Effects of air pollutants on occurrences of influenza-like illness and laboratory-confirmed influenza in Hefei, China

PM2.5 constituents and risk of influenza-like illness: A nationwide analysis in 289 Chinese cities

Discrepancies in fine particulate matter (PM2.5)-related influenza-like illness (ILI) risk have been widely observed in different studies in China, where the individual effect of PM2.5 constituents might be one of the important reasons. However, the associations between PM2.5 constituents and ILI risk in China have yet to be understood. We collected and aggregated weekly ILI cases in 289 Chinese cities during 2006-2019, and 47.8 million ILI cases were finally included in this study. Quasi-Poisson regression models and a random-effect meta-analysis were applied to estimate the impacts of PM2.5 and its constituents on ILI risk. Stratification analyses were also conducted by region, age group, season, and temperature and humidity quartiles. With per inter-quartile range increase in black carbon, ammonium, sulfate, PM2.5, nitrate and organic matter with a cumulative lag of 0-1 week, the overall ILI incidence would increase by 2.55 % (95 % CI: 1.71, 3.40), 2.32 % (1.33, 3.32), 2.19 % (1.29, 3.10), 2.19 % (1.25, 3.13), 2.15 % (1.08, 3.22) and 2.02 % (1.19, 2.85), respectively. The impacts tended to be much stronger in young- and middle-aged population, in North and East China, in winter, and in colder and drier conditions. PM2.5 and its major constituents all have significantly additive effects on ILI incidence. Specific preventive measures against individual constituent should be implemented for improving public health.

Effects and interaction of humidex and air pollution on influenza: A national analysis of 319 cities in mainland China

Influenza imposes a significant global health burden. This study investigates the effects of humidex and air pollution on influenza and their interactions, using multi-city surveillance data in China. Daily data on reported influenza cases, meteorological factors and air pollution from 319 cities in mainland China over the study period of 2014-2019 were collected. A two-stage analytical framework, comprising distributed lag non-linear model and multivariate meta-analysis, was employed to assess the associations between humidex, air pollution and influenza. Hierarchical and joint effect models were employed to examine their interaction. Nationally, an approximately L-shaped relationship between humidex and influenza was observed, with the highest relative risk (RR) of 2.603 (95 % confidence interval [CI]: 2.195-3.086). Per interquartile range increases in PM2.5, PM10, NO2, SO2, CO and O3 were associated with influenza risk increments of 0.035 (95 % CI: 0.010-0.061), 0.029 (95 % CI: 0.003-0.055), 0.191 (95 % CI: 0.152-0.231), 0.239 (95 % CI: 0.166-0.317), 0.038 (95 % CI: 0.001-0.076) and -0.171 (95 % CI: -0.238--0.099), respectively. A synergistic interaction effect was identified between low humidex and high air pollution as well as different air pollutants. Subgroup analyses indicated females and individuals aged 7-18 years old exhibited higher risks. Stronger effects were observed during winter season and in large cities. This study underscores the urgent need for tailored interventions to mitigate the health impacts in regions with concurrent low humidex and high air pollution.

Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China

BACKGROUND

Emerging evidence links air pollution to respiratory infections, yet systematic assessments in cold regions remain limited. This study evaluates the short-term effects of six major air pollutants on influenza-like illness (ILI) incidence in Changchun, Northeast China, with implications for air quality management and respiratory disease prevention.

METHODS

ILI surveillance data from Changchun were extracted from "China Influenza Surveillance Network" and the ambient air quality monitoring data of the city were collected from 2017 to 2022. A generalized additive model (GAM) with quasi-Poisson regression analysis was employed to quantify pollutant-ILI associations, adjusting for meteorological factors and temporal trends.

RESULTS

Among 84,010 ILI cases, immediate exposure effects were observed: each 10 µg/m³ increase in PM2.5 (ER = 1.00%, 95% CI: 0.63-1.37%), PM10 (0.90%, 0.57-1.24%), and O3 (1.05%, 0.44-1.67%) significantly elevated ILI risks. Young and middle-aged individuals (25-59 years old) exhibited the highest susceptibility to five pollutants (PM2.5, PM10, SO2, O3, and CO), and age subgroups under 15 years old exhibited susceptibility to NO2. Post-COVID-19 outbreak showed amplified effects across all pollutants (p < 0.05 vs. pre-outbreak). The effects of PM2.5, PM10, SO2 and O3 on ILI cases were greater in the cold season (October to March) (p < 0.05).

CONCLUSIONS

PM2.5, PM10, and O3 exposure significantly increases ILI risks in Changchun, particularly among young/middle-aged populations during cold seasons and post-pandemic periods. These findings underscore the urgency for real-time air quality alerts and targeted protection strategies during high-risk periods to mitigate respiratory health burdens.

A systematic review and meta-analysis on the association between PM2.5 exposure and increased influenza risk

Introduction

This systematic review and meta-analysis investigate the relationship between PM2.5 exposure and increased influenza risk (e.g., increased hospital admissions, confirmed influenza cases), synthesizing previous findings related to pollutant effects and exposure durations.

Methods

We searched PubMed, Web of Science, and Scopus for relevant studies up to 1 January 2010, following Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines for selection and analysis.

Results

Our review included 16 studies and found that a 10 μg/m3 increase in daily PM2.5 levels was associated with an increase of 1.5% rise in influenza risk (95% CI: 0.08%, 2.2%), with significant variations across different temperatures and lag times post-exposure. The analysis revealed heightened risks, with the most significant increases observed under extreme temperature conditions. Specifically, colder conditions were associated with a 14.2% increase in risk (RR = 14.2%, 95% CI: 3.5%, 24.9%), while warmer conditions showed the highest increase, with a 29.4% rise in risk (RR = 29.4%, 95% CI: 7.8%, 50.9%). Additionally, adults aged 18-64 were notably affected (RR = 4%, 95% CI: 2.9%, 5.1%).

Discussion

These results highlight PM2.5's potential to impair immune responses, increasing flu susceptibility. Despite clear evidence of PM2.5's impact on flu risk, gaps remain concerning exposure timing and climate effects. Future research should broaden to diverse regions and populations to deepen understanding and inform public health strategies.

Acute effects of air pollutants on influenza-like illness in Hangzhou, China

At present, with increasing awareness of the relationship between respiratory disease and air pollution, it is critical to assess the environmental risk factors for influenza. This study aimed to estimate the associations between ambient air pollution and the number of influenza-like illness (ILI) cases in Hangzhou, China, from 2015 to 2021. Weekly meteorological data, including average ambient temperature and average relative humidity, from December 29, 2014 to January 2, 2022 were collected from the Hangzhou Meteorological Service Center, and air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5), and PM with aerodynamic diameter ≤ 10 μm (PM10), were collected from National Ambient Air Quality Automatic Monitoring Stations in Hangzhou. The number of weekly ILI cases was collected from 15 influenza surveillance sentinel hospitals in Hangzhou. A generalized linear model (GLM) with quasi-Poisson regression was adopted to estimate the association between air pollution and ILI. After adjusting for the effects of average temperature, relative humidity, and seasonal and long-term trends, PM2.5, PM10, NO2, and SO2 were found to be significantly associated with the number of ILI cases, with relative risk (RR) values of 1.018 (95% CI 1.001-1.036), 1.016 (1.005-1.028), 1.063 (1.067-1.364), and 1.207 (1.067-1.364), respectively. In the two-pollutant model, putting PM2.5, PM10, NO2, or SO2 into the model separately with O3 produced results similar to those of the single-pollutant model. PM2.5, PM10, and NO2 have statistical significance in cold seasons, with the RR values of 1.020 (95% CI 1.001-1.038), 1.012 (95% CI 1.000-1.024), and 1.060 (95% CI 1.031-1.090), respectively. In summary, our study found that most air pollutants (PM10, PM2.5, NO2, and SO2) have a significant association with the risk of ILI cases in Hangzhou. These findings can serve as a reference for the formulation of effective protective measures.

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BACKGROUND

Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.

METHODS

In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013-2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.

RESULTS

Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013-2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20-50 mm), relative humidity (70-78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19-22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.

CONCLUSION

The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Seasonal patterns of influenza incidence and the influence of meteorological and air pollution factors in Thailand during 2009-2019

Influenza, an acute respiratory illness, remains a significant public health challenge, contributing substantially to morbidity and mortality worldwide. Its seasonal prevalence exhibits diversity across regions with distinct climates. This study aimed to explore the seasonal patterns of influenza and their correlation with meteorological and air pollution factors across six regions of Thailand. We conducted an analysis of monthly average temperature, relative humidity, precipitation, PM10, NO2, O3 concentrations, and influenza incidence data from 2009 to 2019 using wavelet analysis. Our findings reveal inconsistent biannual influenza prevalence patterns throughout the study period. The biannual pattern emerged during 2010-2012 across all regions but disappeared during 2013-2016. However, post-2016, the biannual cycles resurfaced, with peaks occurring during the rainy and winter seasons in most regions, except for the southern region. Wavelet coherence reveals that relative humidity can be the main influencing factor for influenza incidence over a one-year period in the northern, northeastern, central, Bangkok-metropolitan, and eastern regions, not in the southern region during 2010-2012 and 2016-2018. Similarly, precipitation can drive the influenza incidence at the same period for the northeastern, central, Bangkok-metropolitan, and eastern regions. PM10 concentration can influence influenza incidence over a half-year period in the northeastern, central, Bangkok-metropolitan, and eastern regions of Thailand during certain years. These results enhance our understanding of the temporal dynamics of influenza seasonality influenced by weather conditions and air pollution over the past 11 years. Such knowledge is invaluable for resource allocation in clinical settings and informing public health strategies, particularly in navigating Thailand's climatic complexities.

Differentiated impacts of short-term exposure to fine particulate constituents on infectious diseases in 507 cities of Chinese children and adolescents: A nationwide time-stratified case-crossover study from 2008 to 2021

This study assesses the association of short-term exposure to PM2.5 (particles ≤2.5 μm) on infectious diseases among Chinese children and adolescents. Analyzing data from 507 cities (2008-2021) on 42 diseases, it focuses on PM2.5 components (black carbon (BC), ammonium (NH4+), inorganic nitrate (NO3-), organic matter (OM), and sulfate (SO42-)). PM2.5 constituents significantly associated with incidence. Sulfate showed the most substantial effect, increasing all-cause infectious disease risk by 2.72 % per interquartile range (IQR) increase. It was followed by BC (2.04 % increase), OM (1.70 %), NO3- (1.67 %), and NH4+ (0.79 %). Specifically, sulfate and BC had pronounced impacts on respiratory diseases, with sulfate linked to a 10.73 % increase in seasonal influenza risk and NO3- to a 16.39 % rise in tuberculosis. Exposure to PM2.5 also marginally increased risks for gastrointestinal, enterovirus, and vectorborne diseases like dengue (7.46 % increase with SO42-). Sexually transmitted and bloodborne diseases saw an approximate 6.26 % increase in incidence, with specific constituents linked to diseases like hepatitis C and syphilis. The study concludes that managing PM2.5 levels could substantially reduce infectious disease incidence, particularly in China's middle-northern regions. It highlights the necessity of stringent air quality standards and targeted disease prevention, aligning PM2.5 management with international guidelines for public health protection.

Short-Term Exposure to Ambient Air Pollution and Influenza: A Multicity Study in China

BACKGROUND

Air pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.

OBJECTIVE

Our aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.

METHODS

Based on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), NO 2 , SO 2 , CO, and O 3 ], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.

RESULTS

A total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO 2 and NO 2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10   μ g / m 3 increase in the concentration at each of the lags of 1-7 d. Only NO 2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.

DISCUSSION

Increased exposure to ambient air pollutants, particularly NO 2 , is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.

Spatio-temporal evolution patterns of influenza incidence and its nonlinear spatial correlation with environmental pollutants in China

BACKGROUND

Currently, the influenza epidemic in China is at a high level and mixed with other respiratory diseases. Current studies focus on regional influenza and the impact of environmental pollutants on time series, and lack of overall studies on the national influenza epidemic and the nonlinear correlation between environmental pollutants and influenza. The unclear spatial and temporal evolution patterns of influenza as well as the unclear correlation effect between environmental pollutants and influenza epidemic have greatly hindered the prevention and treatment of influenza epidemic by relevant departments, resulting in unnecessary economic and human losses.

METHOD

This study used Chinese influenza incidence data for 2007-2017 released by the China CDC and air pollutant site monitoring data. Seasonal as well as inter monthly differences in influenza incidence across 31 provinces of China have been clarified through time series. Space-Time Cube model (STC) was used to investigate the spatio-temporal evolution of influenza incidence in 315 Chinese cities during 2007-2017. Then, based on the spatial heterogeneity of influenza incidence in China, Generalized additive model (GAM) was used to identify the correlation effect of environmental pollutants (PM2.5, PM10, CO, SO2, NO2, O3) and influenza incidence.

RESULT

The influenza incidence in China had obvious seasonal changes, with frequent outbreaks in winter and spring. The influenza incidence decreased significantly after March, with only sporadic outbreaks occurring in some areas. In the past 11 years, the influenza epidemic had gradually worsened, and the clustering of influenza had gradually expanded, which had become a serious public health problem. The correlation between environmental pollutants and influenza incidence was nonlinear. Generally, PM2.5, CO and NO2 were positively correlated at high concentrations, while PM10 and SO2 were negatively correlated. O3 was not strongly correlated with the influenza incidence.

CONCLUSION

The study found that the influenza epidemic in China was in a rapidly rising stage, and several regions had a multi-year outbreak trend and the hot spots continue to expand outward. The association between environmental pollutants and influenza incidence was nonlinear and spatially heterogeneous. Relevant departments should improve the monitoring of influenza epidemic, optimize the allocation of resources, reduce environmental pollution, and strengthen vaccination to effectively prevent the aggravation and spread of influenza epidemic in the high incidence season and areas.

Seasonal associations between air pollutants and influenza in 10 cities of southern China

Few studies have explored the associations between air pollutants and influenza across seasons, especially at large scales. This study aimed to evaluate seasons' modifying effects on associations between air pollutants and influenza from 10 cities of southern China. Through scientific evidence, it provides mitigation and adaptation strategies with practical guidelines to local health authorities and environmental protection agencies. Daily influenza incidence, meteorological, and air pollutants data from 2016 to 2019 were collected. Quasi-Poisson regression with a distributed lag nonlinear model was used to evaluate city-specific air pollutants and influenza associations. Meta-analysis was used to pool site-specific estimates. Attributable fractions (AFs) of influenza incidence due to pollutants were calculated. Stratified analyses were conducted by season, sex, and age. Overall, the cumulative relative risk (CRR) of influenza incidence for a 10-unit increase in PM_2.5, PM₁₀, SO₂, NO₂, and CO was 1.45 (95% CI: 1.25, 1.68), 1.53 (95% CI: 1.29, 1.81), 1.87 (95% CI: 1.40, 2.48), 1.74 (95% CI: 1.49, 2.03), and 1.19 (95% CI: 1.04, 1.36), respectively. Children aged 0-17 were more sensitive to air pollutants in spring and winter. PM₁₀ had greater effect on influenza than PM_2.5 in autumn, winter, and overall, lesser in spring. The overall AF due to PM_2.5, PM₁₀, SO₂, NO_2, and CO was 4.46% (95% eCI: 2.43%, 6.43%), 5.03% (95% eCI: 2.33%, 7.56%), 5.36% (95% eCI: 3.12%, 7.58%), 24.88% (95% eCI: 18.02%, 31.67%), and 23.22% (95% eCI: 17.56%, 28.61%), respectively. AF due to O₃ was 10.00% (95% eCI: 4.76%, 14.95%) and 3.65% (95% eCI: 0.50%, 6.59%) in spring and summer, respectively. The seasonal variations in the associations between air pollutants and influenza in southern China would provide evidence to service providers for tailored intervention, especially for vulnerable populations.

Critical review on emerging health effects associated with the indoor air quality and its sustainable management

Indoor air quality (IAQ) is one of the fundamental elements affecting people's health and well-being. Currently, there is a lack of awareness among people about the quantification, identification, and possible health effects of IAQ. Airborne pollutants such as volatile organic compounds (VOCs), particulate matter (PM), sulfur dioxide (SO2), carbon monoxide (CO), nitrous oxide (NO), polycyclic aromatic hydrocarbons (PAHs) microbial spores, pollen, allergens, etc. primarily contribute to IAQ deterioration. This review discusses the sources of major indoor air pollutants, molecular toxicity mechanisms, and their effects on cardiovascular, ocular, neurological, women, and foetal health. Additionally, contemporary strategies and sustainable methods for regulating and reducing pollutant concentrations are emphasized, and current initiatives to address and enhance IAQ are explored, along with their unique advantages and potentials. Due to their longer exposure times and particular physical characteristics, women and children are more at risk for poor indoor air quality. By triggering many toxicity mechanisms, including oxidative stress, DNA methylation, epigenetic modifications, and gene activation, indoor air pollution can cause a range of health issues. Low birth weight, acute lower respiratory tract infections, Sick building syndromes (SBS), and early death are more prevalent in exposed residents. On the other hand, the main causes of incapacity and early mortality are lung cancer, chronic obstructive pulmonary disease, and cardiovascular disorders. It's crucial to acknowledge anticipated research needs and implemented efficient interventions and policies to lower health hazards.

Climate and human health: a review of publication trends in the International Journal of Biometeorology

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China

Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), particulate matters ≤2.5 μm (PM_2.5), particulate matters ≤10 μm (PM₁₀), and ozone (O₃), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM_2.5) and NO₂ pollution was observed in cold months, with mean concentrations of 46.06 μg/m³ and 40.03 μg/m³, respectively. Time-series analysis indicated that high concentrations of NO₂, PM_2.5, PM₁₀, and O₃ were associated with more influenza outbreaks at short lag periods (0-5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m³ increment in concentrations of PM_2.5, PM₁₀, and O₃ at lag 04, was associated with a 2.103 (95%CI: 1.528-2.893), 1.618 (95%CI: 1.311-1.996), and 1.569 (95%CI: 1.214-2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m³ increase in NO₂ was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295-2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095-1.588). Nevertheless, barely significant effect of particulate matters (PM_2.5, PM₁₀) on Flu-B and SO₂ on both subtypes was detected. Further, the effect estimates of NO₂ increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO₂, O₃, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.

Impact of PM2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study

Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM_2.5 was the highest (621 μg/m³), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM_2.5 (348 μg/m³), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O₃ concentration was 310 μg/m³, and the RR was 1.65(1.26,2.15), when O₃ concentration was 0 μg/m³. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.

Short-term effects of ambient particulate matter (PM1, PM2.5 and PM10) on influenza-like illness in Guangzhou, China

BACKGROUND

Particulate matter (PM) has been linked to respiratory infections in a growing body of evidence. Studies on the relationship between ILI (influenza-like illness) and PM₁ (particulate matter with aerodynamic diameter ≤1 μm) are, however, scarce. The purpose of this study was to investigate the effects of PM on ILI in Guangzhou, China.

METHODS

Daily ILI cases, air pollution records (PM₁, PM_2.5, PM₁₀ and gaseous pollutants), and metrological data between 2014 and 2019 were gathered from Guangzhou, China. To estimate the risk of ILI linked with exposure to PM pollutants, a quasi-Poisson regression was used. Additionally, subgroup analyses stratified by gender, age and season were carried out.

RESULTS

For each 10 μg/m³ increase of PM₁ and PM_2.5 over the past two days (lag01), and PM₁₀ over the past three days (lag02), the relative risks (RR) of ILI were 1.079 (95% confidence interval [CI]: 1.050, 1.109), 1.044 (95% CI: 1.027, 1.062) and 1.046 (95% CI: 1.032, 1.059), respectively. The estimated risks for men and women were substantially similar. The effects of PM pollutants between male and female were basically equivalent. People aged 15-24 years old were more susceptive to PM pollutants.

CONCLUSIONS

It implies that PM₁, PM_2.5 and PM₁₀ are all risk factors for ILI, the health impacts of PM pollutants vary by particle size. Reducing the concentration of PM₁ needs to be considered when generating a strategy to prevent ILI.

Impact of air pollutants on influenza-like illness outpatient visits under urbanization process in the sub-center of Beijing, China

Air pollutants can cause serious harm to human health and a variety of respiratory diseases. This study aimed to explore the associations between air pollutants and outpatient visits for influenza-like illness (ILI) under urbanization process in the sub-center of Beijing. The data of ILI in sub-center of Beijing from April 1, 2014 to December 31, 2020 were obtained from Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and holidays. A total of 322,559 patients with ILI were included. The results showed that in the early urbanization period, the effects of PM_2.5, PM₁₀, SO₂, O₃, and CO on lag0 day, and PM_2.5, PM₁₀, O₃, and CO on lag1 day were not significant. In the later urbanization period, AQI and the concentrations of PM_2.5, PM₁₀, SO₂, NO₂ and CO on lag1 day were all significantly associated with an increased risk of outpatient visits for ILI, which increased by 0.34% (95%CI 0.23%, 0.45%), 0.42% (95%CI 0.29%, 0.56%), 0.44% (95%CI 0.33%, 0.55%), 0.36% (95%CI 0.24%, 0.49%), 0.91% (95%CI 0.62%, 1.21%) and 0.38% (95%CI 0.26%, 0.49%). The concentration of O₃ on lag1 day was significantly associated with a decreased risk of outpatient visits for ILI, which decreased by 0.21% (95%CI 0.04%, 0.39%). We found that the urbanization process had significantly aggravated the impact of air pollutants on ILI outpatient visits. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants under urbanization process.

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.

Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis

BACKGROUND

Evidence concerning effects of air pollution on influenza-like illness (ILI) from multi-center is limited and little is known about how regional factors might modify this relationship.

METHODS

In this ecological study, ILI cases defined as outpatients with temperature ≥38 °C, accompanied by cough or sore throat, were collected from National Influenza Surveillance Network in China. We adopted generalized additive model with quasi-Poisson to estimate province-specific association between air pollution and ILI in 30 Chinese provinces during 2015-2019, after adjusting for time trend and meteorological factors. We then pooled province-specific association by using random-effect meta-analysis. Potential effect modifications of season and regional characteristics were explored.

FINDINGS

A total of 26, 004, 853 ILI cases and 777, 223, 877 hospital outpatients were collected. In general, effects of air pollutants were acute. An inter-quartile range increase of PM_2.5, SO₂, PM₁₀, NO₂ and CO at lag0, and O₃ at lag0-2 was associated with 3.08% (95% CI: 1.91%, 4.27%), 3.00% (1.86%, 4.16%), 6.46% (4.71%, 8.25%), 7.21% (5.73%, 8.71%), 4.37% (3.05%, 5.70%), and -9.26% (-11.32%, -7.14%) change of ILI at national level, respectively. Associations between air pollutants and ILI varied by season and regions, with higher effect estimates in cold season, eastern and central regions and provinces with more humid condition and larger population.

INTERPRETATION

This study indicated that most air pollutants increased the risk of ILI in China. Our findings might provide implications for the development of policies to protect public health from air pollution and influenza.

FUNDING

National Natural Science Foundation of China and Chongqing Health Commission Program.

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.

A comprehensive understanding of ambient particulate matter and its components on the adverse health effects based from epidemiological and laboratory evidence

The impacts of air pollution on public health have become a great concern worldwide. Ambient particulate matter (PM) is a major air pollution that comprises a heterogeneous mixture of different particle sizes and chemical components. The chemical composition and physicochemical properties of PM change with space and time, which may cause different impairments. However, the mechanisms of the adverse effects of PM on various systems have not been fully elucidated and systematically integrated. The Adverse Outcome Pathway (AOP) framework was used to comprehensively illustrate the molecular mechanism of adverse effects of PM and its components, so as to clarify the causal mechanistic relationships of PM-triggered toxicity on various systems. The main conclusions and new insights of the correlation between public health and PM were discussed, especially at low concentrations, which points out the direction for further research in the future. With the deepening of the study on its toxicity mechanism, it was found that PM can still induce adverse health effects with low-dose exposure. And the recommended Air Quality Guideline level of PM_2.5 was adjusted to 5 μg/m³ by World Health Organization, which meant that deeper and more complex mechanisms needed to be explored. Traditionally, oxidative stress, inflammation, autophagy and apoptosis were considered the main mechanisms of harmful effects of PM. However, recent studies have identified several emerging mechanisms involved in the toxicity of PM, including pyroptosis, ferroptosis and epigenetic modifications. This review summarized the comprehensive evidence on the health effects of PM and the chemical components of it, as well as the combined toxicity of PM with other air pollutants. Based on the AOP Wiki and the mechanisms of PM-induced toxicity at different levels, we first constructed the PM-related AOP frameworks on various systems.

Investigation of the Optimal Operating Position of an Air Cleaner in Terms of Indoor Air Quality in a Four-Bed Hospital Ward

The use of air cleaners indoors has increased with the increase in indoor activities driven by the COVID-19 outbreak. In this study, the indoor air quality was determined at the location of each patient's respirator in a four-bed hospital ward equipped with a ventilation system and curtains, by varying the position of one air cleaner. By operating the air cleaner alone without the ventilation system, it was confirmed that it is better to place the air cleaner close to the center of the ward, regardless of whether curtains are used. It was further identified that the farther away the air cleaner is from the center, the worse the age of air could be, compared to the case of operating it in the center. Moreover, the situation where the ventilation system and air cleaner were operated simultaneously in the hospital ward was considered. It was discovered that operating the air cleaner close to the ventilation inlets in the absence of curtains helps to improve the indoor air quality. Furthermore, it was found that the age of the air is generally low near the location where the air cleaner is operated in the presence of curtains. Selecting an optimal position for the air cleaner can improve the air quality at the location of each bed in a four-bed hospital ward.

Advances on the immunotoxicity of outdoor particulate matter: A focus on physical and chemical properties and respiratory defence mechanisms

Particulate matter (PM) is acknowledged to have multiple detrimental effects on human health. In this review, we report literature results on the possible link between outdoor PM and health outcomes with a focus on pulmonary infections and the mechanisms responsible for observed negative effects. PM physical and chemical properties, such as size and chemical composition, as well as major emission sources are described for a more comprehensive view about the role played by atmospheric PM in the observed adverse health effects; to this aim, major processes leading to the deposition of PM in the respiratory tract and how this can pave the way to the onset of pathologies are also presented. From the literature works here reviewed, two ways in which PM can threaten human health promoting respiratory infectious diseases are mostly taken into account. The first pathway is related to an enhanced susceptibility and here we will also report on molecular mechanisms in the lung immune system responsible for the augmented susceptibility to pathogens, such as the damage of mechanical defensive barriers, the alteration of the innate immune response, and the generation of oxidative stress. The second one deals with the relationship between infectious agents and PM; here we recall that viruses and bacteria (BioPM) are themselves part of atmospheric PM and are collected during sampling together with particles of different origin; so, data should be analysed with caution in order to avoid any false cause-effect relation. To face these issues a multidisciplinary approach is mandatory as also evident from the ongoing research about the mechanisms hypothesized for the SARS-CoV-2 airborne spreading, which is still controversial and claims for further investigation. Therefore, we preferred not to include papers dealing with SARS-CoV-2.

Limited evidence for the role of environmental factors in the unusual peak of influenza in Brisbane during the 2018-2019 Australian summer

OBJECTIVE

To explore the contribution of environmental factors in the unusual pattern of influenza activity observed in Brisbane, Australia during the summer of 2018-2019.

METHODS

Distributed lag nonlinear models (DLNMs) were used to estimate the effect of environmental factors on weekly influenza incidence in Brisbane. Next generation sequencing was then employed to analyze minor and majority variants in influenza strains isolated from Brisbane children during this period.

RESULTS

There were limited marked differences in the environmental factors observed in Brisbane between the 2018-2019 summer period and the same period of the proceeding years, with the exception of significant reduction in rainfall. DLNM showed that reduced rainfall in Brisbane (at levels consistent with the 2018-2019 period) correlated with a dramatic increase in the relative risk of influenza. Sulfur dioxide (SO₂) levels were also increased in the 2018-2019 period, although these levels did not correlate with an increased risk of influenza. Sequencing of a limited number of pediatric influenza virus strains isolated during the 2018-2019 showed numerous mutations within the viral HA.

CONCLUSIONS

Taken together, these data suggest a limited role for key environmental factors in the influenza activity observed in Brisbane, Australia during the summer of 2018-2019. One alternative explanation may that viral factors, in addition to other factors not studied herein, contributed to the unusual influenza season. Our findings provide fundamental information that may be beneficial to a better understanding of the seasonal trends of influenza virus.

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Background

Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza.

Methods

Daily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters.

Results

There were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM_2.5, PM₁₀, CO and O₃ on lag0 days, as well as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM_2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO₂ (RR = 0.892, 95% CI: 0.840–0.948) and CO (RR = 0.306, 95% CI: 0.153–0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O₃. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship.

Conclusions

We found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.

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

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

The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

Background

Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.

Methods

The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted.

Results

Overall, a 10 μg/m³ increment of O₃, PM_2.5, PM₁₀ and NO₂ could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution.

Conclusion

Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01744-6.

Cumulative Effects of Particulate Matter Pollution and Meteorological Variables on the Risk of Influenza-Like Illness

The cold season is usually accompanied by an increased incidence of respiratory infections and increased air pollution from combustion sources. As we are facing growing numbers of COVID-19 cases caused by the novel SARS-CoV-2 coronavirus, an understanding of the impact of air pollutants and meteorological variables on the incidence of respiratory infections is crucial. The incidence of influenza-like illness (ILI) can be used as a close proxy for the circulation of influenza viruses. Recently, SARS-CoV-2 has also been detected in patients with ILI. Using distributed lag nonlinear models, we analyzed the association between ILI, meteorological variables and particulate matter concentration in Bialystok, Poland, from 2013–2019. We found an exponential relationship between cumulative PM_2.5 pollution and the incidence of ILI, which remained significant after adjusting for air temperatures and a long-term trend. Pollution had the greatest effect during the same week, but the risk of ILI was increased for the four following weeks. The risk of ILI was also increased by low air temperatures, low absolute humidity, and high wind speed. Altogether, our results show that all measures implemented to decrease PM_2.5 concentrations would be beneficial to reduce the transmission of SARS-CoV-2 and other respiratory infections.

SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?

Exposure to air pollutants has been previously associated with respiratory viral infections, including influenza, measles, mumps, rhinovirus, and respiratory syncytial virus. Epidemiological studies have also suggested that air pollution exposure is associated with increased cases of SARS-CoV-2 infection and COVID-19-associated mortality, although the molecular mechanisms by which pollutant exposure affects viral infection and pathogenesis of COVID-19 remain unknown. In this review, we suggest potential molecular mechanisms that could account for this association. We have focused on the potential effect of exposure to nitrogen dioxide (NO2 ), ozone (O3 ), and particulate matter (PM) since there are studies investigating how exposure to these pollutants affects the life cycle of other viruses. We have concluded that pollutant exposure may affect different stages of the viral life cycle, including inhibition of mucociliary clearance, alteration of viral receptors and proteases required for entry, changes to antiviral interferon production and viral replication, changes in viral assembly mediated by autophagy, prevention of uptake by macrophages, and promotion of viral spread by increasing epithelial permeability. We believe that exposure to pollutants skews adaptive immune responses toward bacterial/allergic immune responses, as opposed to antiviral responses. Exposure to air pollutants could also predispose exposed populations toward developing COIVD-19-associated immunopathology, enhancing virus-induced tissue inflammation and damage.

Short-term effects of ambient air pollution on the incidence of influenza in Wuhan, China: A time-series analysis

BACKGROUND

Evidence suggests that air pollution is associated with many adverse health outcomes such as cardiovascular diseases (CVD), respiratory diseases, cancer, and birth defects. Yet few studies dig into the relationship between air pollution and airborne infectious diseases.

METHODS

Daily data on influenza incidence were obtained from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Data on air pollutants including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ground-level ozone (O₃), particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM_2.5), and PM with aerodynamic diameter ≤ 10 μm (PM₁₀) were retrieved from ten national air sampling stations located at Wuhan. We applied generalized additive model (GAM) to estimate the associations between air pollution and the risk of influenza in Wuhan, China during 2015-2017.

RESULTS

In the single-day lag model, the largest effect estimates were observed at lag 0. An increased relative risk (RR) of influenza was significantly associated with a 10 μg/m³ increase in SO₂ (RR: 1.099; 95% confidence interval [CI]: 1.011-1.195), NO₂ (RR: 1.039; 95% CI: 1.013-1.065), and O₃ (RR: 1.005; 95% CI: 0.994-1.016), respectively. In the multi-day lag model, concentrations of SO₂, NO₂, and O₃ were statistically significantly associated with the risk of influenza at lag 0-1. The seasonal analysis suggests that the influence of air pollution on influenza is greater in the cold season as compared in the warm season in the early lag days. The multi-pollutant model indicates that NO₂ may be a potential confounder for co-pollutants.

CONCLUSIONS

Our study shows that air pollution may be associated with the risk of influenza in a broad sense. Therefore, when formulating policies to deal with influenza outbreaks in the future, factors regarding air pollution should be taken into consideration.

Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration-response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37-1.43), 1.35 (1.32-1.37), 1.01 (1.00-1.02), 1.08 (1.07-1.10), 1.28 (1.27-1.29), and 1.26 (1.24-1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97-0.98), 0.96 (0.96-0.97), and 0.94 (0.92-0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration-response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

Particulate matter (PM10) enhances RNA virus infection through modulation of innate immune responses

Sensing of pathogens by specialized receptors is the hallmark of the innate immunity. Innate immune response also mounts a defense response against various allergens and pollutants including particulate matter present in the atmosphere. Air pollution has been included as the top threat to global health declared by WHO which aims to cover more than three billion people against health emergencies from 2019 to 2023. Particulate matter (PM), one of the major components of air pollution, is a significant risk factor for many human diseases and its adverse effects include morbidity and premature deaths throughout the world. Several clinical and epidemiological studies have identified a key link between the PM existence and the prevalence of respiratory and inflammatory disorders. However, the underlying molecular mechanism is not well understood. Here, we investigated the influence of air pollutant, PM10 (particles with aerodynamic diameter less than 10 μm) during RNA virus infections using Highly Pathogenic Avian Influenza (HPAI) - H5N1 virus. We thus characterized the transcriptomic profile of lung epithelial cell line, A549 treated with PM10 prior to H5N1infection, which is known to cause severe lung damage and respiratory disease. We found that PM10 enhances vulnerability (by cellular damage) and regulates virus infectivity to enhance overall pathogenic burden in the lung cells. Additionally, the transcriptomic profile highlights the connection of host factors related to various metabolic pathways and immune responses which were dysregulated during virus infection. Collectively, our findings suggest a strong link between the prevalence of respiratory illness and its association with the air quality.

Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R₀) was calculated with the maximum likelihood "removal" method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R₀ was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R₀ and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R₀, while nitrogen dioxide (NO₂), 24-h average concentration of nitrogen dioxide (NO₂_24 h) and daily maximum temperature were inversely correlated to R₀, with coefficients of 0.644, 0.661, -0.636, -0.657, -0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R₀, including mean/maximum/minimum air pressure and mean wind speed, with coefficients of -0.697, -0.697, -0.697 and -0.841, respectively. There was no correlation with R₀ and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.

Understanding the knowledge gaps between air pollution controls and health impacts including pathogen epidemic

Sustainable development calls for a blue sky with quality air. Encouragingly, the current mass reduction-oriented pollution control is making substantial achievements, as the data from Chinese Environmental Monitoring Stations show a significant drop in the annual average concentrations of particulate matters (i.e., PM₁₀ and PM_2.5) and SO₂. But many challenges and knowledge gaps are still confronted nowadays. On one hand, long-term health impacts of fine air particles have to be closely probed through both epidemiological and laboratory studies, and the toxic effects owing to the interactions between particles and associated chemical pollutants should be differentially teased out. On the other hand, due to sole mass control, there are significant changes of overall pollutant fingerprint, such as the increase of ground-level ozone concentration, which should be taken into account for altered health effects relative to the past. Moreover, the interplays with air pollutants and air-borne pathogens should be scrutinized in more details. In other words, it is worth investigating likely spread of pathogens (even for SARS-CoV-2) with aid of aerosols. Here, we recapitulate the current knowledge gaps between air pollution controls and health impacts including pathogen epidemic, and we also propose future research directions to support policy making in balance mass control and health impacts.

The correlation between atmospheric visibility and influenza in Wuxi city, China

Influenza is an acute respiratory infectious disease that poses a threat to public health. We assessed the association between atmospheric visibility and influenza and influenza-like illness (ILI) in Wuxi city, China.Daily meteorological data, ILI activity, and influenza virus infection rates were collected between 31 December 2012 and 31 December 2017. A distributed lag non-linear model (DLNM) was used to analyze the exposure-lag-response of ILI and influenza activity and daily average visibility.A total of 12,800 cases were detected; 1046 cases (8.17%) were of Flu-A and 527 (4.12%) were of Flu-B infection. Our analysis suggested a non-linear relationship between atmospheric visibility and influenza: U-shaped for ILI, and L-shaped for Flu-A and Flu-B. Comparing low visibility (2.5 km) to ILI cases, the risk appeared between day 1 and day 2. For Flu-A, the risk appeared between days 5 and 9, whereas for Flu-B, the risk effect was much stronger and had a longer reaction delay, staying above zero until day 9. The protective effects of high visibility (14 km) on ILI and Flu-B occurred the same day or one day later. However, we found no association between high visibility and Flu-A.In conclusion, our study contributes novel evidence for the effects of atmospheric visibility on influenza. These findings are important for the development of influenza surveillance and early warning systems in Wuxi city.

Effects of air pollutants on the transmission and severity of respiratory viral infections

Particulate matter, sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) are among the outdoor air pollutants that are major factors in diseases, causing especially adverse respiratory effects in humans. On the other hand, the role of respiratory viruses in the pathogenesis of severe respiratory infections is an issue of great importance. The present literature review was aimed at assessing the potential effects of air pollutants on the transmission and severity of respiratory viral infections. We have reviewed the scientific literature regarding the association of outdoor air pollution and respiratory viruses on respiratory diseases. Evidence supports a clear association between air concentrations of some pollutants and human respiratory viruses interacting to adversely affect the respiratory system. Given the undoubted importance and topicality of the subject, we have paid special attention to the association between air pollutants and the transmission and severity of the effects caused by the coronavirus named SARS-CoV-2, which causes the COVID-19. Although to date, and by obvious reasons, the number of studies on this issue are still scarce, most results indicate that chronic exposure to air pollutants delays/complicates recovery of patients of COVID-19 and leads to more severe and lethal forms of this disease. This deserves immediate and in-depth experimental investigations.

Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

Epidemiological and genetic characteristics of influenza virus and the effects of air pollution on laboratory-confirmed influenza cases in Hulunbuir, China, from 2010 to 2019

Objective

A continuous survey on influenza was conducted in Hulunbuir, China from January 2010 to May 2019 to reveal epidemiological, microbiological and air pollutants associated with laboratory-confirmed influenza cases.

Methods

Influenza-like illness and severe acute respiratory infection subjects were enrolled from a sentinel hospital in Hulunbuir during the study period for epidemiological and virological investigation. The association between air pollutants and influenza-positivity rate was assessed by a generalised additive model.

Results

Of 4667 specimens, 550 (11.8%) were tested positive for influenza. The influenza-positivity was highest in the age groups of 5–14 years, 50–69 years and ⩾70 years. We found that the effect of particulate matter ⩽2.5 μm (PM_2.5) concentrations on the influenza-positivity rate was statistically significant, particularly on day lag-4 and lag-5. Genetic characterisations showed that (H1N1) pdm09 strains belonged to subclade 6B.1 and that influenza B isolates belonged to subclade 1A-3Del, with significant substitutions in the haemagglutinin and neuraminidase proteins compared with those in the WHO-recommended vaccine strains.

Conclusions

Elderly individuals and school-age children were at high risk for influenza infection. PM_2.5 concentrations showed significant effects on influenza-positivity rate in Hulunbuir, which could be considered in local influenza prevention strategies.

A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate

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A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate.

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Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO₂ emission and population density) are used in the model.

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The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China.

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A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase.

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The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results.

COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.

In this paper, infection rate of COVID-19 is modelled globally at a 0.5^∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO² and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries’ reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment.

The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D₃ for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40-60 ng/mL (100-150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D₃ doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D₃ for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40-60 ng/mL (100-150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D₃ doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

The short-term effects of air pollutants on influenza-like illness in Jinan, China

Background

There is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China.

Methods

Daily data on the concentration of particulate matters < 2.5 μm (PM 2.5), particulate matters < 10 μm (PM10), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated.

Results

A total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O₃, CO, SO₂ and NO₂ were 67.8 μg/m³, 131.76 μg/ m³, 109.85 μg/ m³, 1133 μg/ m³, 33.06 μg/ m³ and 44.38 μg/ m³, respectively. A 10 μg/ m³ increase in concentration of PM2.5, PM10, CO at lag0 and SO₂ at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083–1.0192), 1.0074 (95% CI: 1.0041–1.0107), 1.0288 (95% CI: 1.0127–1.0451), and 1.0008 (95% CI: 1.0003–1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787–0.9939), and no significant association was observed with NO₂, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25–59, 5–14 and 0–4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO₂; People aged ≥60 year, 5–14 and 0–4 were found to be significantly negative associations with O₃.

Conclusion

Air pollutants, especially PM2.5, PM10, CO and SO₂, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.

The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study

BACKGROUND

Previous studies have estimated the association between meteorological factors and mumps outbreaks without assessing the influence of air pollution. In this research, we explored the effects of short-term exposure to air pollution on the incidence of mumps.

METHODS

Our time-series analysis was conducted using data collected in Wuhan, China from 2015 to 2017. Daily number of mumps cases was obtained from Disease Reporting System in Hubei Provincial Center for Disease Control and Prevention. Data on air pollution was obtained from 10 national air quality monitoring stations, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ground-level ozone (O₃), particulate matter less than or equal to 10 μm in aerodynamic diameter (PM₁₀), and particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM_2.5). Daily meteorological data including temperature and relative humidity were obtained from Hubei Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to explore the association between the incidence of mumps and exposure to air pollution.

RESULTS

We observed that the effects of air pollutants were statistically significant mainly in two periods, lag 0 to lag 5 and lag 20 to lag 25, with the strongest effects appearing at lag 2 and lag 23. The cumulative effects were stronger than single-day lag effects. The stratified analysis showed the effect of pollutants during the hot season was stronger than that during the cold season, especially for NO₂ and SO₂.

CONCLUSIONS

We found that exposure to NO₂ and SO₂ was significantly associated with higher risk of developing mumps. Our findings could help deepen the understanding of how air pollution exposure affects the incidence of mumps.