The Influence of Gaseous Pollutants Concentration on Influenza Outbreak Risk DOI

晓璐 马

Open Journal of Natural Science, Journal Year: 2023, Volume and Issue: 11(06), P. 1003 - 1014

Published: Jan. 1, 2023

Language: Английский

Climate warming and influenza dynamics: the modulating effects of seasonal temperature increases on epidemic patterns DOI Creative Commons

Wenxi Ruan,

Yinglin Liang,

Zhaobin Sun

et al.

npj Climate and Atmospheric Science, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 25, 2025

Language: Английский

Citations

2

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

Baofa Jiang,

Michael Tong

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 490, P. 137865 - 137865

Published: March 6, 2025

Language: Английский

Citations

1

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 DOI Creative Commons

Yi Yin,

Miao Lai,

Sijia Zhou

et al.

Infectious Disease Modelling, Journal Year: 2023, Volume and Issue: 8(3), P. 822 - 831

Published: July 8, 2023

Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions China.We estimated time-varying reproduction number (Rt) and explored impact temperature relative humidity on Rt using generalized additive quasi-Poisson regression models combined with distribution lag non-linear model (DLNM). The effect interaction was explored. multiple random-meta analysis used to evaluate region-specific association. excess risk (ER) index defined investigate correlation between each factor modification seasonal regional characteristics.Low low contributed epidemics national level, while shapes merged cumulative plots were across regions. Compared that median temperature, RR (95%CI) northern southern 1.40(1.24,1.45) 1.20 (1.14,1.27), respectively, those high 1.10(1.03,1.17) 1.00 (0.95,1.04), respectively. There negative interactions (SI = 0.59, 95%CI: 0.57-0.61), 0.49, 0.17-0.80), 0.56,0.62). In general, increase change two factors, ER also gradually increased.Temperature have an China, there but heterogeneous among Meteorological may be considered predict trend epidemic.

Language: Английский

Citations

11

Increased population susceptibility to seasonal influenza during the COVID‐19 pandemic in China and the United States DOI Creative Commons
Qing Wang, Mengmeng Jia, Mingyue Jiang

et al.

Journal of Medical Virology, Journal Year: 2023, Volume and Issue: 95(10)

Published: Oct. 1, 2023

To the best of our knowledge, no previous study has quantitatively estimated dynamics and cumulative susceptibility to influenza infections after widespread lifting COVID-19 public health measures. We constructed an imitated stochastic susceptible-infected-removed model using particle-filtered Markov Chain Monte Carlo sampling estimate time-dependent reproduction number based on surveillance data in southern China, northern United States during 2022-2023 season. compared these estimates those from 2011 2019 seasons without strong social distancing interventions determine restrictions. Compared 2011-2019 a intervention with measures, season length was 45.0%, 47.1%, 57.1% shorter States, respectively, corresponding 140.1%, 74.8%, 50.9% increase scale infections, 60.3%, 72.9%, 45.1% population influenza. Large high-intensity epidemics occurred China 2022-2023. Population increased 2019-2022, especially China. recommend promoting vaccination, taking personal prevention actions at-risk populations, monitoring changes dynamic levels other respiratory prevent potential outbreaks coming

Language: Английский

Citations

11

From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China DOI Creative Commons
Yi‐Chang Zhao, Zhihua Sun,

Jiakai Li

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Abstract Background Carbapenem-resistant Gram-negative bacteria significantly threaten public health due to limited treatment options and high mortality rates. Understanding the factors influencing their detection resistance rates is crucial for effective interventions. Objective: This study aimed investigate carbapenem of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii in China identify associations with climate, agriculture, economy, diet. Method Data were sourced from CARSS, NBS, CMDC, covering 1435 hospitals. Descriptive statistics double fixed effect regression models analyzed associations, using SPSS, RStudio, StataMP, Python. Results From 2014 2021, bacterial counts increased 2,227,420 3,743,027, constituting 70.3–71.5%. coli (29.2–29.9%), pneumoniae (19.4–20.7%), aeruginosa (11.8–12.7%), (9.1–10.8%) most prevalent. Environmental data indicated significant geographic distributions, median humidity at 65%, temperature 15.75°C, annual rainfall 1164.50 mm. Regional disparities observed, showing a rate 1.40%, 18.55%, 6.10%, 55.30%. Factors like hospital environment food consumption affected rates, while GDP per capita impacted Detection correlated (coefficient 0.2007). Conclusion highlights regional carbapenem-resistant China, emphasizing need targeted interventions considering local climatic, economic, dietary conditions. profiles did not affect birth population growth.

Language: Английский

Citations

0

Short-term effects of ambient air pollution on influenza incidence in Chongqing, China: a time-series analysis DOI
Xinyue Wang,

Dianguo Xing,

Xinyun Zhou

et al.

International Journal of Environmental Health Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: Feb. 8, 2025

This study investigated the relationship between air pollution and influenza incidence in Chongqing from 2013 to 2022 using a generalized additive model (GAM), analyzing 199,712 cases. Subgroup analyses were conducted investigate impact of age, gender, season, COVID-19. Influenza was positively associated with PM2.5, PM10, SO2, NO2 CO, but negatively O3. SO2 had most effect. In single-day lag models, largest percentage changes at lag0 for each pollutant were: 2.930% 1.552% -0.637% O3, 0.516% 0.405% PM10. showed change lag11 (1.376%). multi-day peaked lag011–014. Stratified revealed children aged 0–14 years as particularly vulnerable during cold season COVID-19 period. The demonstrates that short-term lags cumulative effects exposure increase incidence, significant establishing response strategies.

Language: Английский

Citations

0

Global warming risks dehydrating and inflaming human airways DOI Creative Commons
David A. Edwards, Aurélie Edwards, Dan Li

et al.

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 17, 2025

Language: Английский

Citations

0

Exploration of the impact of air pollutants on the influenza epidemic after the emergence of COVID-19: based on Jiangsu Province, China (2020–2024) DOI Creative Commons
Chuansheng Zheng,

Xin Jiang,

Yi Yin

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: April 14, 2025

Background Non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic altered influenza transmission patterns, yet age-specific effects of air pollutants on dynamics remain unclear. Methods Utilizing surveillance data Jiangsu Province from 2020 to 2024, we integrated generalized additive quasi-Poisson regression model and distributed lag non-linear models (DLNM) quantify lagged exposure-response relationships between (NO 2 , SO PM 2.5 ) risk across young, middle-aged, older adult groups. Meteorological factors, including temperature humidity, as well implementation stages NPIs, were controlled in isolate impact transmission. Results The NO both showed significant positive all age effect is most young group (RR = 5.02, 95% CI: 4.69–5.37), while exhibited pronounced middle-aged groups 4.22, 3.36–5.30; RR 8.31, 5.77–11.96, respectively). elevated risks 1.99, 1.87–2.12) 1.45, 1.07–1.94) Interactions meteorological factors (temperature, humidity) statistically insignificant. Conclusions Air pollutant impacts are age-dependent: dominates younger populations, whereas disproportionately affects adults. These findings highlight age-related vulnerability pollution need for targeted public health strategies different population subgroups.

Language: Английский

Citations

0

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China DOI Creative Commons
Hansong Zhu, Feifei Qi, Xiaoying Wang

et al.

BMC Infectious Diseases, Journal Year: 2024, Volume and Issue: 24(1)

Published: Oct. 2, 2024

Language: Английский

Citations

2

Association between ambient temperature and influenza prevalence: A nationwide time-series analysis in 201 Chinese cities from 2013 to 2018 DOI Creative Commons
Yin Yi, Miao Lai,

Kailai Lu

et al.

Environment International, Journal Year: 2024, Volume and Issue: 189, P. 108783 - 108783

Published: May 28, 2024

Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well how temperature can be integrated into interventions. Meteorological, pollutant, data from 201 cities mainland between 2013 2018 were analyzed both to investigate relationship prevalence. We examined impact of on time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with distributed lag nonlinear model. Threshold temperatures determined for seven regions based early warning threshold serious outbreaks, set Rt = 1.2. A multivariate random-effects meta-analysis was employed assess region-specific associations. The excess risk (ER) index defined correlation temperature, modified seasonal regional characteristics. At level central, northern, northwestern, southern regions, found negatively correlated relative risk, whereas shapes curves eastern, southwestern, northeastern not defined. Low had an observable prevalence; effects high obvious. 1.2, reaching a outbreaks − 24.3 °C region, 16.6 northwestern 1℃ 10 other regions. study findings revealed that varying transmission trends across different China. By identifying thresholds more effective systems could tailored. These emphasize significance adaptation prevention control measures.

Language: Английский

Citations

1