Global influenza seasonality: reconciling patterns across temperate and tropical regions

Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain

Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016-2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016-2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.

Influenza A, influenza B, and SARS-COV-2 circulation patterns in midwest Brazil during the 2022-2023 period

Until 2022, the COVID-19 pandemic caused by the SARS-CoV-2, had profoundly impacted the world. Consequently, Brazil, including the state of Goiás, was also significantly affected. Furthermore, in the second half of 2022, the state of Goiás experienced an unusual rise in influenza cases, despite it being an off-season period for influenza viruses in this region. As SARS-CoV-2 and Influenza infection have similar clinical manifestations, surveillance strategies are crucial for public health. Understanding how SARS-CoV-2 and Influenza viruses co-circulate is important for surveillance and monitoring of these patterns of respiratory infections. In this context, this investigation monitored Influenza A and B cases from symptomatic individuals diagnosed as negative for COVID-19. Between September 2022 and May 2023, among the 779 samples tested, 126 (16.2%) were positive for Influenza A, whereas 93 samples (11.9%) were positive for Influenza B. In this period, the peak Influenza infection cases did not coincide with the peak of SARS-CoV-2 infections, suggesting a seasonal shift in viral circulation patterns.

Ozone as an environmental driver of influenza

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Effects of meteorological factors on influenza transmissibility by virus type/subtype

BACKGROUND

Quantitative evidence on the impact of meteorological factors on influenza transmissibility across different virus types/subtypes is scarce, and no previous studies have reported the effect of hourly temperature variability (HTV) on influenza transmissibility. Herein, we explored the associations between meteorological factors and influenza transmissibility according to the influenza type and subtype in Guangzhou, a subtropical city in China.

METHODS

We collected influenza surveillance and meteorological data of Guangzhou between October 2010 and December 2019. Influenza transmissibility was measured using the instantaneous effective reproductive number (Rt). A gamma regression with a log link combined with a distributed lag non-linear model was used to assess the associations of daily meteorological factors with Rt by influenza types/subtypes.

RESULTS

The exposure-response relationship between ambient temperature and Rt was non-linear, with elevated transmissibility at low and high temperatures. Influenza transmissibility increased as HTV increased when HTV < around 4.5 °C. A non-linear association was observed between absolute humidity and Rt, with increased transmissibility at low absolute humidity and at around 19 g/m3. Relative humidity had a U-shaped association with influenza transmissibility. The associations between meteorological factors and influenza transmissibility varied according to the influenza type and subtype: elevated transmissibility was observed at high ambient temperatures for influenza A(H3N2), but not for influenza A(H1N1)pdm09; transmissibility of influenza A(H1N1)pdm09 increased as HTV increased when HTV < around 4.5 °C, but the transmissibility decreased with HTV when HTV < 2.5 °C and 3.0 °C for influenza A(H3N2) and B, respectively; positive association of Rt with absolute humidity was witnessed for influenza A(H3N2) even when absolute humidity was larger than 19 g/m3, which was different from that for influenza A(H1N1)pdm09 and influenza B.

CONCLUSIONS

Temperature variability has an impact on influenza transmissibility. Ambient temperature, temperature variability, and humidity influence the transmissibility of different influenza types/subtypes discrepantly. Our findings have important implications for improving preparedness for influenza epidemics, especially under climate change conditions.

Coordinating virus research: The Virus Infectious Disease Ontology

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

Vaccines in cardiology, an underutilized strategy to reduce the residual cardiovascular risk

Cardiovascular diseases stand as the leading cause of mortality among adults globally. For decades, comprehensive evidence has underscored the correlation between infections, particularly those involving the respiratory system, and an elevated risk of cardiovascular and cerebrovascular events, as well as all-cause mortality. The mechanisms through which infections heighten cardiovascular events are intricate, encompassing immune system activation, systemic inflammation, hypercoagulable states, sympathetic system activation, and increased myocardial oxygen demand. Respiratory infections further contribute hypoxemia to this complex interplay. These mechanisms intertwine, giving rise to endothelial dysfunction, plaque ruptures, myocardial depression, and heart failure. They can either instigate de novo cardiovascular events or exacerbate pre-existing conditions. Compelling evidence supports the safety of influenza, pneumococcal, herpes zoster, COVID-19 and respiratory syncytial virus vaccines in individuals with cardiovascular risk factors or established cardiovascular disease. Notably, the influenza vaccine has demonstrated safety even when administered during the acute phase of a myocardial infarction in individuals undergoing angioplasty. Beyond safety, these vaccinations significantly reduce the incidence of cardiovascular events in individuals with an augmented cardiovascular risk. Nevertheless, vaccination rates remain markedly suboptimal. This manuscript delves into the intricate relationship between infections and cardiovascular events. Additionally, we highlight the role of vaccinations as a tool to mitigate these occurrences and reduce residual cardiovascular risk. Finally, we emphasize the imperative need to optimize vaccination rates among individuals with heart diseases.

Air Pollution and Influenza: A Systematic Review and Meta-Analysis

Background

Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases.

Methods

We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1).

Results

A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively.

Conclusion

Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.

Avian influenza outbreaks in domestic cats: another reason to consider slaughter-free cell-cultured poultry?

Avian influenza causes substantial economic loss in the poultry industry and potentially threatens human health. Over recent years, the highly pathogenic avian influenza A/H5N1 virus has led to devastating losses in poultry flocks and wild birds. At the same time, the number of mammalian species identified to be infected with A/H5N1 is increasing, with recent outbreaks in domestic cats, including household individuals, evidenced in July 2023 in Poland, ultimately creating opportunities for the virus to adapt better to mammalian hosts, including humans. Overall, between 2003 and 2023, over 10 outbreaks in felids have been documented globally, and in six of them, feed based on raw chicken was suspected as a potential source of A/H5N1, fuelling a debate on threats posed by A/H5N1 and methods to decrease the associated risks. This article debates that technology allowing the production of slaughter-free meat, including poultry, from cell and tissue cultures could be considered as a part of a mitigation strategy to decrease the overall burden and threat of adaptation of avian influenza viruses to human hosts. By shifting poultry production to the cultured meat industry, the frequency of A/H5N1 outbreaks in farmed birds may be decreased, leading to a reduced risk of virus acquisition by wild and domesticated mammals that have direct contact with birds or eat raw poultry and have close contact with human (including domestic cats), ultimately minimizing the potential of A/H5N1 to adapt better to mammalian host, including humans. This adds to the list of other benefits of cultured meat that are also reviewed in this paper, including decreased antibiotic use, risk of microbial contamination and parasite transmission, and environmental and ethical advantages over conventional slaughtered meat. In conclusion, further development and implementation of this technology, also in the context of poultry production, is strongly advocated. Although cultured poultry is unlikely to replace the conventional process in the near future due to challenges with scaling up the production and meeting the continuously increased demand for poultry meat, it may still decrease the pressures and threats related to the transmission of highly pathogenic avian influenza in selected world regions.

Some developments on seasonal INAR processes with application to influenza data

Influenza epidemic data are seasonal in nature. Zero-inflation, zero-deflation, overdispersion, and underdispersion are frequently seen in such number of cases of disease (count) data. To explain these counts' features, this paper introduces a flexible model for nonnegative integer-valued time series with a seasonal autoregressive structure. Some probabilistic properties of the model are discussed for general seasonal INAR(p) model and three estimation methods are used to estimate the model parameters for its special case seasonal INAR(1) model. The performance of the estimation procedures has been studied using simulation. The proposed model is applied to analyze weekly influenza data from the Breisgau- Hochschwarzwald county of Baden-Württemberg state, Germany. The empirical findings show that the suggested model performs better than existing models.

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

Background

Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023.

Methods

Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023.

Results

We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China.

Conclusions

The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

Seasonality of COVID-19 incidence in the United States

Background

The surges of Coronavirus Disease 2019 (COVID-19) appeared to follow a repeating pattern of COVID-19 outbreaks regardless of social distancing, mask mandates, and vaccination campaigns.

Objectives

This study aimed to investigate the seasonality of COVID-19 incidence in the United States of America (USA), and to delineate the dominant frequencies of the periodic patterns of the disease.

Methods

We characterized periodicity in COVID-19 incidences over the first three full seasonal years (March 2020 to March 2023) of the COVID-19 pandemic in the USA. We utilized a spectral analysis approach to find the naturally occurring dominant frequencies of oscillation in the incidence data using a Fast Fourier Transform (FFT) algorithm.

Results

Our study revealed four dominant peaks in the periodogram: the two most dominant peaks show a period of oscillation of 366 days and 146.4 days, while two smaller peaks indicate periods of 183 days and 122 days. The period of 366 days indicates that there is a single COVID-19 outbreak that occurs approximately once every year, which correlates with the dominant outbreak in the early/mid-winter months. The period of 146.4 days indicates approximately 3 peaks per year and matches well with each of the 3 annual outbreaks per year.

Conclusion

Our study revealed the predictable seasonality of COVID-19 outbreaks, which will guide public health preventative efforts to control future outbreaks. However, the methods used in this study cannot predict the amplitudes of the incidences in each outbreak: a multifactorial problem that involves complex environmental, social, and viral strain variables.

Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China

BACKGROUND

Influenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level.

OBJECTIVE

We examined individual-level associations between six criteria air pollutants and influenza using case-crossover design.

METHODS

In this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM 2.5 and PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1-7 d before the onset (lags 1-7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants' home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility.

RESULTS

N = 108,479 eligible cases were identified in our study. Every 10 - μ g / m 3 increase in exposure to PM 2.5 , PM 10 , NO 2 , and CO and every 5 - μ g / m 3 increase in SO 2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O 3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890).

CONCLUSIONS

Our findings suggest that short-term exposure to air pollution, except for O 3 , is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Climatic variations and pollution on benign paroxysmal positional vertigo in Kars, Türkiye

Benign paroxysmal positional vertigo (BPPV) is the most common diagnosis for peripheral vertigo. Although pathophysiological mechanisms remain unclear, BPPV is mostly idiopathic and factors related to BPPV are still being investigated. Knowing these factors can contribute to the prevention and management of BPPV. In this study, we investigated the correlations between climatic variations, pollution, and BPPV retrospectively. 262 patients diagnosed with BPPV between 2019 and 2021 in Kars, Türkiye, were included in our study. Meteorological parameters were obtained from Turkish State Meteorological Service. Horizontal BPPV increased significantly with the humidity (p < 0.05). In addition, carbon monoxide levels significantly increased the potantial of BPPV (p < 0.05). Surprisingly, BPPV increased in the summertime and showed a significant relationship with humidity. We believe this change is related with the city-specific features as it is the coldest place in the country, emigrant province and crowded in the summer times.

Seasonality of endemic COVID-19

Successive waves of infection by SARS-CoV-2 have left little doubt that this virus will transition to an endemic disease. Foreknowledge of when to expect seasonal surges is crucial for healthcare and public health decision-making. However, the future seasonality of COVID-19 remains uncertain. Evaluating its seasonality is complicated due to the limited years of SARS-CoV-2 circulation, pandemic dynamics, and varied interventions. In this study, we project the expected endemic seasonality by employing a phylogenetic ancestral and descendant state approach that leverages long-term data on the incidence of circulating HCoV coronaviruses. Our projections indicate asynchronous surges of SARS-CoV-2 across different locations in the northern hemisphere, occurring between October and January in New York and between January and March in Yamagata, Japan. This knowledge of spatiotemporal surges leads to medical preparedness and enables the implementation of targeted public health interventions to mitigate COVID-19 transmission.IMPORTANCEThe seasonality of COVID-19 is important for effective healthcare and public health decision-making. Previous waves of SARS-CoV-2 infections have indicated that the virus will likely persist as an endemic pathogen with distinct surges. However, the timing and patterns of potentially seasonal surges remain uncertain, rendering effective public health policies uninformed and in danger of poorly anticipating opportunities for intervention, such as well-timed booster vaccination drives. Applying an evolutionary approach to long-term data on closely related circulating coronaviruses, our research provides projections of seasonal surges that should be expected at major temperate population centers. These projections enable local public health efforts that are tailored to expected surges at specific locales or regions. This knowledge is crucial for enhancing medical preparedness and facilitating the implementation of targeted public health interventions.

Association between ozone and influenza transmissibility in China

BACKGROUND

Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown.

METHODS

We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility.

RESULTS

Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text].

CONCLUSIONS

O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

A combination of annual and nonannual forces drive respiratory disease in the tropics

INTRODUCTION

It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam.

METHODS

We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models.

RESULTS

During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak.

CONCLUSION

Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Old and new aspects of influenza

Influenza is a classic infectious disease that, through the continuous variation of the viruses that produce it, imposes new challenges that we must solve as quickly as possible. The COVID-19 pandemic has substantially modified the behavior of influenza and other respiratory viruses, and in the coming years we will have to coexist with a new pathogen that will probably interact with existing pathogens in a way that we cannot yet glimpse. However, knowledge prior to the pandemic allows us to focus on the aspects that must be modified to make influenza an acceptable challenge for the future. In this review, emphasis is placed on the most relevant aspects of epidemiology, disease burden, diagnosis, and vaccine prevention, and how scientific and clinical trends in these aspects flow from the previously known to future challenges.

Impact of non-pharmaceutical interventions during COVID-19 on future influenza trends in Mainland China

BACKGROUND

Influenza is a common illness for its high rates of morbidity and transmission. The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to manage its dissemination could affect the transmission of influenza.

METHODS

A retrospective analysis, between 2018 and 2023, was conducted to examine the incidence of influenza virus types A and B among patients in sentinel cities located in North or South China as well as in Wuhan City. For validations, data on the total count of influenza patients from 2018 to 2023 were collected at the Central Hospital of Wuhan, which is not included in the sentinel hospital network. Time series methods were utilized to examine seasonal patterns and to forecast future influenza trends.

RESULTS

Northern and southern cities in China had earlier outbreaks during the NPIs period by about 8 weeks compared to the 2018-2019. The implementation of NPIs significantly reduced the influenza-like illness (ILI) rate and infection durations. Influenza B Victoria and H3N2 were the first circulating strains detected after the relaxation of NPIs, followed by H1N1 across mainland China. The SARIMA model predicted synchronized H1N1 outbreak cycles in North and South China, with H3N2 expected to occur in the summer in southern cities and in the winter in northern cities over the next 3 years. The ILI burden is expected to rise in both North and South China over the next 3 years, with higher ILI% levels in southern cities throughout the year, especially in winter, and in northern cities mainly during winter. In Wuhan City and the Central Hospital of Wuhan, influenza levels are projected to peak in the winter of 2024, with 2 smaller peaks expected during the summer of 2023.

CONCLUSIONS

In this study, we report the impact of NPIs on future influenza trends in mainland China. We recommend that local governments encourage vaccination during the transition period between summer and winter to mitigate economic losses and mortality associated with influenza.

LSTM-based recurrent neural network provides effective short term flu forecasting

BACKGROUND

Influenza virus is responsible for a yearly epidemic in much of the world. To better predict short-term, seasonal variations in flu infection rates and possible mechanisms of yearly infection variation, we trained a Long Short-Term Memory (LSTM)-based deep neural network on historical Influenza-Like-Illness (ILI), climate, and population data.

METHODS

Data were collected from the Centers for Disease Control and Prevention (CDC), the National Center for Environmental Information (NCEI), and the United States Census Bureau. The model was initially built in Python using the Keras API and tuned manually. We explored the roles of temperature, precipitation, local wind speed, population size, vaccination rate, and vaccination efficacy. The model was validated using K-fold cross validation as well as forward chaining cross validation and compared to several standard algorithms. Finally, simulation data was generated in R and used for further exploration of the model.

RESULTS

We found that temperature is the strongest predictor of ILI rates, but also found that precipitation increased the predictive power of the network. Additionally, the proposed model achieved a +1 week prediction mean absolute error (MAE) of 0.1973. This is less than half of the MAE achieved by the next best performing algorithm. Additionally, the model accurately predicted simulation data. To test the role of temperature in the network, we phase-shifted temperature in time and found a predictable reduction in prediction accuracy.

CONCLUSIONS

The results of this study suggest that short term flu forecasting may be effectively accomplished using architectures traditionally reserved for time series analysis. The proposed LSTM-based model was able to outperform comparison models at the +1 week time point. Additionally, this model provided insight into the week-to-week effects of climatic and biotic factors and revealed potential patterns in data series. Specifically, we found that temperature is the strongest predictor of seasonal flu infection rates. This information may prove to be especially important for flu forecasting given the uncertain long-term impact of the SARS-CoV-2 pandemic on seasonal influenza.

Influenza: seasonality and travel-related considerations

RATIONALE FOR REVIEW

This review aims to summarize the transmission patterns of influenza, its seasonality in different parts of the globe, air travel- and cruise ship-related influenza infections and interventions to reduce transmission.

KEY FINDINGS

The seasonality of influenza varies globally, with peak periods occurring mainly between October and April in the northern hemisphere (NH) and between April and October in the southern hemisphere (SH) in temperate climate zones. However, influenza seasonality is significantly more variable in the tropics. Influenza is one of the most common travel-related, vaccine-preventable diseases and can be contracted during travel, such as during a cruise or through air travel. Additionally, travellers can come into contact with people from regions with ongoing influenza transmission. Current influenza immunization schedules in the NH and SH leave individuals susceptible during their respective spring and summer months if they travel to the other hemisphere during that time.

CONCLUSIONS/RECOMMENDATIONS

The differences in influenza seasonality between hemispheres have substantial implications for the effectiveness of influenza vaccination of travellers. Health care providers should be aware of influenza activity when patients report travel plans, and they should provide alerts and advise on prevention, diagnostic and treatment options. To mitigate the risk of travel-related influenza, interventions include antivirals for self-treatment (in combination with the use of rapid self-tests), extending the shelf life of influenza vaccines to enable immunization during the summer months for international travellers and allowing access to the influenza vaccine used in the opposite hemisphere as a travel-related vaccine. With the currently available vaccines, the most important preventive measure involves optimizing the seasonal influenza vaccination. It is also imperative that influenza is recognized as a travel-related illness among both travellers and health care professionals.

Effects and interaction of temperature and relative humidity on the trend of influenza prevalence: A multi-central study based on 30 provinces in mainland China from 2013 to 2018

Background

Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions of China.

Methods

We estimated the time-varying reproduction number (R_t) of influenza and explored the impact of temperature and relative humidity on R_t using generalized additive quasi-Poisson regression models combined with the distribution lag non-linear model (DLNM). The effect of temperature and humidity interaction on R_t of influenza was explored. The multiple random-meta analysis was used to evaluate region-specific association. The excess risk (ER) index was defined to investigate the correlation between R_t and each meteorological factor with the modification of seasonal and regional characteristics.

Results

Low temperature and low relative humidity contributed to influenza epidemics on the national level, while shapes of merged cumulative effect plots were different across regions. Compared to that of median temperature, the merged RR (95%CI) of low temperature in northern and southern regions were 1.40(1.24,1.45) and 1.20 (1.14,1.27), respectively, while those of high temperature were 1.10(1.03,1.17) and 1.00 (0.95,1.04), respectively. There were negative interactions between temperature and relative humidity on national (SI = 0.59, 95%CI: 0.57-0.61), southern (SI = 0.49, 95%CI: 0.17-0.80), and northern regions (SI = 0.59, 95%CI: 0.56,0.62). In general, with the increase of the change of the two meteorological factors, the ER of R_t also gradually increased.

Conclusions

Temperature and relative humidity have an effect on the influenza epidemics in China, and there is an interaction between the two meteorological factors, but the effect of each factor is heterogeneous among regions. Meteorological factors may be considered to predict the trend of influenza epidemic.

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

Although the drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and subtropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a nonlinear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.

Shifting Patterns of Influenza Circulation during the COVID-19 Pandemic, Senegal

Historically low levels of seasonal influenza circulation were reported during the first years of the COVID-19 pandemic and were mainly attributed to implementation of nonpharmaceutical interventions. In tropical regions, influenza's seasonality differs largely, and data on this topic are scarce. We analyzed data from Senegal's sentinel syndromic surveillance network before and after the start of the COVID-19 pandemic to assess changes in influenza circulation. We found that influenza shows year-round circulation in Senegal and has 2 distinct epidemic peaks: during January-March and during the rainy season in August-October. During 2021-2022, the expected January-March influenza peak completely disappeared, corresponding to periods of active SARS-CoV-2 circulation. We noted an unexpected influenza epidemic peak during May-July 2022. The observed reciprocal circulation of SARS-CoV-2 and influenza suggests that factors such as viral interference might be at play and should be further investigated in tropical settings.

Immune response in influenza virus infection and modulation of immune injury by viral neuraminidase

Influenza A viruses cause severe respiratory illnesses in humans and animals. Overreaction of the innate immune response to influenza virus infection results in hypercytokinemia, which is responsible for mortality and morbidity. The influenza A virus surface glycoprotein neuraminidase (NA) plays a vital role in viral attachment, entry, and virion release from infected cells. NA acts as a sialidase, which cleaves sialic acids from cell surface proteins and carbohydrate side chains on nascent virions. Here, we review progress in understanding the role of NA in modulating host immune response to influenza virus infection. We also discuss recent exciting findings targeting NA protein to interrupt influenza-induced immune injury.

Spatiotemporal effects of meteorological conditions on global influenza peaks

BACKGROUND

Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes.

OBJECTIVES

We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries.

METHODS

Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons.

RESULTS

Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus.

CONCLUSION

Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Repeatability and timing of tropical influenza epidemics

Much of the world experiences influenza in yearly recurring seasons, particularly in temperate areas. These patterns can be considered repeatable if they occur predictably and consistently at the same time of year. In tropical areas, including southeast Asia, timing of influenza epidemics is less consistent, leading to a lack of consensus regarding whether influenza is repeatable. This study aimed to assess repeatability of influenza in Vietnam, with repeatability defined as seasonality that occurs at a consistent time of year with low variation. We developed a mathematical model incorporating parameters to represent periods of increased transmission and then fitted the model to data collected from sentinel hospitals throughout Vietnam as well as four temperate locations. We fitted the model for individual (sub)types of influenza as well as all combined influenza throughout northern, central, and southern Vietnam. Repeatability was evaluated through the variance of the timings of peak transmission. Model fits from Vietnam show high variance (sd = 64-179 days) in peak transmission timing, with peaks occurring at irregular intervals and throughout different times of year. Fits from temperate locations showed regular, annual epidemics in winter months, with low variance in peak timings (sd = 32-57 days). This suggests that influenza patterns are not repeatable or seasonal in Vietnam. Influenza prevention in Vietnam therefore cannot rely on anticipation of regularly occurring outbreaks.

Association of climatic variables with risk of transmission of influenza in Guangzhou, China, 2005-2021

BACKGROUND

Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission.

OBJECTIVE

This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou.

METHODS

Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (R_t) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined.

RESULTS

Over the study period (2005-21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower R_t, while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall.

CONCLUSION

Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities.

Contribution of the FilmArray BioFire® Technology in the Diagnosis of Viral Respiratory Infections during the COVID-19 Pandemic at Ibn Sina University Hospital Center in Rabat: Epidemiological Study about 503 Cases

Respiratory viruses are the most involved pathogens in acute respiratory infections. During the COVID-19 pandemic, new elements have been brought to this topic, especially at the diagnostic and therapeutic level. The objective of this work is to describe the epidemiology of respiratory viruses in patients admitted to the Ibn Sina University Hospital of Rabat during a period characterized by the emergence and spread of SARS-CoV-2. We conducted a retrospective study from January 1 to December 31. We included all patients treated for acute respiratory infection and for whom a multiplex respiratory panel PCR was requested. Virus detection was performed using a FilmArray RP 2.1 plus BioFire multiplex respiratory panel. The study population was relatively adults with a mean age of 39 years. The sex ratio M/F was 1.20. The survey revealed a high prevalence of 42.3% of patients hospitalized in the adult intensive care unit whose respiratory distress was the most common reason for hospitalization (58%). The positivity rate was 48.1%. This rate was higher in the pediatric population 83.13% compared to adults 29.7%. Monoinfection was found in 36.4% of cases, and codetection in 11.7% of cases. This survey revealed that a total of 322 viruses were detected, HRV being the most incriminated virus (48.7%), followed by RSV in 13.8% of patients. Considering the five most detected viruses in our study (HRV, RSV, PIV3, ADV, and hMPV), we found that the incidence was significantly higher in the pediatric population. SARS-CoV-2 was detected only in adult's population. In our study, we found that influenza A and B viruses, PIV2, MERS, and all bacteria were not detected by this kit during the study period. Regarding the seasonal distribution, RSV and hMPV showed a significantly high incidence during autumn and summer and SARS-CoV-2 and CoV OC43 showed a high peak during winter. In this study, we found a lack of detection of influenza virus and a shift in the usual winter peak of RSV to the summer, while the detection of ADV and HRV was less affected. This difference in detection could be due on the one hand to the difference in stability between enveloped and nonenveloped viruses and on the other hand to the escape of certain viruses to the different sanitary measures introduced after the declaration of the COVID-19 pandemic. These same measures were effective against enveloped viruses such as RSV and influenza viruses. The emergence of SARS-CoV-2 has modified the epidemiology of other respiratory viruses, either directly by viral interference or indirectly by the preventive measures taken.

Performance of discrete wavelet transform-based method in the detection of influenza outbreaks in Iran: An ecological study

Background and Aim

Timely detection of outbreaks is one of the main purposes of the health surveillance system. The presence of appropriate methods in the detection of outbreaks can have an important role in the timely detection of outbreaks. Because of the importance of this issue, this study aimed to assess the performance of discrete wavelet transform (DWT) based methods in detecting influenza outbreaks in Iran from January 2010 to January 2020.

Methods

All registered influenza-positive virus cases in Iran from January 2010 to January 2010 were obtained from the FluNet web base tool, the World Health Organization website. The combination method that includes DWT and Shewhart control chart was used in this study. All analyses were performed using MATLAB software version 2018a Stata software version 15.

Results

The Mean ± SD and median of reported influenza cases from January 2010 to January 2020 was 36 ± 108 and four cases per week. The combination of the DWT and Shewhart control chart with K = 0.25 had the most sensitivity. The most specificity in the detection of nonoutbreak days was seen in the combination of DWT and Shewhart control chart with K = 1.5, K = 1.75, and K = 2, respectively. The combination of DWT and Shewhart control chart with K = 0.5 had the best performance in the detection of outbreaks (sensitivity = 0.64, specificity: 0.90, Youden index: 0.54, and area under the curve [AUC]: 0.77).

Conclusion

The DWT-based method in detecting influenza outbreaks has acceptable performance, but it is recommended that this method's performance be assessed in detecting outbreaks of other infectious diseases.

Molecular Characterization of Influenza A/H3N2 Virus Isolated from Indonesian Hajj and Umrah Pilgrims 2013 to 2014

The Hajj and Umrah are the annual mass gatherings of Muslims in Saudi Arabia and increase the transmission risk of acute respiratory infection. This study describes influenza infection among pilgrims upon arrival in Indonesia and the genetic characterization of imported influenza A/H3N2 virus. In total, 251 swab samples with influenza-like illness were tested using real-time RT-PCR for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and influenza viruses. Complete sequences of influenza A/H3N2 HA and NA genes were obtained using DNA sequencing and plotted to amino acid and antigenicity changes. Phylogenetic analysis was performed using a neighbour-joining method including the WHO vaccine strains and influenza A/H3N2 as references. The real-time RT-PCR test detected 100 (39.5%) samples positive with influenza with no positivity of MERS-CoV. Mutations in the HA gene were mainly located within the antigenic sites A, B, and D, while for the NA gene, no mutations related to oseltamivir resistance were observed. Phylogenetic analysis revealed that these viruses grouped together with clades 3C.2 and 3C.3; however, they were not closely grouped with the WHO-recommended vaccine (clades 3C.1). Sequences obtained from Hajj and Umrah pilgrims were also not grouped together with viruses from Middle East countries but clustered according to years of collection. This implies that the influenza A/H3N2 virus mutates continually across time.

Determining meteorologically-favorable zones for seasonal influenza activity in Hong Kong

Investigations of simple and accurate meteorology classification systems for influenza epidemics, particularly in subtropical regions, are limited. To assist in preparing for potential upsurges in the demand on healthcare facilities during influenza seasons, our study aims to develop a set of meteorologically-favorable zones for epidemics of influenza A and B, defined as the intervals of meteorological variables with prediction performance optimized. We collected weekly detection rates of laboratory-confirmed influenza cases from four local major hospitals in Hong Kong between 2004 and 2019. Meteorological and air quality records for hospitals were collected from their closest monitoring stations. We employed classification and regression trees to identify zones that optimize the prediction performance of meteorological data in influenza epidemics, defined as a weekly rate > 50^th percentile over a year. According to the results, a combination of temperature > 25.1℃ and relative humidity > 79% was favorable to epidemics in hot seasons, whereas either temperature < 16.4℃ or a combination of < 20.4℃ and relative humidity > 76% was favorable to epidemics in cold seasons. The area under the receiver operating characteristic curve (AUC) in model training achieved 0.80 (95% confidence interval [CI], 0.76-0.83) and was kept at 0.71 (95%CI, 0.65-0.77) in validation. The meteorologically-favorable zones for predicting influenza A or A and B epidemics together were similar, but the AUC for predicting influenza B epidemics was comparatively lower. In conclusion, we established meteorologically-favorable zones for influenza A and B epidemics with a satisfactory prediction performance, even though the influenza seasonality in this subtropical setting was weak and type-specific.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France

BACKGROUND

Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.

METHODS

We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.

FINDINGS

Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.

INTERPRETATION

Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Global, regional and national estimates of influenza-attributable ischemic heart disease mortality

BACKGROUND

Influenza virus infection is associated with incident ischemic heart disease (IHD) events. Here, we estimate the global, regional, and national IHD mortality burden attributable to influenza.

METHODS

We used vital registration data from deaths in adults ≥50 years (13.2 million IHD deaths as underlying cause) to assess the relationship between influenza activity and IHD mortality in a non-linear meta-regression framework from 2010 to 2019. This derived relationship was then used to estimate the global influenza attributable IHD mortality. We estimated the population attributable fraction (PAF) of influenza for IHD deaths based on the relative risk associated with a given level of weekly influenza test positivity rate and multiplied PAFs by IHD mortality from the Global Burden of Disease study.

FINDINGS

Influenza activity was associated with increased risk of IHD mortality across all countries analyzed. The mean PAF of influenza for IHD mortality was 3.9% (95% uncertainty interval [UI] 2.5-5.3%), ranging from <1% to 10%, depending on country and year. Globally, 299,858 IHD deaths (95% UI 191,216-406,809) in adults ≥50 years could be attributed to influenza, with the highest rates per 100,000 population in the Central Europe, Eastern Europe and Central Asia Region (32.3; 95% UI 20.6-43.8), and in the North Africa and Middle East Region (26.7; 95% UI 17-36.2).

INTERPRETATION

Influenza may contribute substantially to the burden of IHD. Our results suggest that if there were no influenza, an average of 4% of IHD deaths globally would not occur.

FUNDING

Collaborative study funded by Sanofi Vaccines.

The influence of air quality and meteorological variations on influenza A and B virus infections in a paediatric population in Singapore

INTRODUCTION

Influenza is an important cause of paediatric illness across the globe. However, information about the relationships between air pollution, meteorological variability and paediatric influenza A and B infections in tropical settings is limited.

METHODS

We analysed all daily reports of influenza A and B infections in children <5 years old obtained from the largest specialist women and children's hospital in Singapore. In separate negative binomial regression models, we assessed the dependence of paediatric influenza A and B infections on air quality and meteorological variability, using multivariable fractional polynomial modelling and adjusting for time-varying confounders.

RESULTS

Approximately 80% of 7329 laboratory-confirmed reports were caused by influenza A. We observed positive associations between sulphur dioxide (SO₂) exposure and the subsequent risk of infection with both influenza types. We observed evidence of a harvesting effect of SO₂ on Influenza A but not Influenza B. Ambient temperature was associated with a decline in influenza A reports (Relative Risk at lag 5 [RR_lag5]: 0.949, 95% CI: 0.916-0.983). Rainfall was positively associated with a subsequent increase in influenza A reports (RR_lag3: 1.044, 95% CI: 1.017-1.071). Nitrogen dioxide (NO₂) concentration was positively associated with influenza B reports (RR_lag5: 1.015, 95% CI: 1.005-1.025). There was a non-linear association between CO and influenza B reports. Absolute humidity increased the ensuing risk of influenza B (RR_lag5: 4.799, 95% CI: 2.277-10.118). Influenza A and B infections displayed dissimilar but predictable within-year seasonal patterns.

CONCLUSIONS

We observed different independent associations between air quality and meteorological variability with paediatric influenza A and B infections. Anticipated seasonal infection peaks and variations in air quality and meteorological parameters can inform the timing of community measures aimed at reducing influenza infection risk.

Indoor relative humidity shapes influenza seasonality in temperate and subtropical climates in China

OBJECTIVES

The aim of this study was to explore whether indoor or outdoor relative humidity (RH) modulates the influenza epidemic transmission in temperate and subtropical climates.

METHODS

In this study, the daily temperature and RH in 1558 households from March 2017 to January 2019 in five cities across both temperate and subtropical regions in China were collected. City-level outdoor temperature and RH from 2013 to 2019 were collected from the weather stations. We first estimated the effective reproduction number (R_t) of influenza and then used time-series analyses to explore the relationship between indoor/outdoor RH/absolute humidity and the R_t of influenza. Furthermore, we expanded the measured 1-year indoor temperature and the RH data into 5 years and used the same method to examine the relationship between indoor/outdoor RH and the R_t of influenza.

RESULTS

Indoor RH displayed a seasonal pattern, with highs during the summer months and lows during the winter months, whereas outdoor RH fluctuated with no consistent pattern in subtropical regions. The R_t of influenza followed a U-shaped relationship with indoor RH in both temperate and subtropical regions, whereas a U-shaped relationship was not observed between outdoor RH and R_t. In addition, indoor RH may be a better indicator for R_t of influenza than indoor absolute humidity.

CONCLUSION

The findings indicated that indoor RH may be the driver of influenza seasonality in both temperate and subtropical locations in China.

Temperature and particulate matter as environmental factors associated with seasonality of influenza incidence - an approach using Earth observation-based modeling in a health insurance cohort study from Baden-Württemberg (Germany)

BACKGROUND

Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones.

OBJECTIVES

We examined influences of medium-term residential exposure to fine particulate matter (PM_2.5), NO₂, SO₂, air temperature and precipitation on influenza incidence.

METHODS

To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input.

RESULTS

In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM_2.5 and NO₂. Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m³ to 15.98 μg/m³.

CONCLUSIONS

Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.

Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study

Circulation of seasonal influenza is the product of complex interplay among multiple drivers, yet characterizing the underlying mechanism remains challenging. Leveraging the diverse seasonality of A(H3N2) virus and abundant climatic space across regions in China, we quantitatively investigated the relative importance of population susceptibility, climatic factors, and antigenic change on the dynamics of influenza A(H3N2) through an integrative modelling framework. Specifically, an absolute humidity driven multiscale transmission model was constructed for the 2013/2014, 2014/2015 and 2016/2017 influenza seasons that were dominated by influenza A(H3N2). We revealed the variable impact of absolute humidity on influenza transmission and differences in the occurring timing and magnitude of antigenic change for those three seasons. Overall, the initial population susceptibility, climatic factors, and antigenic change explained nearly 55% of variations in the dynamics of influenza A(H3N2). Specifically, the additional variation explained by the initial population susceptibility, climatic factors, and antigenic change were at 33%, 26%, and 48%, respectively. The vaccination program alone failed to fully eliminate the summer epidemics of influenza A(H3N2) and non-pharmacological interventions were needed to suppress the summer circulation. The quantitative understanding of the interplay among driving factors on the circulation of influenza A(H3N2) highlights the importance of simultaneous monitoring of fluctuations for related factors, which is crucial for precise and targeted prevention and control of seasonal influenza.

Subtypes specified environmental dependence of seasonal influenza virus

Understanding the role of meteorological factors in the transmission dynamics of respiratory infectious diseases remains challenging. Our study was to comprehensively investigate the nonlinear effects of environmental factors on influenza transmission, based on multi-region surveillance data from mainland China. An approach related to time-varying reproduction number (R_t) was proposed, which extracts the environment-related components from R_t to estimate the relationship between environmental factors and influenza transmission based on a mixed-effects regression model. Nonlinear relationships for absolute humidity (the lowest transmission was observed at absolute humidity of 12 g/m³) and mean temperature (the lowest transmission was observed at the mean temperature of 18 ^°C) with influenza transmission were observed. Influenza transmission holds almost constant with the average precipitation below 1 mm or sunshine hour below 9 h/day, but increases for the precipitation and decreases for the sunshine hour afterward. The environmental dependence varies across subtypes: A(H3N2) maintains relatively higher transmission in high temperature and humidity conditions, compared with other influenza subtypes. Overall, the subtypes specified environmental dependence of influenza transmission could explain 23.1 %, 29.2 % and 27.1 % of the variations for A(H1N1)pdm09, A(H3N2) and B-lineage in China. The projected seasonal transmission rates based on our approach could be used as a valuable seasonal proxy to model the influenza dynamics under various meteorological spaces. Finally, our approach is also applicable to obtain novel insights into the impact of environmental factors on other respiratory infectious diseases.

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017

Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

Influenza incidence and air pollution: Findings from a four-year surveillance study of prefecture-level cities in China

Background

Influenza is a serious public health problem, and its prevalence and spread show significant spatiotemporal characteristics. Previous studies have found that air pollutants are linked to an increased risk of influenza. However, the mechanism of influence and the degree of their association have not been determined. This study aimed to determine the influence of the air environment on the spatiotemporal distribution of influenza.

Methods

The kernel density estimation and Getis-Ord Gi * statistic were used to analyze the spatial distribution of the influenza incidence and air pollutants in China. A simple analysis of the correlation between influenza and air pollutants was performed using Spearman's correlation coefficients. A linear regression analysis was performed to examine changes in the influenza incidence in response to air pollutants. The sensitivity of the influenza incidence to changes in air pollutants was evaluated by performing a gray correlation analysis. Lastly, the entropy weight method was used to calculate the weight coefficient of each method and thus the comprehensive sensitivity of influenza incidence to six pollution elements.

Results

The results of the sensitivity analysis using Spearman's correlation coefficients showed the following ranking of the contributions of the air pollutants to the influenza incidence in descending order: SO2 >NO2 >CO> PM2.5 >O3 >PM10. The sensitivity results obtained from the linear regression analysis revealed the following ranking: CO>NO2 >SO2 >O3 >PM2.5 >PM10. Lastly, the sensitivity results obtained from the gray correlation analysis showed the following ranking: NO2 >CO>PM10 >PM2.5 >SO2 >O3. According to the sensitivity score, the study area can be divided into hypersensitive, medium-sensitive, and low-sensitive areas.

Conclusion

The influenza incidence showed a strong spatial correlation and associated sensitivity to changes in concentrations of air pollutants. Hypersensitive areas were mainly located in the southeastern part of northeastern China, the coastal areas of the Yellow River Basin, the Beijing-Tianjin-Hebei region and surrounding areas, and the Yangtze River Delta. The influenza incidence was most sensitive to CO, NO2, and SO2, with the occurrence of influenza being most likely in areas with elevated concentrations of these three pollutants. Therefore, the formulation of targeted influenza prevention and control strategies tailored for hypersensitive, medium-sensitive, low-sensitive, and insensitive areas are urgently needed.

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

Synergistic influence of air temperature and vaccination on COVID-19 transmission and mortality in 146 countries or regions

OBJECTIVE

We aimed to determine the influence of vaccination and air temperature on COVID-19 transmission and severity.

METHODS

The study data in 146 countries from January 6, 2020 to July 28, 2022 were aggregated into 19,856 weeks. Country-level weekly incidence, time-varying reproduction number (Rt), mortality, and infection-fatality ratio (IFR) were compared among groups of these weeks with different vaccination rates and air temperatures.

RESULTS

Weeks with <15 °C air temperature and 60% vaccination showed the highest incidence (mean, 604; SD, 855; 95% CI, 553-656, unit, /100,000 persons; N = 1073) and the highest rate of weeks with >1 Rt (mean, 41.6%; SD, 1.49%; 95% CI, 39.2-45.2%; N = 1090), while weeks with >25 °C and <20% showed the lowest incidence (mean, 24; SD, 75; 95% CI, 22-26; N = 5805) and the lowest rate of weeks with >1 Rt (mean, 15.3%; SD, 0.461%; 95% CI, 14.2-16.2%; N = 6122). Mortality in weeks with <15 °C (mean, 2.1; SD, 2.8; 95% CI, 2.0-2.2, unit, /100,000 persons; N = 4365) was five times of the mortality in weeks with >25 °C (mean, 0.44; SD, 1; 95% CI, 0.41-0.46; N = 7741). IFR ranged between 2% and 2.6% (SD, 1.9%-2.4%; 95% CI, 2.0-2.7%) at < 20% vaccination level, 1.8% (SD, 2%-2.2%; 95% CI, 1.7-2.0%) at 20-60% vaccination level, and 0.7%-1% (SD, 1%-1.8%; 95% CI, 0.7-1.1%) at > 60% vaccination level and at all air temperatures (all P < 0.001).

CONCLUSIONS

Vaccination was insufficient to mitigate the transmission since the significantly elevated weekly incidence and >1 Rt rate in weeks with high vaccination, while IFR was reduced by high vaccination. Countries with long-term low air temperature were affected by high transmission and high mortality.

Association between meteorological factors and the epidemics of influenza (sub)types in a subtropical basin of Southwest China

BACKGROUND

The effects of climatic conditions on the prevalence of individual influenza (sub)types are not well understood in the subtropics. This study aims to evaluate the associations between meteorological factors and seasonal epidemics of A(H3N2), A(H1N1)pdm09, and type B influenza viruses, as well as to estimate the interactions between climatic variables in a subtropical basin region.

METHODS

The seasonality of influenza (sub)types during 2010-2019 were characterized in Chengdu Plain Economic Zone, a densely populated and highly humid plain area in Sichuan Basin in subtropical Southwest China. Generalized additive models were adopted to assess the independent exposure-response relationship between meteorological variables and influenza prevalence. The interactions of meteorological variables were further estimated using bivariate response surface models and strata models.

RESULTS

Our analyses indicated that the temperature, relative humidity, and absolute humidity have exhibited a major influence on influenza infection in Chengdu Plain Economic Zone. Low temperature was shown to promote the prevalence of A(H1N1)pdm09 and type B in winter-spring days at all levels of relative humidity. High risk of A(H3N2) infections was observed at low temperature or high temperature, and at higher relative humidity. Moreover, absolute humidity decreased or increased influenza (sub)type infections within different ranges.

CONCLUSIONS

This study found different nonlinear relationships between meteorological factors and the seasonality of influenza (sub)types, as well as significant interactive effects between climatic variables, contributing to the research on the climate drivers of influenza prevalence in warm-humid basin regions in the subtropics.

The Impact of Urbanization and Human Mobility on Seasonal Influenza in Northern China

The intensity of influenza epidemics varies significantly from year to year among regions with similar climatic conditions and populations. However, the underlying mechanisms of the temporal and spatial variations remain unclear. We investigated the impact of urbanization and public transportation size on influenza activity. We used 6-year weekly provincial-level surveillance data of influenza-like disease incidence (ILI) and viral activity in northern China. We derived the transmission potential of influenza for each epidemic season using the susceptible-exposed-infectious-removed-susceptible (SEIRS) model and estimated the transmissibility in the peak period via the instantaneous reproduction number (R_t). Public transport was found to explain approximately 28% of the variance in the seasonal transmission potential. Urbanization and public transportation size explained approximately 10% and 21% of the variance in maximum R_t in the peak period, respectively. For the mean R_t during the peak period, urbanization and public transportation accounted for 9% and 16% of the variance in R_t, respectively. Our results indicated that the differences in the intensity of influenza epidemics among the northern provinces of China were partially driven by urbanization and public transport size. These findings are beneficial for predicting influenza intensity and developing preparedness strategies for the early stages of epidemics.

Circulation and seasonality of influenza viruses in different transmission zones in Africa

Background

Influenza is responsible for more than 5 million severe cases and 290,000 to 650,000 deaths every year worldwide. Developing countries account for 99% of influenza deaths in children under 5 years of age. This paper aimed to determine the dynamics of influenza viruses in African transmission areas to identify regional seasonality for appropriate decision-making and the development of regional preparedness and response strategies.

Methods

We used data from the WHO FluMart website collected by National Influenza Centers for seven transmission periods (2013–2019). We calculated weekly proportions of positive influenza cases and determined transmission trends in African countries to determine the seasonality.

Results

From 2013 to 2019, influenza A(H1N1)pdm2009, A(H3N2), and A(H5N1) viruses, as well as influenza B Victoria and Yamagata lineages, circulated in African regions. Influenza A(H1N1)pdm2009 and A(H3N2) highly circulated in northern and southern Africa regions. Influenza activity followed annual and regional variations. In the tropical zone, from eastern to western via the middle regions, influenza activities were marked by the predominance of influenza A subtypes despite the circulation of B lineages. One season was identified for both the southern and northern regions of Africa. In the eastern zone, four influenza seasons were differentiated, and three were differentiated in the western zone.

Conclusion

Circulation dynamics determined five intense influenza activity zones in Africa. In the tropics, influenza virus circulation waves move from the east to the west, while alternative seasons have been identified in northern and southern temperate zones. Health authorities from countries with the same transmission zone, even in the absence of local data based on an established surveillance system, should implement concerted preparedness and control activities, such as vaccination.

Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020

The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM2.5, and PM10). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM2.5 and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM2.5 lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM10 and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.

Seasonal variations and risk factors of Streptococcus pyogenes infection: a multicenter research network study

Background:

Streptococcus pyogenes, or Group A Streptococcus (GAS), causes acute pharyngitis and necrotizing fasciitis. Seasonal variations in GAS infections are not robustly characterized. We assessed seasonal variations and risk factors of GAS pharyngitis and ICD-10-diagnosed necrotizing fasciitis.

Methods:

From the period 2010–2019, we conducted a case–control study using laboratory-confirmed cases of GAS pharyngitis and a descriptive observational study of necrotizing fasciitis using ICD-10 codes. Data were collected from TriNetX, a federated research network. We extracted seasonal (quarterly) incidence rates. We used an autoregressive integrated moving average (ARIMA) model to assess seasonal variations. Demographic characteristics and 1-month outcomes were compared among adults with or without GAS pharyngitis.

Results:

We identified 224,471 adults with GAS pharyngitis (test-positive) and 546,142 adults without it (test-negative). GAS pharyngitis adults were younger (25.3 versus 30.2 years of age, p < 0.0001), more likely to be Hispanic individuals (10% versus 8%, p < 0.0001) and slightly more likely to be Black or African American individuals (14% versus 13%, p < 0.0001). Propensity score matching found that adults with test-positive cases of GAS pharyngitis had a higher risk of acute rheumatic fever while having no significant differences in risk of intensive care unit admission and mortality compared with test-negative cases. GAS pharyngitis average incidence peaked in the winter while dipping in the summer (0.32 versus 0.18 and 4.07 versus 1.78 per 1000 adults and pediatric patients, respectively). Necrotizing fasciitis diagnoses were highest during summer (0.032 per 1000 adults). There was a significant ARIMA seasonal variation in the time series analysis for adult and pediatric GAS pharyngitis (p < 0.0001 and p = 0.014, respectively). Necrotizing fasciitis diagnosis was not associated with seasonal variation (p = 0.861).

Conclusion:

Peaks in GAS pharyngitis occur in the winter months. ICD code–based necrotizing fasciitis did not show a quarterly seasonal variation.

The influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza activity in China

Previous research has extensively studied the seasonalities of human influenza infections and the effect of specific climatic factors in different regions. However, there is limited understanding of the influences of monsoons. This study applied generalized additive model with monthly surveillance data from mainland China to explore the influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza in China. The results suggested two influenza active periods in northern China and three active periods in southern China. The study found that the northerly advancement of East Asian Summer Monsoon (EASM) influences the summer influenza spatio-temporal patterns in both southern and northern China. At the interannual scale, the north-south converse effect of EASM on influenza activity is mainly due to the converse effect of EASM on humidity and precipitation. Within the annual scale, influenza activity in southern China gradually reaches its maximum during the summer exacerbated by the northerly advancement of EASM. Furthermore, the winter epidemic in China is related to the low temperature and humidity influenced by the East Asian Winter Monsoon (EAWM). Moreover, the active period in transition season is related partially to the large rapid temperature change influenced by the transition of EAWM and EASM. Despite the delayed onset and instability, the climatic condition influenced by the East Asian Monsoon is one of the potential key drivers of influenza activity.