The geographic synchrony of seasonal influenza: a waves across Canada and the United States

Mortality associated with Omicron and influenza infections in France before and during the COVID-19 pandemic

For many deaths associated with influenza and Omicron infections, those viruses are not detected. We applied previously developed methodology to estimate the contribution of influenza and Omicron infections to all-cause mortality in France for the 2014-2015 through the 2018-2019 influenza seasons, and the period between week 33, 2022 and week 12, 2023. For the 2014-2015 through the 2018-2019 seasons, influenza was associated with annual average of 15,654 (95% CI (13,013, 18,340)) deaths, while between week 33, 2022 and week 12, 2023, we estimated 7,851 (5,213, 10,463) influenza-associated deaths and 32,607 (20,794, 44,496) SARS-CoV-2 associated deaths. For many Omicron-associated deaths for cardiac disease, mental&behavioural disorders, and other causes, Omicron infections are not characterised as a contributing cause of death - for example, between weeks 33-52 in 2022, we estimated 23,983 (15,307, 32,620) SARS-CoV-2-associated deaths in France, compared with 12,811 deaths with COVID-19 listed on death certificate. Our results suggest the need for boosting influenza vaccination coverage in different population groups in France, and for wider detection of influenza infections in respiratory illness episodes (including pneumonia) in combination with the use of antiviral medications. For Omicron epidemics, wider detection of Omicron infections in persons with underlying health conditions is needed.

Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw

In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland-Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013-2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality

Computational surveillance of pneumonia and influenza mortality in the United States using FluView uses epidemic thresholds to identify high mortality rates but is limited by statistical issues such as seasonality and autocorrelation. We used time series anomaly detection to improve recognition of high mortality rates. Results suggest that anomaly detection can complement mortality reporting.

Shifting patterns of seasonal influenza epidemics

Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.

Deploying digital health data to optimize influenza surveillance at national and local scales

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.

Synchronicity of influenza activity within Phoenix, AZ during the 2015-2016 seasonal epidemic

Background

Variability in the timing of influenza epidemics has been observed across global and regional scales, but this variability has not been studied extensively at finer spatial scales. As such, the aim of this study was to test whether influenza cases were synchronized across sites and/or age-groups within a major city.

Methods

We used influenza cases identified by rapid influenza tests from a network of clinics across Phoenix, AZ during the 2015–2016 influenza A season. We used a combination of KS tests and a bootstrapping approach to evaluate whether the temporal distribution of cases varied by site and/or age group.

Results

Our analysis indicates that the timing of influenza cases during the 2015–2016 seasonal influenza epidemic were generally synchronized across sites and age groups. That said, we did observe some statistically significant differences in the timing of cases across some sites, and by site and age group. We found no evidence that influenza activity consistently begins or peaks earlier in children than in adults.

Conclusions

To our knowledge, this is the first study to investigate differences in the intra-urban timing of influenza using influenza-specific case data. We were able to show evidence that influenza cases are not entirely synchronized across an urban area, but the differences we observed were relatively minor. It is important to understand the geographic scale at which influenza is synchronized in order to gain a better understanding of local transmission dynamics, and to determine the appropriate geographic scale that influenza surveillance data should be aggregated for prediction and warning systems.

Temporal Patterns of Influenza A and B in Tropical and Temperate Countries: What Are the Lessons for Influenza Vaccination?

Introduction

Determining the optimal time to vaccinate is important for influenza vaccination programmes. Here, we assessed the temporal characteristics of influenza epidemics in the Northern and Southern hemispheres and in the tropics, and discuss their implications for vaccination programmes.

Methods

This was a retrospective analysis of surveillance data between 2000 and 2014 from the Global Influenza B Study database. The seasonal peak of influenza was defined as the week with the most reported cases (overall, A, and B) in the season. The duration of seasonal activity was assessed using the maximum proportion of influenza cases during three consecutive months and the minimum number of months with ≥80% of cases in the season. We also assessed whether co-circulation of A and B virus types affected the duration of influenza epidemics.

Results

212 influenza seasons and 571,907 cases were included from 30 countries. In tropical countries, the seasonal influenza activity lasted longer and the peaks of influenza A and B coincided less frequently than in temperate countries. Temporal characteristics of influenza epidemics were heterogeneous in the tropics, with distinct seasonal epidemics observed only in some countries. Seasons with co-circulation of influenza A and B were longer than influenza A seasons, especially in the tropics.

Discussion

Our findings show that influenza seasonality is less well defined in the tropics than in temperate regions. This has important implications for vaccination programmes in these countries. High-quality influenza surveillance systems are needed in the tropics to enable decisions about when to vaccinate.

Leading Indicators and the Evaluation of the Performance of Alerts for Influenza Epidemics

BACKGROUND

Most evaluations of epidemic thresholds for influenza have been limited to internal criteria of the indicator variable. We aimed to initiate discussion on appropriate methods for evaluation and the value of cross-validation in assessing the performance of a candidate indicator for influenza activity.

METHODS

Hospital records of in-patients with a diagnosis of confirmed influenza were extracted from the Canadian Discharge Abstract Database from 2003 to 2011 and aggregated to weekly and regional levels, yielding 7 seasons and 4 regions for evaluation (excluding the 2009 pandemic period). An alert created from the weekly time-series of influenza positive laboratory tests (FluWatch, Public Health Agency of Canada) was evaluated against influenza-confirmed hospitalizations on 5 criteria: lead/lag timing; proportion of influenza hospitalizations covered by the alert period; average length of the influenza alert period; continuity of the alert period and length of the pre-peak alert period.

RESULTS

Influenza hospitalizations led laboratory positive tests an average of only 1.6 (95% CI: -1.5, 4.7) days. However, the difference in timing exceeded 1 week and was statistically significant at the significance level of 0.01 in 5 out of 28 regional seasons. An alert based primarily on 5% positivity and 15 positive tests produced an average alert period of 16.6 weeks. After allowing for a reporting delay of 2 weeks, the alert period included 80% of all influenza-confirmed hospitalizations. For 20 out of the 28 (71%) seasons, the first alert would have been signalled at least 3 weeks (in real time) prior to the week with maximum number of influenza hospitalizations.

CONCLUSIONS

Virological data collected from laboratories was a good indicator of influenza activity with the resulting alert covering most influenza hospitalizations and providing a reasonable pre-peak warning at the regional level. Though differences in timing were statistically significant, neither time-series consistently led the other.

An IDEA for short term outbreak projection: nearcasting using the basic reproduction number

BACKGROUND

Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth.

METHODOLOGY

The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts).

PRINCIPAL FINDINGS

Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1(th) serial interval using data from the i(th) serial interval within an average of 20% of actual incidence.

CONCLUSIONS AND SIGNIFICANCE

This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists.

Distribution of influenza and other acute respiratory viruses during the first year after the 2009-2010 influenza pandemic in the English- and Dutch-speaking Caribbean countries

BACKGROUND

Limited specimen collection and testing for influenza occurred in the English and Dutch-speaking Caribbean countries prior to the 2009/2010 influenza pandemic. Caribbean Epidemiology Centre (CAREC) member countries rapidly mobilized to collect specimens during the pandemic and a vast majority of confirmed cases during the pandemic period were influenza A(H1N1)pdm09.

OBJECTIVES

To describe the aetiology and distribution of acute respiratory illness (ARI) among laboratory confirmed cases during the first year after the 2009/2010 influenza pandemic in the English- and Dutch-speaking Caribbean.

RESULTS

In total, 774 specimens were tested and 394 (52.7%) cases had positive laboratory confirmation. Respiratory syncytial virus (RSV) (28.4%) and influenza A(H3N2) (23.1%) were most frequently detected. RSV activity peaked in July 2011 while influenza A(H3N2) peaked in October 2010. Influenza was responsible for illness in greater numbers in persons 15-64 years while RSV was seen in primarily in children<5 years and adults>65 years. Other agents confirmed include rhinovirus (12.9%), influenza B (10.9%) and influenza A(H1N1)pdm09 (9.4%).

CONCLUSIONS

RSV and influenza A(H3N2) were the most common viruses identified during the first year after the influenza A(H1N1)pdm09 pandemic. Influenza was detected every month with peak activity corresponding to that typically seen in North America (October to March). In order to determine the seasonality of influenza and RSV, laboratory data from subsequent years and increased specimen submission is needed.

Patterns of spread of influenza A in Canada

Understanding spatial patterns of influenza transmission is important for designing control measures. We investigate spatial patterns of laboratory-confirmed influenza A across Canada from October 1999 to August 2012. A statistical analysis (generalized linear model) of the seasonal epidemics in this time period establishes a clear spatio-temporal pattern, with influenza emerging earlier in western provinces. Early emergence is also correlated with low temperature and low absolute humidity in the autumn. For the richer data from the 2009 pandemic, a mechanistic mathematical analysis, based on a transmission model, shows that both school terms and weather had important effects on pandemic influenza transmission.

Deviations in influenza seasonality: odd coincidence or obscure consequence?

In temperate regions, influenza typically arrives with the onset of colder weather. Seasonal waves travel over large spaces covering many climatic zones in a relatively short period of time. The precise mechanism for this striking seasonal pattern is still not well understood, and the interplay of factors that influence the spread of infection and the emergence of new strains is largely unknown. The study of influenza seasonality has been fraught with problems. One of these is the ever-shifting description of illness resulting from influenza and the use of both the historical definitions and new definitions based on actual isolation of the virus. The compilation of records describing influenza oscillations on a local and global scale is massive, but the value of these data is a function of the definitions used. In this review, we argue that observations of both seasonality and deviation from the expected pattern stem from the nature of this disease. Heterogeneity in seasonal patterns may arise from differences in the behaviour of specific strains, the emergence of a novel strain, or cross-protection from previously observed strains. Most likely, the seasonal patterns emerge from interactions of individual factors behaving as coupled resonators. We emphasize that both seasonality and deviations from it may merely be reflections of our inability to disentangle signal from noise, because of ambiguity in measurement and/or terminology. We conclude the review with suggestions for new promising and realistic directions with tangible consequences for the modelling of complex influenza dynamics in order to effectively control infection.

Latitudinal variations in seasonal activity of influenza and respiratory syncytial virus (RSV): a global comparative review

BACKGROUND

There is limited information on influenza and respiratory syncytial virus (RSV) seasonal patterns in tropical areas, although there is renewed interest in understanding the seasonal drivers of respiratory viruses.

METHODS

We review geographic variations in seasonality of laboratory-confirmed influenza and RSV epidemics in 137 global locations based on literature review and electronic sources. We assessed peak timing and epidemic duration and explored their association with geography and study settings. We fitted time series model to weekly national data available from the WHO influenza surveillance system (FluNet) to further characterize seasonal parameters.

RESULTS

Influenza and RSV activity consistently peaked during winter months in temperate locales, while there was greater diversity in the tropics. Several temperate locations experienced semi-annual influenza activity with peaks occurring in winter and summer. Semi-annual activity was relatively common in tropical areas of Southeast Asia for both viruses. Biennial cycles of RSV activity were identified in Northern Europe. Both viruses exhibited weak latitudinal gradients in the timing of epidemics by hemisphere, with peak timing occurring later in the calendar year with increasing latitude (P<0.03). Time series model applied to influenza data from 85 countries confirmed the presence of latitudinal gradients in timing, duration, seasonal amplitude, and between-year variability of epidemics. Overall, 80% of tropical locations experienced distinct RSV seasons lasting 6 months or less, while the percentage was 50% for influenza.

CONCLUSION

Our review combining literature and electronic data sources suggests that a large fraction of tropical locations experience focused seasons of respiratory virus activity in individual years. Information on seasonal patterns remains limited in large undersampled regions, included Africa and Central America. Future studies should attempt to link the observed latitudinal gradients in seasonality of viral epidemics with climatic and population factors, and explore regional differences in disease transmission dynamics and attack rates.

Sequencing, annotation, and characterization of the influenza ferret infectome

Ferrets have become an indispensable tool in the understanding of influenza virus virulence and pathogenesis. Furthermore, ferrets are the preferred preclinical model for influenza vaccine and therapeutic testing. Here we characterized the influenza infectome during the different stages of the infectious process in ferrets with and without prior specific immunity to influenza. RNA from lung tissue and lymph nodes from infected and naïve animals was subjected to next-generation sequencing, followed by de novo data assembly and annotation of the resulting sequences; this process generated a library comprising 13,202 ferret mRNAs. Gene expression profiles during pandemic H1N1 (pdmH1N1) influenza virus infection were analyzed by digital gene expression and solid support microarrays. As expected during primary infection, innate immune responses were triggered in the lung tissue; meanwhile, in the lymphoid tissue, genes encoding antigen presentation and maturation of effector cells of adaptive immunity increased dramatically. After 5 days postinfection, the innate immune gene expression was replaced by the adaptive immune response, which correlates with viral clearance. Reinfection with homologous pandemic influenza virus resulted in a diminished innate immune response, early adaptive immune gene regulation, and a reduction in clinical severity. The fully annotated ferret infectome will be a critical aid to the understanding of the molecular events that regulate disease severity and host-influenza virus interactions among seasonal, pandemic, and highly pathogenic avian influenzas.

Age as a determinant for dissemination of seasonal and pandemic influenza: an open cohort study of influenza outbreaks in Östergötland County, Sweden

An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427.000) to investigate whether age was a determinant for being diagnosed with influenza 2005-2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10-19 (p<0.001) and 20-29 years old (p<0.01) were found to be larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. An interaction between age and outbreak was observed (p<0.001) indicating a difference in age effects between circulating virus types; this interaction persisted for seasonal outbreaks only (p<0.001). The outbreaks also differed regarding when the age groups received their diagnosis (p<0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10-19 year olds, led outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. The validation analysis showed a strong correlation (r = 0.625;p<0.001) between the recorded numbers of clinically and microbiologically defined influenza cases. Our findings demonstrate the complexity of age effects underlying the emergence of local influenza outbreaks. Disentangling these effects on the causal pathways will require an integrated information infrastructure for data collection and repeated studies of well-defined communities.