Influenza seasonality and its environmental driving factors in mainland China and Hong Kong

Association of ambient temperature and influenza-like illness with acute appendicitis: an ecological study using 22-year data

BACKGROUND

While acute appendicitis poses a significant disease burden worldwide, its etiology is not completely known. Previous studies have separately demonstrated its associations with ambient temperature and seasonal influenza, but there was no study that examined two exposures concurrently, leaving room for confounding and failing to isolate the effects of these two factors. This study aims to quantify such associations under a unified model, using population-level data in Hong Kong from 1998 to 2019.

METHODS

The study outcome of weekly acute appendicitis admissions was analyzed with a number of covariates. The major covariates of interest included weekly mean temperature and three strain-specific influenza-like illness-positive (ILI+) rates, which were proxies for the activities of the respective influenza strains. Other covariates including weekly mean relative humidity, total rainfall and a composite index for air pollution were used for confounder control. A generalized additive model under the framework of distributed-lag non-linear model and quasi-Poisson distribution was used for multivariate analysis.

RESULTS

A significant positive association between ambient temperature and acute appendicitis admission was found, with a cumulative adjusted relative risk (ARR) of 1.082 (95% CI: 1.065-1.099) comparing the 95th percentile to the median temperature. ILI + rates for influenza A/H1N1 and A/H3N2 were found to significantly and negatively associate with acute appendicitis admission, with cumulative ARRs of 0.961 (95% CI: 0.934-0.989) and 0.961 (95% CI: 0.929-0.993) respectively, comparing the 95th percentiles to zero. No significant association was found between ILI + rate for influenza B and acute appendicitis admission.

CONCLUSIONS

While high temperature was associated with acute appendicitis admission, a negative association of influenza infection was showed. The mechanisms underlying the above associations should be investigated in future studies, with the aim of formulating preventive strategies against acute appendicitis that take environmental exposures into consideration.

Effects of temperature and environmental covariates on the dynamic transmission of hand, foot, and mouth disease in Zhejiang, China

BACKGROUND

Studies have documented the impact of temperature on the incidence of hand, foot, and mouth disease (HFMD); however, no study has examined its impact on the transmissibility.

METHODS

The longitudinal surveillance data of HFMD in Zhejiang Province during 2013-2019 were collected from National Notifiable Infectious Diseases Reporting Information System. The incidence of HFMD was represented by daily case counts, and the transmissibility was quantified as the instantaneous reproductive number ([Formula: see text]). The case time series design was applied to investigate the association between temperature and HFMD incidence at small-scale spatial patterns (i.e., townships). General additive model was further employed to analyze the effects of temperature and other driving factors on the transmissibility of HFMD. Separate models were also conducted for each city, along with seasonal and spatial stratified analysis.

RESULTS

We observed an inverted V-shaped association between temperature and HFMD incidence, with the highest cumulative relative risk (RR: 3.81, 95% CI: 3.75-3.86) at 28°C compared to the reference temperature. Notably, we discovered that HFMD transmissibility exhibited a similar but more pronounced sensitivity to temperature changes, peaking at a lower temperature of 19.69°C. City-specific and stratified results were aligned with the overall provincial pattern. Additionally, other significant driving factors of HFMD transmissibility included the depletion of susceptible individuals, school holidays, vaccination program, relative humidity, and the Normalized Difference Vegetation Index.

CONCLUSION

Nonlinear associations between temperature and HFMD incidence, as well as transmissibility, are observed. Other driving factors potentially contribute to changes in HFMD dynamic transmission. These findings underscore the importance of implementing targeted policies aimed at early intervention, particularly when HFMD transmissibility begins to reach its peak.

Improving influenza forecast in the tropics and subtropics: a case study of Hong Kong

Influenza forecasts could aid public health response as shown for temperate regions, but such efforts are more challenging in the tropics and subtropics due to more irregular influenza activities. Here, we built six forecast approaches for influenza in the (sub)tropics, with six model forms designed to model seasonal infection risk (i.e. seasonality) based on the dependence of virus survival on climate conditions and to flexibly account for immunity waning. We ran the models jointly with the ensemble adjustment Kalman filter to generate retrospective forecasts of influenza incidence in subtropical Hong Kong from January 1999 to December 2019 including the 2009 A(H1N1)pdm09 pandemic. In addition to short-term targets (one to four weeks ahead predictions), we also tested mid-range (one to three months) and long-range (four to six months) forecasts, which could be valuable for long-term planning. The largest improvement came from the inclusion of climate-modulated seasonality modelling, particularly for the mid- and long-range forecasts. The best-performing approach included a seasonal-trend-based climate modulation and assumed mixed immunity waning; the forecast accuracies, including peak week and intensity, were comparable to that reported for temperate regions including the USA. These findings demonstrate that incorporating mechanisms of climate modulation on influenza transmission can substantially improve forecast performance in the (sub)tropics.

Characterizing the seasonal influenza disease burden attributable to climate variability: A nationwide time-series modelling study in Japan, 2000-2019

BACKGROUND

Ambient temperature and humidity are established environmental stressors with regard to influenza infections; however, mapping disease burden is difficult owing to the complexities of the underlying associations and differences in vulnerable population distributions. In this study, we aimed to quantify the burden of influenza attributable to non-optimal ambient temperature and absolute humidity in Japan considering geographical differences in vulnerability.

METHODS

The exposure-lag-response relationships between influenza incidence, ambient temperature, and absolute humidity in all 47 Japanese prefectures for 2000-2019 were quantified using a distributed lag non-linear model for each prefecture; the estimates from all the prefectures were then pooled using a multivariate mixed-effects meta-regression model to derive nationwide average associations. Association between prefecture-specific indicators and the risk were also examined. Attributable risks were estimated for non-optimal ambient temperature and absolute humidity according to the exposure-lag-response relationships obtained before.

RESULTS

A total of 25,596,525 influenza cases were reported during the study period. Cold and dry conditions significantly increased influenza incidence risk. Compared with the minimum incidence weekly mean ambient temperature (29.8 °C) and the minimum incidence weekly mean absolute humidity (20.2 g/m3), the cumulative relative risks (RRs) of influenza in cold (2.5 °C) and dry (3.6 g/m3) conditions were 2.79 (95% confidence interval [CI]: 1.78-4.37) and 3.20 (95% CI: 2.37-4.31), respectively. The higher RRs for cold and dry conditions were associated with geographical and climatic indicators corresponding to the central and northern prefectures; demographic, socioeconomic, and health resources indicators showed no clear trends. Finally, 27.25% (95% empirical CI [eCI]: 5.54-36.35) and 32.35% (95% eCI: 22.39-37.87) of all cases were attributable to non-optimal ambient temperature and absolute humidity (6,976,300 [95% eCI: 1,420,068-9,306,128] and 8,280,981 [95% eCI: 8,280,981-9,693,532] cases), respectively.

CONCLUSIONS

These findings could help identify the most vulnerable populations in Japan and design adaptation policies to reduce the attributable burden of influenza due to climate variability.

Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control

Pathogen-pathogen interactions represent a critical but little-understood feature of infectious disease dynamics. In particular, experimental evidence suggests that influenza virus and respiratory syncytial virus (RSV) compete with each other, such that infection with one confers temporary protection against the other. However, such interactions are challenging to study using common epidemiologic methods. Here, we use a mathematical modeling approach, in conjunction with detailed surveillance data from Hong Kong and Canada, to infer the strength and duration of the interaction between influenza and RSV. Based on our estimates, we further utilize our model to evaluate the potential conflicting effects of live attenuated influenza vaccines (LAIV) on RSV burden. We find evidence of a moderate to strong, negative, bidirectional interaction, such that infection with either virus yields 40-100% protection against infection with the other for one to five months. Assuming that LAIV reduces RSV susceptibility in a similar manner, we predict that the impact of such a vaccine at the population level would likely depend greatly on underlying viral circulation patterns. More broadly, we highlight the utility of mathematical models as a tool to characterize pathogen-pathogen interactions.

An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context

Forecasting influenza activity in tropical and subtropical regions, such as Hong Kong, is challenging due to irregular seasonality and high variability. We develop a diverse set of statistical, machine learning, and deep learning approaches to forecast influenza activity in Hong Kong 0 to 8 weeks ahead, leveraging a unique multi-year surveillance record spanning 32 epidemics from 1998 to 2019. We consider a simple average ensemble (SAE) of the top two individual models, and develop an adaptive weight blending ensemble (AWBE) that dynamically updates model contribution. All models outperform the baseline constant incidence model, reducing the root mean square error (RMSE) by 23%-29% and weighted interval score (WIS) by 25%-31% for 8-week ahead forecasts. The SAE model performed similarly to individual models, while the AWBE model reduces RMSE by 52% and WIS by 53%, outperforming individual models for forecasts in different epidemic trends (growth, plateau, decline) and during both winter and summer seasons. Using the post-COVID data (2023-2024) as another test period, the AWBE model still reduces RMSE by 39% and WIS by 45%. Our framework contributes to comparing and benchmarking models in ensemble forecasts, enhancing evidence for synthesizing multiple models in disease forecasting for geographies with irregular influenza seasonality.

Molecular epidemiology and vaccine compatibility analysis of seasonal influenza A viruses in the context of COVID-19 epidemic in Wuhan, China

The COVID-19 pandemic had a significant impact on the global influenza vaccination and the epidemics of seasonal influenza. To further explore the molecular epidemiology of influenza viruses and assess vaccine effectiveness, we collected influenza cases in Wuhan during the 2022-2023 influenza season. Among 1312 clinical samples, 312 samples tested positive for influenza viruses using reverse transcription polymerase chain reaction. These positive samples included 146A/H1N1 subtypes (46.8%), 164A/H3N2 subtypes (52.6%) and 2 influenza B virus types (0.6%). Based on the whole genome sequence information of hemagglutinin (HA) and neuraminidase (NA) from 27A/H1N1 influenza virus strains and 26A/H3N2 influenza virus strains obtained in this study, a phylogenetic analysis was conducted. The analysis revealed that all A/H1N1 strains belonged to the evolutionary branch 6B.1A.5a.2a, and they exhibited specific substitutions at positions K71Q, Q206E, E241A, and R276K. Similarly, all A/H3N2 strains were classified into the 3C.2a1b.2a.1a subclade and displayed amino acid substitutions at positions S172H, N175Y, I176T, K187N, and S214P. Notably, the A/H3N2 strains also acquired a new potential glycosylation site at position N174. Using an epitope model, the predicted vaccine effectiveness was assessed for the A/H1N1 and A/H3N2 strains. The predicted vaccine effectiveness against the Wuhan influenza epidemic strain was over 85% for the A/H1N1 vaccine strain. However, the effectiveness against the A/H3N2 vaccine strain was only 48.7%. To further verify the protection of influenza vaccine against circulating influenza viruses in the region, we conducted in vivo and in vitro animal studies. The results of in vitro neutralization experiment showed that rabbit serum antibodies inoculated with quadrivalent isolated influenza vaccine had neutralization ability against all 24 isolated influenza viruses. In vivo experiments showed that vaccinated mice had fewer lung lesions when infected with the influenza strain circulating in Wuhan, suggesting that vaccination can effectively reduce the occurrence of severe lung damage. These findings emphasize the importance of accurately predicting seasonal influenza strains for effective influenza prevention and control, especially during the co-circulation of SARS-CoV-2 and influenza viruses. This study provides valuable information on the seasonal influenza virus in Wuhan during the COVID-19 pandemic and serves as a basis for vaccine prediction and updates.

Two Peaks of Seasonal Influenza Epidemics - China, 2023

Introduction

Influenza is a communicable respiratory disease that causes annual epidemics and occasional unpredictable pandemics. This study examines the occurrence of two unexpected influenza peaks within the year 2023.

Methods

Influenza-like illness (ILI) data were reported, and specimens of ILI were collected by designated surveillance hospitals. Positive cases were confirmed using real-time reverse transcription polymerase chain reaction (rRT-PCR) testing conducted at national surveillance network laboratories. The data were then submitted to the Chinese Influenza Surveillance Information System.

Results

From December 2022 to January 2023, influenza activity was minimal until a spring epidemic began in February, peaking in late March. The outbreak spread from the north to the south, with A(H1N1)pdm09 being the predominant strain and A(H3N2) also circulating concurrently. The positivity rate in northern and southern provincial-level administrative divisions (PLADs) reached 60.0% (week 10) and 60.2% (week 13), respectively, before dropping to below 5% from May to August. Subsequently, rates surged starting in week 41 in the south and week 44 in the north, peaking at 54.4% (week 50) and 44.0% (week 49), respectively, and remained high until the end of 2023. A(H3N2) became the prevailing strain, with a notable increase in B/Victoria lineage viruses from December, resulting in two peaks during the 2023 influenza season.

Conclusions

The pattern of the influenza epidemic shifted due to the coronavirus disease 2019 (COVID-19) outbreak and is gradually returning to its usual seasonal and intensity patterns. It is, therefore, crucial to continuously strengthen influenza surveillance, enhance the influenza surveillance network, and lay the groundwork for advancing integrated surveillance of multiple pathogens in China.

Temperature and humidity as drivers for the transmission of zoonotic diseases

Zoonotic diseases remain a persistent threat to global public health. Many major zoonotic pathogens exhibit seasonal patterns associated with climatic variations. Quantifying the impacts of environmental variables such as temperature and humidity on disease transmission dynamics is critical for improving prediction and control measures. This review synthesizes current evidence on the relationships between temperature and humidity and major zoonotic diseases, including malaria, dengue, rabies, anisakiasis, and influenza. Overall, this review highlighted some overarching themes across the different zoonotic diseases examined. Higher temperatures within suitable ranges were generally associated with increased transmission risks, while excessively high or low temperatures had adverse effects. Humidity exhibited complex nonlinear relationships, facilitating transmission in certain temperature zones but inhibiting it in others. Heavy rainfall and high humidity were linked to vector‐borne disease outbreaks such as malaria by enabling vector breeding. However, reduced incidence of some diseases like dengue fever was observed with high rainfall. To address existing knowledge gaps, future research efforts should prioritize several key areas: enhancing data quality through robust surveillance and the integration of high‐resolution microclimate data; standardizing analytical frameworks and leveraging advanced methodologies such as machine learning; conducting mechanistic studies to elucidate pathogen, vector, and host responses to climatic stimuli; adopting interdisciplinary approaches to account for interacting drivers; and projecting disease impacts under various climate change scenarios to inform adaptation strategies. Investing in these research priorities can propel the development of evidence‐based climate‐aware disease prediction and control measures, ultimately safeguarding public health more effectively.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong

Like other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1-4 weeks ahead) and medium-term forecasts (1-13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.

Association between ambient temperature and influenza prevalence: A nationwide time-series analysis in 201 Chinese cities from 2013 to 2018

BACKGROUND

Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions.

METHODS

Meteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at Rt = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between Rt and temperature, modified based on seasonal and regional characteristics.

RESULTS

At the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an Rt of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were - 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions.

CONCLUSION

The study findings revealed that temperature had a varying effect on influenza transmission trends (Rt) across different regions in China. By identifying region-specific temperature thresholds at Rt = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China

Objective

This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems.

Methods

Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve.

Results

With an advance warning set at three days, the SEAR model outperformed five primary models - logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model - in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios.

Conclusion

The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019

Introduction

This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics.

Methods

In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method.

Results

Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs.

Discussion

The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Significant differences in usage of antibiotics in three Chinese cities measured by wastewater-based epidemiology

Antibiotic use, particularly inappropriate use by irrational prescribing or over-the-counter purchases, is of great concern for China as it facilitates the spread of antibiotic resistances. In this study, we applied wastewater-based epidemiology (WBE) to monitor the total consumption of eight common antibiotics in three cities in northern, eastern and southern China. Wastewater samples were successively collected from 17 wastewater treatment plants including weekdays and weekends spanning four seasons between 2019 and 2021. The concentration of antibiotics and their corresponding metabolites showed a significant correlation, confirming the measured antibiotics were actually consumed. Different seasonal trends in antibiotic use were found among the cities. It was more prevalent in the winter in the northern city Beijing, with the high antibiotic consumption attributed to peak influenza occurrence in the city. This is clear evidence of irrational prescription of antibiotics since it's known that antibiotics do little to treat influenza. In terms of overall consumption, Foshan is significantly lower, thanks to warmer climate and higher use of herbal tea as a prevention measure. WBE estimates of antibiotic consumption were relatively comparable with other data sources, with azithromycin as the top antibiotic measured here. The studied cities had higher WBE estimated antibiotics consumption than results of previous studies in the literature. Monitoring antibiotic use in different areas and periods through WBE in combination with complementary information, can better inform appropriate antibiotic guideline policies in various regions and nations.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases

BACKGROUND

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

METHODS

We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

RESULTS

We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

CONCLUSIONS

Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Cocirculation and coinfection of multiple respiratory viruses during autumn and winter seasons of 2023 in Beijing, China: A retrospective study

China experienced severe epidemics of multiple respiratory pathogens in 2023 after lifting "Zero-COVID" policy. The present study aims to investigate the changing circulation and infection patterns of respiratory pathogens in 2023. The 160 436 laboratory results of influenza virus and respiratory syncytial virus (RSV) from February 2020 to December 2023, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from June 2020 to December 2023, Mycoplasma pneumoniae, adenovirus, and human rhinovirus from January 2023 to December 2023 were analyzed. We observed the alternating epidemics of SARS-CoV-2 and influenza A virus (IAV), as well as the out-of-season epidemic of RSV during the spring and summer of 2023. Cocirculation of multiple respiratory pathogens was observed during the autumn and winter of 2023. The susceptible age range of RSV in this winter epidemic (10.5, interquartile range [IQR]: 5-30) was significantly higher than previously (4, IQR: 3-34). The coinfection rate of IAV and RSV in this winter epidemic (0.695%) was significantly higher than that of the last cocirculation period (0.027%) (p < 0.001). Similar trend was also found in the coinfection of IAV and SARS-CoV-2. The present study observed the cocirculation of multiple respiratory pathogens, changing age range of susceptible population, and increasing coinfection rates during the autumn and winter of 2023, in Beijing, China.

Influenza vaccine effectiveness against hospitalizations associated with influenza A(H3N2) in Hong Kong children aged 9 months to 17 years, June-November 2023

A test negative study was carried out from 13 June through to 15 November 2023 enrolling 3183 children hospitalized with acute respiratory illness in Hong Kong. Influenza A and B viruses were detected in 528 (16.6%) children, among which 419 (79.4%) were influenza A(H3N2). The overall vaccine effectiveness against hospitalization associated with any influenza virus infection was estimated as 22.4% (95% CI: -11.7%, 46.1%), and against influenza A(H3N2) specifically was 14.3% (95% CI: -29.2%, 43.2%). Despite the moderate to low VE estimated here, which could be a result of waning immunity and antigenic drift, influenza vaccination remains an important approach to reduce the impact of influenza in children.

Effects of scheduled school breaks on the circulation of influenza in children, school-aged population, and adults in China: A spatio-temporal analysis

OBJECTIVES

To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults.

METHODS

In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza.

RESULTS

We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years.

CONCLUSION

The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics.

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.

The effect of nonpharmaceutical interventions on influenza virus transmission

Background

The use of nonpharmaceutical interventions (NPIs) during severe acute respiratory syndrome 2019 (COVID-19) outbreaks may influence the spread of influenza viruses. This study aimed to evaluate the impact of NPIs against SARS-CoV-2 on the epidemiological features of the influenza season in China.

Methods

We conducted a retrospective observational study analyzing influenza monitoring data obtained from the China National Influenza Center between 2011 and 2023. We compared the changes in influenza-positive patients in the pre-COVID-19 epidemic, during the COVID-19 epidemic, and post-COVID-19 epidemic phases to evaluate the effect of NPIs on influenza virus transmission.

Results

NPIs targeting COVID-19 significantly suppressed influenza activity in China from 2019 to 2022. In the seventh week after the implementation of the NPIs, the number of influenza-positive patients decreased by 97.46% in southern regions of China and 90.31% in northern regions of China. However, the lifting of these policies in December 2022 led to an unprecedented surge in influenza-positive cases in autumn and winter from 2022 to 2023. The percentage of positive influenza cases increased by 206.41% (p < 0.001), with high positivity rates reported in both the northern and southern regions of China.

Conclusion

Our findings suggest that NPIs against SARS-CoV-2 are effective at controlling influenza epidemics but may compromise individuals' immunity to the virus.

The role of absolute humidity in influenza transmission in Beijing, China: risk assessment and attributable fraction identification

To assess the impact of absolute humidity on influenza transmission in Beijing from 2014 to 2019, we estimated the influenza transmissibility via the instantaneous reproduction number (Rt), and evaluated its nonlinear exposure-response association and delayed effects with absolute humidity by using the distributed lag nonlinear model (DLNM). Attributable fraction (AF) of Rt due to absolute humidity was calculated. The result showed a significant M-shaped relationship between Rt and absolute humidity. Compared with the effect of high absolute humidity, the low absolute humidity effect was more immediate with the most significant effect observed at lag 6 days. AFs were relatively high for the group aged 15-24 years, and was the lowest for the group aged 0-4 years with low absolute humidity. Therefore, we concluded that the component attributed to the low absolute humidity effect is greater. Young and middle-aged people are more sensitive to low absolute humidity than children and elderly.

The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022

Objective

Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.

Methods

The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.

Results

The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group.

Conclusion

Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.

Evaporation of Concentrated Polymer Solutions Is Insensitive to Relative Humidity

A recent theory suggests that the evaporation kinetics of macromolecular solutions is insensitive to the ambient relative humidity (RH) due to the formation of a "polarization layer" of solutes at the air-solution interface. We confirm this insensitivity up to RH≈80% in the evaporation of polyvinyl alcohol solutions from open-ended capillaries. To explain the observed drop in evaporation rate at higher RH, we need to invoke compressive stresses due to interfacial polymer gelation. Moreover, RH-insensitive evaporation sets in earlier than theory predicts, suggesting a further role for a gelled "skin." We discuss the relevance of these observations for respiratory virus transmission via aerosols.

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.

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

Multi-omics data integration reveals the complexity and diversity of host factors associated with influenza virus infection

Influenza viruses pose a significant and ongoing threat to human health. Many host factors have been identified to be associated with influenza virus infection. However, there is currently a lack of an integrated resource for these host factors. This study integrated human genes and proteins associated with influenza virus infections for 14 subtypes of influenza A viruses, as well as influenza B and C viruses, and built a database named H2Flu to store and organize these genes or proteins. The database includes 28,639 differentially expressed genes (DEGs), 1,850 differentially expressed proteins, and 442 proteins with differential posttranslational modifications after influenza virus infection, as well as 3,040 human proteins that interact with influenza virus proteins and 57 human susceptibility genes. Further analysis showed that the dynamic response of human cells to virus infection, cell type and strain specificity contribute significantly to the diversity of DEGs. Additionally, large heterogeneity was also observed in protein-protein interactions between humans and different types or subtypes of influenza viruses. Overall, the study deepens our understanding of the diversity and complexity of interactions between influenza viruses and humans, and provides a valuable resource for further studies on such interactions.

Temporal Association Among Influenza-Like Illness, Cardiovascular Events, and Vaccine Dose in Patients With High-Risk Cardiovascular Disease: Secondary Analysis of a Randomized Clinical Trial

Importance

Influenza-like illness (ILI) activity has been associated with increased risk of cardiopulmonary (CP) events during the influenza season. High-dose trivalent influenza vaccine was not superior to standard-dose quadrivalent vaccine for reducing these events in patients with high-risk cardiovascular (CV) disease in the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial.

Objective

To evaluate whether high-dose trivalent influenza vaccination is associated with benefit over standard-dose quadrivalent vaccination in reducing CP events during periods of high, local influenza activity.

Design, Setting, and Participants

This study was a prespecified secondary analysis of INVESTED, a multicenter, double-blind, active comparator randomized clinical trial conducted over 3 consecutive influenza seasons from September 2016 to July 2019. Follow-up was completed in July 2019, and data were analyzed from September 21, 2016, to July 31, 2019. Weekly Centers for Disease Control and Prevention (CDC)-reported, state-level ILI activity was ascertained to assess the weekly odds of the primary outcome. The study population included 3094 patients with high-risk CV disease from participating centers in the US.

Intervention

Participants were randomized to high-dose trivalent or standard-dose quadrivalent influenza vaccine and revaccinated for up to 3 seasons.

Main Outcomes and Measures

The primary outcome was the time to composite of all-cause death or CP hospitalization within each season. Additional measures included weekly CDC-reported ILI activity data by state.

Results

Among 3094 participants (mean [SD] age, 65 [12] years; 2309 male [75%]), we analyzed 129 285 person-weeks of enrollment, including 1396 composite primary outcome events (1278 CP hospitalization, 118 deaths). A 1% ILI increase in the prior week was associated with an increased risk in the primary outcome (odds ratio [OR], 1.14; 95% CI, 1.07-1.21; P < .001), CP hospitalization (OR, 1.13; 95% CI, 1.06-1.21; P < .001), and CV hospitalization (OR, 1.12; 95% CI, 1.04-1.19; P = .001), after adjusting for state, demographic characteristics, enrollment strata, and CV risk factors. Increased ILI activity was not associated with all-cause death (OR, 1.00; 95% CI, 0.88-1.13; P > .99). High-dose compared with standard-dose vaccine did not significantly reduce the primary outcome, even when the analysis was restricted to weeks of high ILI activity (OR, 0.88; 95% CI, 0.65-1.20; P = .43). Traditionally warmer months in the US were associated with lower CV risk independent of local ILI activity.

Conclusions and Relevance

In this secondary analysis of a randomized clinical trial, ILI activity was temporally associated with increased CP events in patients with high-risk CV disease, and a higher influenza vaccine dose did not significantly reduce temporal CV risk. Other seasonal factors may play a role in the coincident high rates of ILI and CV events.

Trial Registration

ClinicalTrials.gov Identifier: NCT02787044.

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.

Impact of meteorological and demographic factors on the influenza epidemic in Japan: a large observational database study

Factors affecting the start date of the influenza epidemic season and total number of infected persons per 1,000,000 population in 47 prefectures of Japan were evaluated. This retrospective observational study (September 2014-August 2019; N = 472,740-883,804) evaluated data from a Japanese health insurance claims database. Single and multiple regression analyses evaluated the time to start of the epidemic or total infected persons per 1,000,000 population with time to absolute humidity (AH) or number of days with AH (≤ 5.5, ≤ 6.0, ≤ 6.5, and ≤ 7.0), total visitors (first epidemic month or per day), and total population. For the 2014/15, 2015/16, and 2016/17 seasons, a weak-to-moderate positive correlation (R2: 0.042-0.417) was observed between time to start of the epidemic and time to first day with AH below the cutoff values. Except in the 2016/17 season (R2: 0.089), a moderate correlation was reported between time to start of the epidemic and the total population (R2: 0.212-0.401). For all seasons, multiple regression analysis showed negative R2 for time to start of the epidemic and total visitors and population density (positive for time to AH ≤ 7.0). The earlier the climate becomes suitable for virus transmission and the higher the human mobility (more visitors and higher population density), the earlier the epidemic season tends to begin.

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.

The association between ambient pollutants and influenza transmissibility: A nationwide study involving 30 provinces in China

Background

The impact of exposure to ambient pollutants on influenza transmissibility is poorly understood. We aim to examine the associations of six ambient pollutants with influenza transmissibility in China and assess the effect of the depletion of susceptibles.

Methods

Provincial-level surveillance data on weekly influenza-like illness (ILI) incidence and viral activity were utilized to estimate the instantaneous reproduction number (R_t) using spline functions. Log-linear regression and the distributed lag non-linear model (DLNM) were employed to investigate the effects of ambient pollutants-ozone (O₃), particulate matter ≤2.5 μm (PM_2.5), particulate matter ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO)-on influenza transmissibility across 30 Chinese provinces from 2014 to 2019. Additionally, the potential effects of the depletion of susceptibles and regional characteristics were explored.

Results

There is a significantly positive correlation between influenza transmissibility and five distinct ambient pollutants: PM_2.5, PM₁₀, SO₂, CO, and NO₂. On average, these ambient pollutants explained percentages of the variance in R_t: 0.8%, 0.8%, 1.9%, 1.3%, and 1.4%, respectively. Conversely, O₃ was found to be negatively associated with R_t, explaining 1.5% of the variance in R_t. When controlling for the effect of susceptibles depletion, the effects of all pollutants were more pronounced. The effects of PM_2.5, PM₁₀, CO, and SO₂ were higher in the eastern and southern regions.

Conclusions

Most ambient pollutants may potentially contribute to the facilitation of human-to-human influenza virus transmission in China. This observed association was maintained even after adjusting for variation in the susceptible population.

Epidemiological change of influenza virus in hospitalized children with acute respiratory tract infection during 2014-2022 in Hubei Province, China

PURPOSE

Influenza virus (IFV) causes acute respiratory tract infection (ARTI) and leads to high morbidity and mortality annually. This study explored the epidemiological change of IFV after the implementation of the universal two-child policy and evaluated the impact of coronavirus disease 2019 (COVID-19) pandemic on the detection of IFV.

METHODS

Hospitalized children under 18 years with ARTI were recruited from Hubei Maternal and Child Healthcare Hospital of Hubei Province from January 2014 to June 2022. The positive rates of IFV were compared among different periods by the implementation of the universal two-child policy and public health measures against COVID-19 pandemic.

RESULTS

Among 75,128 hospitalized children with ARTI, the positive rate of IFV was 1.98% (1486/75128, 95% CI 1.88-2.01). Children aged 6-17 years had the highest positive rate of IFV (166/5504, 3.02%, 95% CI 2.58-3.50). The positive rate of IFV dropped to the lowest in 2015, then increased constantly and peaked in 2019. After the universal two-child policy implementation, the positive rate of IFV among all the hospitalized children increased from 0.40% during 2014-2015 to 2.70% during 2017-2019 (RR 6.72, 95% CI 4.94-9.13, P < 0.001), particularly children under one year shown a violent increasing trend from 0.20 to 2.01% (RR 10.26, 95% CI 5.47-19.23, P < 0.001). During the initial outbreak of COVID-19, the positive rate of IFV decreased sharply compared to that before COVID-19 (0.35% vs. 3.37%, RR 0.10, 95% CI 0.04-0.28, P < 0.001), and then rebounded to 0.91%, lower than the level before COVID-19 (RR 0.26, 95% CI 0.20-0.36, P < 0.001).

CONCLUSION

IFV epidemiological pattern has changed after the implementation of the universal two-child policy. More attention should be emphasized to comprehend the health benefits generated by COVID-19 restrictions on IFV transmission in future.

Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping

The incidence of respiratory infections in the population is related to many factors, among which environmental factors such as air quality, temperature, and humidity have attracted much attention. In particular, air pollution has caused widespread discomfort and concern in developing countries. Although the correlation between respiratory infections and air pollution is well known, establishing causality between them remains elusive. In this study, by conducting theoretical analysis, we updated the procedure of performing the extended convergent cross-mapping (CCM, a method of causal inference) to infer the causality between periodic variables. Consistently, we validated this new procedure on the synthetic data generated by a mathematical model. For real data in Shaanxi province of China in the period of 1 January 2010 to 15 November 2016, we first confirmed that the refined method is applicable by investigating the periodicity of influenza-like illness cases, an air quality index, temperature, and humidity through wavelet analysis. We next illustrated that air quality (quantified by AQI), temperature, and humidity affect the daily influenza-like illness cases, and, in particular, the respiratory infection cases increased progressively with increased AQI with a time delay of 11 days.

Viral respiratory infections in a rapidly changing climate: the need to prepare for the next pandemic

Viral respiratory infections (VRIs) cause seasonal epidemics and pandemics, with their transmission influenced by climate conditions. Despite the risks posed by novel VRIs, the relationships between climate change and VRIs remain poorly understood. In this review, we synthesized existing literature to explore the connections between changes in meteorological conditions, extreme weather events, long-term climate warming, and seasonal outbreaks, epidemics, and pandemics of VRIs from an interdisciplinary perspective. We proposed a comprehensive conceptual framework highlighting the potential biological, socioeconomic, and ecological mechanisms underlying the impact of climate change on VRIs. Our findings suggested that climate change increases the risk of VRI emergence and transmission by affecting the biology of viruses, host susceptibility, human behavior, and environmental conditions of both society and ecosystems. Further interdisciplinary research is needed to address the dual challenge of climate change and pandemics.

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.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation

BACKGROUND

In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement.

OBJECTIVE

The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity.

METHODS

We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R² value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models.

RESULTS

The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R² reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R² reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect.

CONCLUSIONS

The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Seasonal transmission dynamics of varicella in Japan: The role of temperature and school holidays

In Japan, major and minor bimodal seasonal patterns of varicella have been observed. To investigate the underlying mechanisms of seasonality, we evaluated the effects of the school term and temperature on the incidence of varicella in Japan. We analyzed epidemiological, demographic and climate datasets of seven prefectures in Japan. We fitted a generalized linear model to the number of varicella notifications from 2000 to 2009 and quantified the transmission rates as well as the force of infection, by prefecture. To evaluate the effect of annual variation in temperature on the rate of transmission, we assumed a threshold temperature value. In northern Japan, which has large annual temperature variations, a bimodal pattern in the epidemic curve was observed, reflecting the large deviation in average weekly temperature from the threshold value. This bimodal pattern was diminished with southward prefectures, gradually shifting to a unimodal pattern in the epidemic curve, with little temperature deviation from the threshold. The transmission rate and force of infection, considering the school term and temperature deviation from the threshold, exhibited similar seasonal patterns, with a bimodal pattern in the north and a unimodal pattern in the south. Our findings suggest the existence of preferable temperatures for varicella transmission and an interactive effect of the school term and temperature. Investigating the potential impact of temperature elevation that could reshape the epidemic pattern of varicella to become unimodal, even in the northern part of Japan, is required.

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.

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.

Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study

BACKGROUND

The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics.

METHODS

For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect.

FINDINGS

We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17·3% (95% CI 13·3-21·4) to 40·6% (35·2-45·9) and attack rate by 5·1% (1·5-7·2) to 24·8% (20·8-27·5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes.

INTERPRETATION

Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community.

FUNDING

Health and Medical Research Fund, Hong Kong.

Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China

Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0-2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0-14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): -27.8--9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4-28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5-18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.

Influenza

Annual seasonal influenza epidemics of variable severity caused by influenza A and B virus infections result in substantial disease burden worldwide. Seasonal influenza virus circulation declined markedly in 2020-21 after SARS-CoV-2 emerged but increased in 2021-22. Most people with influenza have abrupt onset of respiratory symptoms and myalgia with or without fever and recover within 1 week, but some can experience severe or fatal complications. Prevention is primarily by annual influenza vaccination, with efforts underway to develop new vaccines with improved effectiveness. Sporadic zoonotic infections with novel influenza A viruses of avian or swine origin continue to pose pandemic threats. In this Seminar, we discuss updates of key influenza issues for clinicians, in particular epidemiology, virology, and pathogenesis, diagnostic testing including multiplex assays that detect influenza viruses and SARS-CoV-2, complications, antiviral treatment, influenza vaccines, infection prevention, and non-pharmaceutical interventions, and highlight gaps in clinical management and priorities for clinical research.

Global variation in early epidemic growth rates and reproduction number of seasonal influenza

INTRODUCTION

Little is known about global variation in early epidemic growth rates and effective reproduction numbers (R_e) of seasonal influenza. We aimed to estimate global variation in R_e of influenza type A and B during a single period.

METHODS

Country influenza detection time series from September 2017 through January 2019 were obtained from an international database. Type A and B epidemics by country were selected based on R_e estimates for a five-week moving window advanced by week. Associations of R_e with absolute latitude, Human Development Index, percent of the population aged <15 years and percent living rurally in each country were assessed.

RESULTS

Time series were included for 119 of 169 available countries. There were 100 countries with influenza A and 79 with B epidemics. Median R_e for both influenza A and B epidemics was 1.23 (ranges: A 1.10, 1.60; B 1.06, 1.58). R_e of influenza B, but not A, was independently associated with absolute latitude, increasing by 0.022 (95% CI 0.002, 0.043) per 10 degrees.

CONCLUSIONS

R_e of influenza A and B were similar. Only R_e of influenza B was associated with country characteristics; increasing with distance from the equator. The approach may be suitable for continuous R_e surveillance.