Shifting patterns of seasonal influenza epidemics

Global analysis of respiratory viral circulation and timing of epidemics in the pre-COVID-19 and COVID-19 pandemic eras, based on data from the Global Influenza Surveillance and Response System (GISRS)

OBJECTIVES

The COVID-19 pandemic significantly changed respiratory viruses' epidemiology due to non-pharmaceutical interventions and possible viral interactions. This study investigates whether the circulation patterns of respiratory viruses have returned to pre-pandemic norms by comparing their peak timing and duration during the first three SARS-CoV-2 seasons to pre-pandemic times.

METHODS

Global Influenza Surveillance and Response System data from 194 countries (2014-2023) was analyzed for epidemic peak timing and duration, focusing on pre-pandemic and pandemic periods across both hemispheres and the intertropical belt. The analysis was restricted to countries meeting specific data thresholds to ensure robustness.

RESULTS

In 2022/2023, the northern hemisphere experienced earlier influenza and respiratory syncytial virus (RSV) peaks by 1.9 months (P <0.001). The duration of influenza epidemics increased by 2.2 weeks (P <0.001), with RSV showing a similar trend. The southern hemisphere's influenza peak shift was not significant (P = 0.437). Intertropical regions presented no substantial change in peak timing but experienced a significant reduction in the duration for human metapneumovirus and adenovirus (7.2 and 6.5 weeks shorter, respectively, P <0.001).

CONCLUSIONS

The pandemic altered the typical patterns of influenza and RSV, with earlier peaks in 2022 in temperate areas. These findings highlight the importance of robust surveillance data to inform public health strategies on evolving viral dynamics in the years to come.

Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness

In this study, we propose a novel approach that integrates regime-shift detection with a mechanistic model to forecast the peak times of seasonal influenza. The key benefit of this approach is its ability to detect regime shifts from non-epidemic to epidemic states, which is particularly beneficial with the year-round presence of non-zero Influenza-Like Illness (ILI) data. This integration allows for the incorporation of external factors that trigger the onset of the influenza season-factors that mechanistic models alone might not adequately capture. Applied to ILI data collected in Korea from 2005 to 2020, our method demonstrated stable peak time predictions for seasonal influenza outbreaks, particularly in years characterized by unusual onset times or epidemic magnitudes.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System

Based upon 30-years of research by the author, a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 people is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem, a new approach to bed modeling has been developed that plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with a higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence of overutilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, and surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Background

Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity.

Methods

We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space.

Results

We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics.

Conclusions

Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with a high spatiotemporal resolutio and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change.

Funding

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.

Cost-effectiveness of influenza vaccination with a high dose quadrivalent vaccine of the elderly population in Belgium, Finland, and Portugal

BACKGROUND

Seasonal influenza may result in severe outcomes, resulting in a significant increase of hospitalizations during the winter. To improve the protection provided by the standard dose influenza quadrivalent vaccine (SDQIV), a high-dose vaccine (HDQIV) has been developed specifically for adults aged 60 and older who are at higher risk of life-threatening complications.

OBJECTIVES

The aim of this study was to determine the cost-effectiveness of HD QIV vs. SD-QIV in the recommended population of three European countries: Belgium, Finland and Portugal.

METHODS

A cost-utility analysis comparing HDQIV vs. SDQIV was conducted using a decision tree estimating health outcomes conditional on influenza: cases, general practitioner and emergency department visits, hospitalizations and deaths. To account for the full benefit of the vaccine, an additional outcome-hospitalizations attributable to influenza-was also evaluated. Demographic, epidemiological and economic inputs were based on the respective local data. HDQIV relative vaccine efficacy vs. SDQIV was obtained from a phase IV efficacy randomized clinical trial. The incremental cost-effectiveness ratios (ICER) were computed for each country, and a probabilistic sensitivity analysis (1,000 simulations per country) was performed to assess the robustness of the results.

RESULTS

In the base case analysis, HDQIV resulted in improved health outcomes (visits, hospitalizations, and deaths) compared to SDQIV. The ICERs computed were 1,397, 9,581, and 15,267 €/QALY, whereas the PSA yielded 100, 100, and 84% of simulations being cost-effective at their respective willingness-to-pay thresholds, for Belgium, Finland, and Portugal, respectively.

CONCLUSION

In three European countries with different healthcare systems, HD-QIV would contribute to a significant improvement in the prevention of influenza health outcomes while being cost-effective.

Spatial aggregation choice in the era of digital and administrative surveillance data

Traditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.

Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

Epidemiology of acute respiratory viral infections in children in Vientiane, Lao People's Democratic Republic

Respiratory infections are one of the most frequent reasons for medical consultations in children. In low resource settings such as in Lao People's Democratic Republic, knowledge gaps and the dearth of laboratory capacity to support differential diagnosis may contribute to antibiotic overuse. We studied the etiology, temporal trends, and genetic diversity of viral respiratory infections in children to provide evidence for prevention and treatment guidelines. From September 2014 to October 2015, throat swabs and nasopharyngeal aspirates from 445 children under 10 years old with symptoms of acute respiratory infection were collected at the Children Hospital in Vientiane. Rapid antigen tests were performed for influenza A and B and respiratory syncytial virus. Real-time reverse-transcription polymerase chain reactions (RT-PCRs) were performed to detect 16 viruses. Influenza infections were detected with a higher sensitivity using PCR than with the rapid antigen test. By RT-PCR screening, at least one pathogen could be identified for 71.7% of cases. Human rhinoviruses were most frequently detected (29.9%), followed by influenza A and B viruses combined (15.9%). We identify and discuss the seasonality of some of the infections. Altogether these data provide a detailed characterization of respiratory pathogens in Lao children and we provide recommendations for vaccination and further studies.

Differences in epidemic spread patterns of norovirus and influenza seasons of Germany: an application of optical flow analysis in epidemiology

This analysis presents data from a new perspective offering key insights into the spread patterns of norovirus and influenza epidemic events. We utilize optic flow analysis to gain an informed overview of a wealth of statistical epidemiological data and identify trends in movement of influenza waves throughout Germany on the NUTS 3 level (413 locations) which maps municipalities on European level. We show that Influenza and norovirus seasonal outbreak events have a highly distinct pattern. We investigate the quantitative statistical properties of the epidemic patterns and find a shifted distribution in the time between influenza and norovirus seasonal peaks of reported infections over one decade. These findings align with key biological features of both pathogens as shown in the course of this analysis.

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

OBJECTIVES

To investigate epidemiological features and trends of influenza incidence with 1,173,640 cases in mainland China from 2005 to 2015.

METHODS

Incidence and mortality data for influenza from 2005 to 2015 were provided by the data-center of China public health science and covered a population of about 1.3 billion people from 31 provinces and regions in mainland China. Joinpoint regression and exploratory spatial data analyses were used to examine the incidence trends from 2005 to 2015.

RESULTS

The first upsurge in influenza cases occurred in 2009, and the highest incidence of influenza occurred in 2014 (15.9045 cases/100,000 people). The average incidence per year from 2009 to 2015 was threefold higher than that from 2005 to 2008 (10.5308 vs 3.4589 cases/100,000 people; incidence rate ratio=3.0446). The joinpoint regression results showed that there was an increasing influenza incidence trend from 2005 to 2015 (annual change in percentage=13.6%, 95%CI 2.2-26.3, p=0.0236). The seasonal pattern analysis showed that influenza typically occurred in winter and spring during each monitoring year, peaking from November to March the next year.

CONCLUSIONS

This study will help governments to make valuable decisions in allocating scarce resources and providing strategies to limit the spread of influenza.

On the predictability of infectious disease outbreaks

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.