associated illnesses, medically attended illnesses, and hospitalizations across 5 age groups. Compared with estimates of prevented illness during each season, given observed VE and VC, we explored the number of additional outcomes that would have been prevented from a 5% absolute increase in VE or VC or from achieving 60% VE or 70% VC.

RESULTS

During the 2017-18 season, compared with the burden already prevented by influenza vaccination, a 5% absolute VE increase would have prevented an additional 1 050 000 illnesses and 25 000 hospitalizations (76% among those aged ≥65 years), while achieving 60% VE would have prevented an additional 190 000 hospitalizations. A 5% VC increase would have resulted in 785 000 fewer illnesses (56% among those aged 18-64 years) and 11 000 fewer hospitalizations; reaching 70% would have prevented an additional 39 000 hospitalizations.

CONCLUSIONS

Small, attainable improvements in effectiveness or VC of the influenza vaccine could lead to substantial additional reductions in the influenza burden in the United States. Improvements in VE would have the greatest impact in reducing hospitalizations in adults aged ≥65 years, and VC improvements would have the largest benefit in reducing illnesses in adults aged 18-49 years.

Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza-like illness surveillance data 2009-2018 in Tunisia

Background

Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method.

Methods

We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure.

Results

The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate.

Conclusions

This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control.

Real-time estimation of the influenza-associated excess mortality in Hong Kong

Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas

We developed a contactless syndromic surveillance platform FluSense that aims to expand the current paradigm of influenza-like illness (ILI) surveillance by capturing crowd-level bio-clinical signals directly related to physical symptoms of ILI from hospital waiting areas in an unobtrusive and privacy-sensitive manner. FluSense consists of a novel edge-computing sensor system, models and data processing pipelines to track crowd behaviors and influenza-related indicators, such as coughs, and to predict daily ILI and laboratory-confirmed influenza caseloads. FluSense uses a microphone array and a thermal camera along with a neural computing engine to passively and continuously characterize speech and cough sounds along with changes in crowd density on the edge in a real-time manner. We conducted an IRB-approved 7 month-long study from December 10, 2018 to July 12, 2019 where we deployed FluSense in four public waiting areas within the hospital of a large university. During this period, the FluSense platform collected and analyzed more than 350,000 waiting room thermal images and 21 million non-speech audio samples from the hospital waiting areas. FluSense can accurately predict daily patient counts with a Pearson correlation coefficient of 0.95. We also compared signals from FluSense with the gold standard laboratory-confirmed influenza case data obtained in the same facility and found that our sensor-based features are strongly correlated with laboratory-confirmed influenza trends.

Characteristics of hospitalizations with an influenza diagnosis, France, 2012-2013 to 2016-2017 influenza seasons

Background

Estimating the global burden of influenza hospitalizations is required to allocate resources and assess interventions that aim to prevent severe influenza. In France, the current routine influenza surveillance system does not fully measure the burden of severe influenza cases. The objective was to describe the characteristics and severity of influenza hospitalizations by age‐group and by season between 2012 and 2017.

Methods

All hospitalizations with a diagnosis of influenza in metropolitan France between July 2012 and June 2017 were extracted from the French national hospital discharge database (PMSI). For each season, the total number of influenza hospitalizations, admissions to intensive care units (ICU), proportion of deaths, lengths of stay, and distribution in diagnosis‐related groups were described by age‐group.

Results

Over the five seasons, 91 255 hospitalizations with a diagnosis of influenza were identified. The average influenza hospitalization rate varied from 13/100 000 in 2013‐2014 to 46/100 000 in 2016‐2017. A high rate was observed in elderlies during the 2014‐2015 and 2016‐2017 seasons, dominated by A(H3N2) virus. The youngest were impacted in 2015‐2016, dominated by B/Victoria virus. The proportion of influenza hospitalizations with ICU admission was 10%, and was higher in age‐group 40‐79 years. The proportion of deaths and length of stay increased with age.

Conclusions

The description of influenza hospitalizations recorded in the PMSI give key information on the burden of severe influenza in France. Analyses of these data annually is valuable in order to document the severity of influenza hospitalizations by age‐group and according to the circulating influenza viruses.

Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence.

Social Media- and Internet-Based Disease Surveillance for Public Health

Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.

Comparative Effectiveness of High-Dose Versus Standard-Dose Influenza Vaccine Among Patients Receiving Maintenance Hemodialysis

RATIONALE & OBJECTIVE

Studies of patients on maintenance dialysis therapy suggest that standard-dose influenza vaccine (SDV) may not prevent influenza-related outcomes. Little is known about the comparative effectiveness of SDV versus high-dose influenza vaccine (HDV) in this population.

STUDY DESIGN

Cohort study using data from the US Renal Data System.

SETTING & PARTICIPANTS

507,552 adults undergoing in-center maintenance hemodialysis between the 2010 to 2011 and 2014 to 2015 influenza seasons.

EXPOSURES

SDV and HDV.

OUTCOMES

All-cause mortality, hospitalization due to influenza or pneumonia, and influenza-like illness during the influenza season.

ANALYTIC APPROACH

Patients were eligible for inclusion in multiple yearly cohorts; thus, our unit of analysis was the influenza patient-season. To examine the relationship between vaccine dose and effectiveness outcomes, we estimated risk differences and risk ratios using propensity score weighting of Kaplan-Meier functions, accounting for a wide range of patient- and facility-level characteristics. For nonmortality outcomes, we used competing-risk methods to account for the high mortality rate in the dialysis population.

RESULTS

Within 225,215 influenza patient-seasons among adults 65 years and older, 97.4% received SDV and 2.6% received HDV. We observed similar risk estimates for HDV and SDV recipients for mortality (risk difference, -0.08%; 95% CI, -0.85% to 0.80%), hospitalization due to influenza or pneumonia (risk difference, 0.15%; 95% CI, -0.69% to 0.93%), and influenza-like illness (risk difference, 0.00%; 95% CI, -1.50% to 1.08%). Our findings were similar among adults younger than 65 years, as well as within other subgroups defined by influenza season, age group, dialysis vintage, month of influenza vaccination, and vaccine valence.

LIMITATIONS

Residual confounding and outcome misclassification.

CONCLUSIONS

The HDV does not appear to provide additional protection beyond the SDV against all-cause mortality or influenza-related outcomes for adults undergoing hemodialysis. The additional cost and side effects associated with HDV should be considered when offering this vaccine. Future studies of HDV and other influenza vaccine strategies are warranted.

Effects of Influenza Vaccination in the United States During the 2017-2018 Influenza Season

BACKGROUND

The severity of the 2017-2018 influenza season in the United States was high, with influenza A(H3N2) viruses predominating. Here, we report influenza vaccine effectiveness (VE) and estimate the number of vaccine-prevented influenza-associated illnesses, medical visits, hospitalizations, and deaths for the 2017-2018 influenza season.

METHODS

We used national age-specific estimates of 2017-2018 influenza vaccine coverage and disease burden. We estimated VE against medically attended reverse-transcription polymerase chain reaction-confirmed influenza virus infection in the ambulatory setting using a test-negative design. We used a compartmental model to estimate numbers of influenza-associated outcomes prevented by vaccination.

RESULTS

The VE against outpatient, medically attended, laboratory-confirmed influenza was 38% (95% confidence interval [CI], 31%-43%), including 22% (95% CI, 12%-31%) against influenza A(H3N2), 62% (95% CI, 50%-71%) against influenza A(H1N1)pdm09, and 50% (95% CI, 41%-57%) against influenza B. We estimated that influenza vaccination prevented 7.1 million (95% CrI, 5.4 million-9.3 million) illnesses, 3.7 million (95% CrI, 2.8 million-4.9 million) medical visits, 109 000 (95% CrI, 39 000-231 000) hospitalizations, and 8000 (95% credible interval [CrI], 1100-21 000) deaths. Vaccination prevented 10% of expected hospitalizations overall and 41% among young children (6 months-4 years).

CONCLUSIONS

Despite 38% VE, influenza vaccination reduced a substantial burden of influenza-associated illness, medical visits, hospitalizations, and deaths in the United States during the 2017-2018 season. Our results demonstrate the benefit of current influenza vaccination and the need for improved vaccines.

Seasonal Incidence of Symptomatic Influenza in the United States

Background

The seasonal incidence of influenza is often approximated as 5%-20%.

Methods

We used 2 methods to estimate the seasonal incidence of symptomatic influenza in the United States. First, we made a statistical estimate extrapolated from influenza-associated hospitalization rates for 2010-2011 to 2015-2016, collected as part of national surveillance, covering approximately 9% of the United States, and including the existing mix of vaccinated and unvaccinated persons. Second, we performed a literature search and meta-analysis of published manuscripts that followed cohorts of subjects during 1996-2016 to detect laboratory-confirmed symptomatic influenza among unvaccinated persons; we adjusted this result to the US median vaccination coverage and effectiveness during 2010-2016.

Results

The statistical estimate of influenza incidence among all ages ranged from 3.0%-11.3% among seasons, with median values of 8.3% (95% confidence interval [CI], 7.3%-9.7%) for all ages, 9.3% (95% CI, 8.2%-11.1%) for children <18 years, and 8.9% (95% CI, 8.2%-9.9%) for adults 18-64 years. Corresponding values for the meta-analysis were 7.1% (95% CI, 6.1%-8.1%) for all ages, 8.7% (95% CI, 6.6%-10.5%) for children, and 5.1% (95% CI, 3.6%-6.6%) for adults.

Conclusions

The 2 approaches produced comparable results for children and persons of all ages. The statistical estimates are more versatile and permit estimation of season-to-season variation. During 2010-2016, the incidence of symptomatic influenza among vaccinated and unvaccinated US residents, including both medically attended and nonattended infections, was approximately 8% and varied from 3% to 11% among seasons.

Recommendations for Prevention and Control of Influenza in Children, 2019-2020

This statement updates the recommendations of the American Academy of Pediatrics for the routine use of influenza vaccines and antiviral medications in the prevention and treatment of influenza in children during the 2019-2020 season. The American Academy of Pediatrics continues to recommend routine influenza immunization of all children without medical contraindications, starting at 6 months of age. Any licensed, recommended, age-appropriate vaccine available can be administered, without preference of one product or formulation over another. Antiviral treatment of influenza with any licensed, recommended, age-appropriate influenza antiviral medication continues to be recommended for children with suspected or confirmed influenza, particularly those who are hospitalized, have severe or progressive disease, or have underlying conditions that increase their risk of complications of influenza.

Update: Influenza Activity in the United States During the 2018-19 Season and Composition of the 2019-20 Influenza Vaccine

Influenza activity* in the United States during the 2018-19 season (September 30, 2018-May 18, 2019) was of moderate severity (1). Nationally, influenza-like illness (ILI)† activity began increasing in November, peaked during mid-February, and returned to below baseline in mid-April; the season lasted 21 weeks,§ making it the longest season in 10 years. Illness attributed to influenza A viruses predominated, with very little influenza B activity. Two waves of influenza A were notable during this extended season: influenza A(H1N1)pdm09 viruses from October 2018 to mid-February 2019 and influenza A(H3N2) viruses from February through May 2019. Compared with the 2017-18 influenza season, rates of hospitalization this season were lower for adults, but were similar for children. Although influenza activity is currently below surveillance baselines, testing for seasonal influenza viruses and monitoring for novel influenza A virus infections should continue year-round. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences.

Excessive neutrophil levels in the lung underlie the age-associated increase in influenza mortality

Neutrophils clear viruses, but excessive neutrophil responses induce tissue injury and worsen disease. Aging increases mortality to influenza infection; however, whether this is due to impaired viral clearance or a pathological host immune response is unknown. Here we show that aged mice have higher levels of lung neutrophils than younger mice after influenza viral infection. Depleting neutrophils after, but not before, infection substantially improves the survival of aged mice without altering viral clearance. Aged alveolar epithelial cells (AECs) have a higher frequency of senescence and secrete higher levels of the neutrophil-attracting chemokines CXCL1 and CXCL2 during influenza infection. These chemokines are required for age-enhanced neutrophil chemotaxis in vitro. Our work suggests that aging increases mortality from influenza in part because senescent AECs secrete more chemokines, leading to excessive neutrophil recruitment. Therapies that mitigate this pathological immune response in the elderly might improve outcomes of influenza and other respiratory infections.

A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.