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Motlogeloa O, Fitchett JM. Assessing the impact of climatic variability on acute respiratory diseases across diverse climatic zones in South Africa. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170661. [PMID: 38320698 DOI: 10.1016/j.scitotenv.2024.170661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 02/13/2024]
Abstract
Acute respiratory diseases are a significant public health concern in South Africa, with climatic variables such as temperature and rainfall being key influencers. This study investigates the associations between these variables and the prevalence of acute respiratory diseases in Johannesburg, Cape Town, and Gqeberha (Port Elizabeth), representing distinct climatic zones. Spearman's correlation analyses showed negative correlations in Johannesburg for respiratory disease claims with maximum temperature (r = -0.12, p < 0.0001) and mean temperature (r = -0.13, p < 0.0001), and a negative correlation with daily rainfall (r = -0.12, p < 0.0001). Cape Town demonstrated a negative correlation with maximum temperature (r = -0.18, p < 0.0001) and a positive correlation with rainfall (r = 0.08, p < 0.0001). Utilizing Distributed Lag Non-linear Models (DLNM), the study revealed that in Johannesburg, the relative risk (RR) of respiratory claims increases notably at temperatures below 12 °C, and again at a Tmax between 16 and 23 °C. The risk escalates further at >30 °C, although with a considerable error margin. For Cape Town, a stable level of moderate RR is seen from Tmax 15-24 °C, with a significant increase in RR and error margin above 30 °C. In Gqeberha, the DLNM results are less definitive, reflecting the city's moderate climate and year-round rainfall. The RR of acute respiratory diseases did not show clear patterns with temperature changes, with increasing error margins outside the 22 °C threshold. These findings emphasize the imperative for region-specific public health strategies that account for the complex, non-linear influences of climate on respiratory health. This detailed understanding of the climate-health nexus provides a robust basis for enhancing public health interventions and future research directed at reducing the impacts of climate factors.
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Affiliation(s)
- Ogone Motlogeloa
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Jennifer M Fitchett
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa.
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Si X, Wang L, Mengersen K, Hu W. Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland. Infect Dis Poverty 2024; 13:4. [PMID: 38200542 PMCID: PMC10777546 DOI: 10.1186/s40249-024-01173-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. METHODS Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. RESULTS All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. CONCLUSIONS Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.
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Affiliation(s)
- Xiaohan Si
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, 4059, Australia
| | - Liping Wang
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
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Chen Y, Hou W, Hou W, Dong J. Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China. BMC Public Health 2023; 23:1826. [PMID: 37726705 PMCID: PMC10510220 DOI: 10.1186/s12889-023-16712-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 09/06/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner. METHODS Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors. RESULTS The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100-120 μg/m3, PM2.5 at the level of 60-80 μg/m3, and NO2 at the level of 60 μg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models. CONCLUSIONS Exposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.
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Affiliation(s)
- Ye Chen
- Department of Infectious Disease, Shenyang Center for Disease Control and Prevention, 110100, Shenyang, Liaoning Province, People's Republic of China
- Shenyang Natural Focal Diseases Clinical Medical Research Center, 110100, Shenyang, Liaoning Province, People's Republic of China
| | - Weiming Hou
- Department of Occupational and Environmental Health, School of Public Health, China Medical University, No.77 Puhe Road, 110122, Shenyang, People's Republic of China
| | - Weiyu Hou
- The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, 030012, Taiyuan, People's Republic of China
| | - Jing Dong
- Department of Occupational and Environmental Health, School of Public Health, China Medical University, No.77 Puhe Road, 110122, Shenyang, People's Republic of China.
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, No.77 Puhe Road, 110122, Shenyang, People's Republic of China.
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Yang Y, Lian J, Jia X, Wang T, Fan J, Yang C, Wang Y, Bao J. Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study. BMC Public Health 2023; 23:1403. [PMID: 37474889 PMCID: PMC10360314 DOI: 10.1186/s12889-023-16240-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States. METHODS ILI, meteorological factors, and PM2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors. RESULTS A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022). CONCLUSION The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.
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Affiliation(s)
- Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Jiao Lian
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xiaocan Jia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Tianrun Wang
- School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Jingwen Fan
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Chaojun Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Yuping Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Junzhe Bao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China.
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Servadio JL, Thai PQ, Choisy M, Boni MF. Repeatability and timing of tropical influenza epidemics. PLoS Comput Biol 2023; 19:e1011317. [PMID: 37467254 PMCID: PMC10389745 DOI: 10.1371/journal.pcbi.1011317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/29/2023] [Indexed: 07/21/2023] Open
Abstract
Much of the world experiences influenza in yearly recurring seasons, particularly in temperate areas. These patterns can be considered repeatable if they occur predictably and consistently at the same time of year. In tropical areas, including southeast Asia, timing of influenza epidemics is less consistent, leading to a lack of consensus regarding whether influenza is repeatable. This study aimed to assess repeatability of influenza in Vietnam, with repeatability defined as seasonality that occurs at a consistent time of year with low variation. We developed a mathematical model incorporating parameters to represent periods of increased transmission and then fitted the model to data collected from sentinel hospitals throughout Vietnam as well as four temperate locations. We fitted the model for individual (sub)types of influenza as well as all combined influenza throughout northern, central, and southern Vietnam. Repeatability was evaluated through the variance of the timings of peak transmission. Model fits from Vietnam show high variance (sd = 64-179 days) in peak transmission timing, with peaks occurring at irregular intervals and throughout different times of year. Fits from temperate locations showed regular, annual epidemics in winter months, with low variance in peak timings (sd = 32-57 days). This suggests that influenza patterns are not repeatable or seasonal in Vietnam. Influenza prevention in Vietnam therefore cannot rely on anticipation of regularly occurring outbreaks.
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Affiliation(s)
- Joseph L Servadio
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Maciej F Boni
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Zhang R, Lai KY, Liu W, Liu Y, Cai W, Webster C, Luo L, Sarkar C. Association of climatic variables with risk of transmission of influenza in Guangzhou, China, 2005-2021. Int J Hyg Environ Health 2023; 252:114217. [PMID: 37418782 DOI: 10.1016/j.ijheh.2023.114217] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 06/16/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission. OBJECTIVE This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou. METHODS Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (Rt) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined. RESULTS Over the study period (2005-21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower Rt, while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall. CONCLUSION Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Wenfeng Cai
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Urban Systems Institute, The University of Hong Kong, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK; Urban Systems Institute, The University of Hong Kong, Hong Kong, China.
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Ma P, Zhou N, Wang X, Zhang Y, Tang X, Yang Y, Ma X, Wang S. Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China. ENVIRONMENTAL RESEARCH 2023; 219:115100. [PMID: 36565842 DOI: 10.1016/j.envres.2022.115100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/10/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
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 (PM2.5), 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 (PM2.5) 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, PM2.5, 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 PM2.5, 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 (PM2.5, 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.
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Affiliation(s)
- Pan Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China; Chengdu Plain Urban Meteorology and Environment Scientific Observation and Research Station of Sichuan Province, Chengdu, 610225, Sichuan, China.
| | - Ning Zhou
- The First People's Hospital of Lanzhou, Lanzhou, 730050, Gansu, China.
| | - Xinzi Wang
- Meteorological Bureau of Jinnan District, Tianjin, 300350, China.
| | - Ying Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China; Chengdu Plain Urban Meteorology and Environment Scientific Observation and Research Station of Sichuan Province, Chengdu, 610225, Sichuan, China.
| | - Xiaoxin Tang
- Shenzhen National Climate Observatory, Shenzhen, 518000, China.
| | - Yang Yang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Xiaolu Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Shigong Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
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Zhao Z, Zhai M, Li G, Gao X, Song W, Wang X, Ren H, Cui Y, Qiao Y, Ren J, Chen L, Qiu L. Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China. BMC Infect Dis 2023; 23:71. [PMID: 36747126 PMCID: PMC9901390 DOI: 10.1186/s12879-023-08025-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/23/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.
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Affiliation(s)
- Zhiyang Zhao
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Mengmeng Zhai
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Guohua Li
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012 Shanxi China
| | - Xuefen Gao
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012 Shanxi China
| | - Wenzhu Song
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Xuchun Wang
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Hao Ren
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Yu Cui
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Yuchao Qiao
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Jiahui Ren
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
| | - Limin Chen
- grid.464423.3Shanxi Provincial Peoples Hospital, Taiyuan, Shanxi China
| | - Lixia Qiu
- grid.263452.40000 0004 1798 4018Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi China
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Yin J, Liu T, Tang F, Chen D, Sun L, Song S, Zhang S, Wu J, Li Z, Xing W, Wang X, Ding G. Effects of ambient temperature on influenza-like illness: A multicity analysis in Shandong Province, China, 2014-2017. Front Public Health 2023; 10:1095436. [PMID: 36699880 PMCID: PMC9868675 DOI: 10.3389/fpubh.2022.1095436] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Background The associations between ambient temperature and influenza-like illness (ILI) have been investigated in previous studies. However, they have inconsistent results. The purpose of this study was to estimate the effect of ambient temperature on ILI in Shandong Province, China. Methods Weekly ILI surveillance and meteorological data over 2014-2017 of the Shandong Province were collected from the Shandong Center for Disease Control and Prevention and the China Meteorological Data Service Center, respectively. A distributed lag non-linear model was adopted to estimate the city-specific temperature-ILI relationships, which were used to pool the regional-level and provincial-level estimates through a multivariate meta-analysis. Results There were 911,743 ILI cases reported in the study area between 2014 and 2017. The risk of ILI increased with decreasing weekly ambient temperature at the provincial level, and the effect was statistically significant when the temperature was <-1.5°C (RR = 1.24, 95% CI: 1.00-1.54). We found that the relationship between temperature and ILI showed an L-shaped curve at the regional level, except for Southern Shandong (S-shaped). The risk of ILI was influenced by cold, with significant lags from 2.5 to 3 weeks, and no significant effect of heat on ILI was found. Conclusion Our findings confirm that low temperatures significantly increased the risk of ILI in the study area. In addition, the cold effect of ambient temperature may cause more risk of ILI than the hot effect. The findings have significant implications for developing strategies to control ILI and respond to climate change.
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Affiliation(s)
- Jia Yin
- Department of Epidemiology, School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China,Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Ti Liu
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Fang Tang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Dongzhen Chen
- Institute of Viral Disease Control and Prevention, Liaocheng Center for Disease Control and Prevention, Liaocheng, Shandong, China
| | - Lin Sun
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Shaoxia Song
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Shengyang Zhang
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Julong Wu
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Zhong Li
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Weijia Xing
- Department of Epidemiology, School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China,Weijia Xing ✉
| | - Xianjun Wang
- Institute for Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China,Xianjun Wang ✉
| | - Guoyong Ding
- Department of Epidemiology, School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China,*Correspondence: Guoyong Ding ✉
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10
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Zhou L, Yang H, Pan W, Xu J, Feng Y, Zhang W, Shao Z, Li T, Li S, Huang T, Wang C, Li W, Li M, He S, Zhan Y, Pan M. Association between meteorological factors and the epidemics of influenza (sub)types in a subtropical basin of Southwest China. Epidemics 2022; 41:100650. [PMID: 36375312 DOI: 10.1016/j.epidem.2022.100650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 10/13/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The effects of climatic conditions on the prevalence of individual influenza (sub)types are not well understood in the subtropics. This study aims to evaluate the associations between meteorological factors and seasonal epidemics of A(H3N2), A(H1N1)pdm09, and type B influenza viruses, as well as to estimate the interactions between climatic variables in a subtropical basin region. METHODS The seasonality of influenza (sub)types during 2010-2019 were characterized in Chengdu Plain Economic Zone, a densely populated and highly humid plain area in Sichuan Basin in subtropical Southwest China. Generalized additive models were adopted to assess the independent exposure-response relationship between meteorological variables and influenza prevalence. The interactions of meteorological variables were further estimated using bivariate response surface models and strata models. RESULTS Our analyses indicated that the temperature, relative humidity, and absolute humidity have exhibited a major influence on influenza infection in Chengdu Plain Economic Zone. Low temperature was shown to promote the prevalence of A(H1N1)pdm09 and type B in winter-spring days at all levels of relative humidity. High risk of A(H3N2) infections was observed at low temperature or high temperature, and at higher relative humidity. Moreover, absolute humidity decreased or increased influenza (sub)type infections within different ranges. CONCLUSIONS This study found different nonlinear relationships between meteorological factors and the seasonality of influenza (sub)types, as well as significant interactive effects between climatic variables, contributing to the research on the climate drivers of influenza prevalence in warm-humid basin regions in the subtropics.
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Affiliation(s)
- Linlin Zhou
- Department of Pathogenic Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Huiping Yang
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Wen Pan
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jianan Xu
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Yuliang Feng
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Weihua Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Zerui Shao
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Tianshu Li
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Shuang Li
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Ting Huang
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Chuang Wang
- Department of Medical Technology, West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - Wanyi Li
- Department of Pathogenic Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Mingyuan Li
- Department of Pathogenic Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Shusen He
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Ming Pan
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China.
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11
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Ng H, Li Y, Zhang T, Lu Y, Wong C, Ni J, Zhao Q. Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau. Heliyon 2022; 8:e11820. [DOI: 10.1016/j.heliyon.2022.e11820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/03/2022] [Accepted: 11/15/2022] [Indexed: 11/26/2022] Open
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12
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Li HL, Yang BY, Wang LJ, Liao K, Sun N, Liu YC, Ma RF, Yang XD. A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission. ENVIRONMENTAL RESEARCH 2022; 212:113297. [PMID: 35436453 PMCID: PMC9011904 DOI: 10.1016/j.envres.2022.113297] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/08/2022] [Accepted: 04/09/2022] [Indexed: 05/15/2023]
Abstract
Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.
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Affiliation(s)
- Hong-Li Li
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China
| | - Bai-Yu Yang
- College of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Li-Jing Wang
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China
| | - Ke Liao
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China
| | - Nan Sun
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China
| | - Yong-Chao Liu
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China; Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research at Ningbo University, Ningbo, 315211, China; Donghai Academy, Ningbo University, Ningbo, 315211, China
| | - Ren-Feng Ma
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China; Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research at Ningbo University, Ningbo, 315211, China; Donghai Academy, Ningbo University, Ningbo, 315211, China
| | - Xiao-Dong Yang
- College of Geography and Tourism Culture, Ningbo University, Ningbo, 315211, China; Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research at Ningbo University, Ningbo, 315211, China; Donghai Academy, Ningbo University, Ningbo, 315211, China.
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13
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Qi L, Liu T, Gao Y, Li Q, Tang W, Tian D, Su K, Xiong Y, Yang J, Feng L, Liu Q. Effect of absolute humidity on influenza activity across different climate regions in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:49373-49384. [PMID: 35218485 DOI: 10.1007/s11356-022-19279-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
Until now, we have no thorough understanding the role of absolute humidity on influenza activity, especially in tropical and subtropical areas. In this study, we investigated the relationship between absolute humidity and influenza activity in seven municipalities/provinces covering different climatic zones in China. Weekly meteorological data and influenza surveillance data in seven provinces/municipalities in China were collected from January 2012 to December 2019. A distributed lag nonlinear model was adopted to investigate the association between absolute humidity (AH) and influenza activity in each study site. Then, seven study sites were grouped into three regions: northern, intermediate, and southernmost regions. A multivariate meta-analysis was applied to estimate the exposure-lag-response associations in three regions. The province-specific or municipality-specific curves appeared to be nonlinear, and the association between influenza activity and AH varied across regions. In Beijing and Tianjin, located in northern China, the cumulative relative risks (RRs) increased as weekly average AHmean fell below 3.41 g/m3 and 6.62 g/m3. In Guangdong and Hainan, located in southernmost China, the risk of influenza activity increased with rising average AHmean with 16.74 g/m3 and 20.18 g/m3 as the break points. In Shanghai, Zhejiang, and Chongqing, the relationship between weekly average AHmean and influenza could be described as U-shaped curves, with the lowest RRs when weekly average AHmean was 11.95 g/m3, 11.94 g/m3, and 15.96 g/m3, respectively. Meta-analysis results showed the cumulative RRs significantly increased as weekly average AHmean fell below 3.86 g/m3 in the northern region, whereas significantly increased as weekly average AHmean rose above 18.46 g/m3 and 15.22 g/m3 in intermediate and southernmost regions, respectively. Both low and high AH might increase influenza risk in China, and the relationship varies geographically. Our findings suggest that public health policies for climate change adaptation should be tailored to the local climate conditions.
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Affiliation(s)
- Li Qi
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Tian Liu
- Jingzhou Center for Disease Control and Prevention, Hubei, 434000, China
| | - Yuan Gao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Qin Li
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Wenge Tang
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Dechao Tian
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, 518107, China
| | - Kun Su
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Yu Xiong
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Jun Yang
- School of Public Health, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
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14
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de Mangou A, Combe A, Coolen-Allou N, Miltgen G, Traversier N, Belmonte O, Vandroux D, Bohrer M, Cousty J, Caron M, Vidal C, Allyn J, Allou N. Severe community-acquired pneumonia in Reunion Island: Epidemiological, clinical, and microbiological characteristics, 2016–2018. PLoS One 2022; 17:e0267184. [PMID: 35427402 PMCID: PMC9012352 DOI: 10.1371/journal.pone.0267184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/04/2022] [Indexed: 12/17/2022] Open
Abstract
Purpose No data are available on severe community-acquired pneumonia (CAP) in the French overseas department of Reunion Island. This is unfortunate as the microorganisms responsible for the disease are likely to differ from those in temperate regions due to a tropical climate and proximity to other islands of the Indian Ocean region. The aim of this study was to assess the epidemiological, clinical, prognosis, and microbiological characteristics of patients with severe CAP in Reunion Island. Materials and methods This retrospective study evaluated all patients with CAP aged >18 years and hospitalized in one of the two intensive care units of Reunion Island between 2016 and 2018. Microorganisms were identified by culture from blood and respiratory samples, multiplex polymerase chain reaction from respiratory samples, urinary antigen tests, and serology. Results Over the study period, 573 cases of severe CAP were recorded, with a mean incidence of 22 per 100,000 person-years. The most frequently isolated microorganism was influenza (21.9%) followed by Streptococcus pneumoniae (12%). The influenza virus was detected in affected patients all year round. Twenty-four patients with severe CAP came from another island of the Indian Ocean region (4.2%), mainly Madagascar (>50%). Two of these patients presented with melioidosis and 4 were infected with Acinetobacter spp. Conclusions Our findings have major implications for the management of severe CAP in tropical regions. The most frequently isolated microorganism in patients with severe CAP in Reunion Island is influenza followed by S. pneumoniae. Physicians should be aware that influenza is the main cause of severe CAP in patients living in or returning from Reunion Island, where this virus circulates all year round.
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Affiliation(s)
- Axel de Mangou
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Agathe Combe
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Nathalie Coolen-Allou
- Respiratory Disease, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Guillaume Miltgen
- Microbiology, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
- UMR Processus Infectieux en Milieu Insulaire Tropical, CNRS 9192, INSERM U1187, IRD 249, Université de la Réunion, Saint-Denis, France
| | - Nicolas Traversier
- Microbiology, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Olivier Belmonte
- Microbiology, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - David Vandroux
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Michel Bohrer
- Department of Medical Information, Saint-Denis University Hospital, Saint-Denis, Reunion Island, France
| | - Julien Cousty
- Intensive Care Unit, Centre Hospitalier Universitaire Sud Réunion, Saint-Pierre, France
| | - Margot Caron
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Charles Vidal
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
| | - Jérôme Allyn
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
- Clinical Informatic Department, Saint-Denis University Hospital, Saint-Denis, Reunion Island, France
| | - Nicolas Allou
- Intensive Care Unit, Centre Hospitalier Universitaire Felix Guyon, Saint-Denis, France
- Clinical Informatic Department, Saint-Denis University Hospital, Saint-Denis, Reunion Island, France
- * E-mail:
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15
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Li Y, Wu J, Hao J, Dou Q, Xiang H, Liu S. Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:18116-18125. [PMID: 34677763 DOI: 10.1007/s11356-021-16948-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.
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Affiliation(s)
- Yanbing Li
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Jingtao Wu
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100005, China
| | - Jiayuan Hao
- Department of Biostatistics, Harvard University, Cambridge, MA, 02138, USA
| | - Qiujun Dou
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
| | - Hao Xiang
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China
| | - Suyang Liu
- School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan, 430071, China.
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16
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Ma P, Tang X, Zhang L, Wang X, Wang W, Zhang X, Wang S, Zhou N. Influenza A and B outbreaks differed in their associations with climate conditions in Shenzhen, China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:163-173. [PMID: 34693474 PMCID: PMC8542503 DOI: 10.1007/s00484-021-02204-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 05/20/2023]
Abstract
Under the variant climate conditions in the transitional regions between tropics and subtropics, the impacts of climate factors on influenza subtypes have rarely been evaluated. With the available influenza A (Flu-A) and influenza B (Flu-B) outbreak data in Shenzhen, China, which is an excellent example of a transitional marine climate, the associations of multiple climate variables with these outbreaks were explored in this study. Daily laboratory-confirmed influenza virus and climate data were collected from 2009 to 2015. Potential impacts of daily mean/maximum/minimum temperatures (T/Tmax/Tmin), relative humidity (RH), wind velocity (V), and diurnal temperature range (DTR) were analyzed using the distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Under its local climate partitions, Flu-A mainly prevailed in summer months (May to June), and a second peak appeared in early winter (December to January). Flu-B outbreaks usually occurred in transitional seasons, especially in autumn. Although low temperature caused an instant increase in both Flu-A and Flu-B risks, its effect could persist for up to 10 days for Flu-B and peak at 17 C (relative risk (RR) = 14.16, 95% CI: 7.46-26.88). For both subtypes, moderate-high temperature (28 C) had a significant but delayed effect on influenza, especially for Flu-A (RR = 26.20, 95% CI: 13.22-51.20). The Flu-A virus was sensitive to RH higher than 76%, while higher Flu-B risks were observed at both low (< 65%) and high (> 83%) humidity. Flu-A was active for a short term after exposure to large DTR (e.g., DTR = 10 C, RR = 12.45, 95% CI: 6.50-23.87), whereas Flu-B mainly circulated under stable temperatures. Although the overall wind speed in Shenzhen was low, moderate wind (2-3 m/s) was found to favor the outbreaks of both subtypes. This study revealed the thresholds of various climatic variables promoting influenza outbreaks, as well as the distinctions between the flu subtypes. These data can be helpful in predicting seasonal influenza outbreaks and minimizing the impacts, based on integrated forecast systems coupled with short-term climate models.
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Affiliation(s)
- Pan Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Xiaoxin Tang
- Shenzhen National Climate Observatory, Shenzhen Meteorological Bureau, Shenzhen, 518000, China
| | - Li Zhang
- Shenzhen National Climate Observatory, Shenzhen Meteorological Bureau, Shenzhen, 518000, China
| | - Xinzi Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Weimin Wang
- Shangluo Meteorological Bureau, Shangluo, 726000, Shanxi, China
| | - Xiaoling Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Shigong Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Ning Zhou
- The First Hospital of Lanzhou, Lanzhou, 730000, Gansu, China
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17
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Chen C, Zhang X, Jiang D, Yan D, Guan Z, Zhou Y, Liu X, Huang C, Ding C, Lan L, Huang X, Li L, Yang S. Associations between Temperature and Influenza Activity: A National Time Series Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010846. [PMID: 34682590 PMCID: PMC8535740 DOI: 10.3390/ijerph182010846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%–3.72%). The risk of influenza cases reached a peak at −5.35 °C with RRs of 2.14 (95%CI: 1.38–3.33) and the monthly AT in the range of −5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%–9.96%). The risk of A/H1N1 reached a peak at −3.14 °C with RRs of 4.88 (95%CI: 1.01–23.75) and the weekly AT in the range of −3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.
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Affiliation(s)
- Can Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaobao Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Daixi Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Danying Yan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Zhou Guan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Yuqing Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaoxiao Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Chenyang Huang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Lei Lan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xihui Huang
- Subject Teaching (English), College of Foreign Languages, Fujian Normal University, Fujian 350117, China;
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
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18
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Yap TF, Decker CJ, Preston DJ. Effect of daily temperature fluctuations on virus lifetime. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 789:148004. [PMID: 34323833 PMCID: PMC8570935 DOI: 10.1016/j.scitotenv.2021.148004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 05/25/2023]
Abstract
Epidemiological studies based on statistical methods indicate inverse correlations between virus lifetime and both (i) daily mean temperature and (ii) diurnal temperature range (DTR). While thermodynamic models have been used to predict the effect of constant-temperature surroundings on virus inactivation rate, the relationship between virus lifetime and DTR has not been explained using first principles. Here, we model the inactivation of viruses based on temperature-dependent chemical kinetics with a time-varying temperature profile to account for the daily mean temperature and DTR simultaneously. The exponential Arrhenius relationship governing the rate of virus inactivation causes fluctuations above the daily mean temperature during daytime to increase the instantaneous rate of inactivation by a much greater magnitude than the corresponding decrease in inactivation rate during nighttime. This asymmetric behavior results in shorter predicted virus lifetimes when considering DTR and consequently reveals a potential physical mechanism for the inverse correlation observed between the number of cases and DTR reported in statistical epidemiological studies. In light of the ongoing COVID-19 pandemic, a case study on the effect of daily mean temperature and DTR on the lifetime of SARS-CoV-2 was performed for the five most populous cities in the United States. In Los Angeles, where mean monthly temperature fluctuations are low (DTR ≈ 7 °C), accounting for DTR decreases predicted SARS-CoV-2 lifetimes by only 10%; conversely, accounting for DTR for a similar mean temperature but larger mean monthly temperature fluctuations in Phoenix (DTR ≈ 15 °C) decreases predicted lifetimes by 50%. The modeling framework presented here provides insight into the independent effects of mean temperature and DTR on virus lifetime, and a significant impact on transmission rate is expected, especially for viruses that pose a high risk of fomite-mediated transmission.
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Affiliation(s)
- Te Faye Yap
- Department of Mechanical Engineering, Rice University, 6100 Main St., Houston, TX 77005, United States of America
| | - Colter J Decker
- Department of Mechanical Engineering, Rice University, 6100 Main St., Houston, TX 77005, United States of America
| | - Daniel J Preston
- Department of Mechanical Engineering, Rice University, 6100 Main St., Houston, TX 77005, United States of America.
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19
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Sahoo MM. Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:40474-40495. [PMID: 33638789 PMCID: PMC7912974 DOI: 10.1007/s11356-021-12709-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/25/2021] [Indexed: 04/15/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.
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Affiliation(s)
- Mrunmayee Manjari Sahoo
- Domain of Environmental and Water Resources Engg, SCE, Lovely Professional University, Phagwara, 144411, India.
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20
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Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18137120. [PMID: 34281057 PMCID: PMC8297262 DOI: 10.3390/ijerph18137120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/27/2021] [Accepted: 06/29/2021] [Indexed: 01/04/2023]
Abstract
About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.
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21
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Wu Q, He J, Zhang WY, Zhao KF, Jin J, Yu JL, Chen QQ, Hou S, Zhu M, Xu Z, Pan HF. The contrasting relationships of relative humidity with influenza A and B in a humid subtropical region. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:36828-36836. [PMID: 33710490 DOI: 10.1007/s11356-021-13107-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/18/2021] [Indexed: 05/19/2023]
Abstract
Influenza is an acute respiratory disease that seriously threatens public health. The occurrence of influenza has been proved to be related to a variety of meteorological factors. However, less attention has been paid to the effect of relative humidity (RH) on different types of influenza, especially in subtropical regions. Daily data on laboratory-confirmed influenza cases, weather variables, and air pollutants in Hefei covering the 2014-2019 period were collected. The seasonality and trend of daily influenza cases were explored by the time series seasonal decomposition method. Generalized linear model was fitted in conjunction with distributed lag nonlinear model to quantify the associations of RH with influenza A and influenza B. Subgroup analyses were conducted by sex, age (0-4, 5-17, and ≥18 years), and season (cold and warm seasons). A total of 5238 influenza cases including 2847 influenza A cases and 2391 influenza B cases were recorded. The epidemic of influenza presented a distinct seasonal pattern, and the number of daily influenza cases increased steadily since 2016. High RH was related to an increased risk of influenza A (maximum RR = 1.683, 95%CI: 1.365-2.076), especially among males, females, and school-age children. Low RH was associated with an increased risk of influenza B (maximum RR = 1.252, 95%CI: 1.169-1.340). The contrasting relationships of RH with influenza A and B remained significant in cold seasons. High RH and low RH were significantly associated with the increased risk of influenza A and B, respectively. The findings of our study may provide clues for proposing new effective interventions.
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Affiliation(s)
- Qian Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China
| | - Jun He
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Wen-Yan Zhang
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Ke-Fu Zhao
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Jing Jin
- Hefei Center for Disease Control and Prevention, Anhui, China
| | - Jun-Ling Yu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Qing-Qing Chen
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
- Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Sai Hou
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Meng Zhu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia.
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, 81 Meishan Road, Hefei, Anhui, China.
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22
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Lau SY, Cheng W, Yu Z, Mohammad KN, Wang MH, Zee BC, Li X, Chong KC, Chen E. Independent association between meteorological factors, PM2.5, and seasonal influenza activity in Hangzhou, Zhejiang province, China. Influenza Other Respir Viruses 2021; 15:513-520. [PMID: 33342077 PMCID: PMC8189232 DOI: 10.1111/irv.12829] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/28/2020] [Accepted: 12/01/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Due to variations in climatic conditions, the effects of meteorological factors and PM2.5 on influenza activity, particularly in subtropical regions, vary in existing literature. In this study, we examined the relationship between influenza activity, meteorological parameters, and PM2.5 . METHODS A total of 20 165 laboratory-confirmed influenza cases in Hangzhou, Zhejiang province, were documented in our dataset and aggregated into weekly counts for downstream analysis. We employed a combination of the quasi-Poisson-generalized additive model and the distributed lag non-linear model to examine the relationship of interest, controlling for long-term trends, seasonal trends, and holidays. RESULTS A hockey-stick association was found between absolute humidity and the risk of influenza infections. The overall cumulative adjusted relative risk (ARR) was statistically significant when weekly mean absolute humidity was low (<10 µg/m3 ) and high (>17.5 µg/m3 ). A slightly higher ARR was observed when weekly mean temperature reached over 30.5°C. A statistically significantly higher ARR was observed when weekly mean relative humidity dropped below 67%. ARR increased statistically significantly with increasing rainfall. For PM2.5 , the ARR was marginally statistically insignificant. In brief, high temperature, wet and dry conditions, and heavy rainfall were the major risk factors associated with a higher risk of influenza infections. CONCLUSIONS The present study contributes additional knowledge to the understanding of the effects of various environmental factors on influenza activities. Our findings shall be useful and important for the development of influenza surveillance and early warning systems.
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Affiliation(s)
- Steven Yuk‐Fai Lau
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Wei Cheng
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
| | - Zhao Yu
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
| | - Kirran N. Mohammad
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Maggie Haitian Wang
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
| | - Benny Chung‐Ying Zee
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
| | - Xi Li
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
| | - Ka Chun Chong
- School of Public Health and Primary CareThe Chinese University of Hong KongHong KongChina
- Clinical Trials and Biostatistics LaboratoryShenzhen Research InstituteThe Chinese University of Hong KongHong KongChina
- Centre for Health Systems and Policy ResearchThe Chinese University of Hong KongHong KongChina
| | - Enfu Chen
- Zhejiang Province Centre for Disease Control and PreventionHangzhouChina
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23
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Qi L, Liu T, Gao Y, Tian D, Tang W, Li Q, Feng L, Liu Q. Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012-2019. PLoS One 2021; 16:e0246023. [PMID: 33534840 PMCID: PMC7857549 DOI: 10.1371/journal.pone.0246023] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 01/12/2021] [Indexed: 12/29/2022] Open
Abstract
Background The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. Methods Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. Results Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. Conclusions Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.
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Affiliation(s)
- Li Qi
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tian Liu
- Jingzhou Center for Disease Control and Prevention, Hubei, China
| | - Yuan Gao
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Dechao Tian
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Wenge Tang
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Qin Li
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- * E-mail: (QL); (LF)
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- * E-mail: (QL); (LF)
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24
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Weather Variability and COVID-19 Transmission: A Review of Recent Research. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18020396. [PMID: 33419216 PMCID: PMC7825623 DOI: 10.3390/ijerph18020396] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 12/15/2022]
Abstract
Weather and climate play a significant role in infectious disease transmission, through changes to transmission dynamics, host susceptibility and virus survival in the environment. Exploring the association of weather variables and COVID-19 transmission is vital in understanding the potential for seasonality and future outbreaks and developing early warning systems. Previous research examined the effects of weather on COVID-19, but the findings appeared inconsistent. This review aims to summarize the currently available literature on the association between weather and COVID-19 incidence and provide possible suggestions for developing weather-based early warning system for COVID-19 transmission. Studies eligible for inclusion used ecological methods to evaluate associations between weather (i.e., temperature, humidity, wind speed and rainfall) and COVID-19 transmission. The review showed that temperature was reported as significant in the greatest number of studies, with COVID-19 incidence increasing as temperature decreased and the highest incidence reported in the temperature range of 0–17 °C. Humidity was also significantly associated with COVID-19 incidence, though the reported results were mixed, with studies reporting positive and negative correlation. A significant interaction between humidity and temperature was also reported. Wind speed and rainfall results were not consistent across studies. Weather variables including temperature and humidity can contribute to increased transmission of COVID-19, particularly in winter conditions through increased host susceptibility and viability of the virus. While there is less indication of an association with wind speed and rainfall, these may contribute to behavioral changes that decrease exposure and risk of infection. Understanding the implications of associations with weather variables and seasonal variations for monitoring and control of future outbreaks is essential for early warning systems.
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Iqbal MM, Abid I, Hussain S, Shahzad N, Waqas MS, Iqbal MJ. The effects of regional climatic condition on the spread of COVID-19 at global scale. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 739:140101. [PMID: 32531684 PMCID: PMC7280824 DOI: 10.1016/j.scitotenv.2020.140101] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 05/17/2023]
Abstract
The pandemic outbreak of the novel coronavirus epidemic disease (COVID-19) is spreading like a diffusion-reaction in the world and almost 208 countries and territories are being affected around the globe. It became a sever health and socio-economic problem, while the world has no vaccine to combat this virus. This research aims to analyze the connection between the fast spread of COVID-19 and regional climate parameters over a global scale. In this research, we collected the data of COVID-19 cases from the time of 1st reported case to the 5th June 2020 in different affected countries and regional climatic parameters data from January 2020 to 5th June 2020. It was found that most of the countries located in the relatively lower temperature region show a rapid increase in the COVID-19 cases than the countries locating in the warmer climatic regions despite their better socio-economic conditions. A correlation between metrological parameters and COVID-19 cases was observed. Average daylight hours are correlated to total the COVID-19 cases with a coefficient of determination of 0.42, while average high-temperature shows a correlation of 0.59 and 0.42 with total COVID-19 cases and death cases respectively. The finding of the study will help international health organizations and local administrations to combat and well manage the spread of COVID-19.
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Affiliation(s)
| | - Irfan Abid
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Saddam Hussain
- Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan
| | - Naeem Shahzad
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Sohail Waqas
- Soil Conservation Group, Agriculture Department (Field Wing), Government of the Punjab, Pakistan
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26
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Zhang Y, Bambrick H, Mengersen K, Tong S, Feng L, Liu G, Xu A, Zhang L, Hu W. Association of weather variability with resurging pertussis infections among different age groups: A non-linear approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 719:137510. [PMID: 32135321 DOI: 10.1016/j.scitotenv.2020.137510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/14/2020] [Accepted: 02/21/2020] [Indexed: 06/10/2023]
Abstract
Pertussis has resurged in many countries over recent years, especially among adolescents and adults. This study assessed the effect of weather variability on resurging pertussis among different age groups in Jinan, China. Data on weekly pertussis notifications by age group and weather factors (mean temperature (MeanT), mean temperature standard deviation within a week (MeanT SD), diurnal temperature range (DTR) and relative humidity (RH)) were collected between 2013 and 2017. Distributed lag non-linear models (DLNMs) and regression tree models were used to examine the non-linear association between weather variability and pertussis infections. The 2-weeks cumulative relative risk (RR) of pertussis infections was 4.46 (95% confidence interval (CI): 2.33-9.51) in 0-4 age group, 6.25 (95% CI: 1.38-22.76) in 5-9 age group and 10.11 (95% CI: 2.83-39.07) in 10+ age group when MeanT was at 30.0 °C. MeanT SD (RR range in the three age groups: 2.82-5.83), DTR (RR range: 6.33-11.56) and RH (RR range: 2.02-7.43) also exert significant influence, with the highest risks at 10+ age group. Regression tree models showed the interactive effects of weather variability. The mean pertussis infections increased by over 1.7-fold in 0-4 years group when MeanT ≥14 °C, RH ≥57% and DTR ≥10 °C; by over 2.3-fold in 5-9 years group when MeanT ≥20 °C and MeanT SD ≥3 °C; by 2.0-fold in 10+ years group when MeanT ≥0.7 °C, DTR ≥8.3 °C and RH ≥74%. The study found significantly different associations between weather variability and pertussis infections by age group, and appeared to be stronger in 10+ years group. Continuing climate change, together with other risk factors such as low antibody levels among adolescents and adults, may facilitate pertussis resurgence. This supports previous suggestions of carefully reconsidering current vaccination programme to effectively curb the resurgence of pertussis.
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Affiliation(s)
- Yuzhou Zhang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Shilu Tong
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; School of Public Health and Institute of Environment and Human Health, Anhui Medical University, Hefei, Anhui, China; Shanghai Children's Medical Centre, Shanghai Jiao-Tong University, Shanghai, China
| | - Lei Feng
- Shandong Provincial Centre of Disease Control and Prevention, Jinan, China
| | - Guifang Liu
- Shandong Provincial Centre of Disease Control and Prevention, Jinan, China
| | - Aiqiang Xu
- Shandong Provincial Centre of Disease Control and Prevention, Jinan, China
| | - Li Zhang
- Shandong Provincial Centre of Disease Control and Prevention, Jinan, China.
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan. Sci Rep 2020; 10:7764. [PMID: 32385282 PMCID: PMC7211015 DOI: 10.1038/s41598-020-63712-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 03/20/2020] [Indexed: 11/24/2022] Open
Abstract
Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations.
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Zheng H, Zhou W, Zhang L, Li X, Cheng J, Ding Z, Xu Y, Hu W. Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17092983. [PMID: 32344848 PMCID: PMC7246778 DOI: 10.3390/ijerph17092983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/18/2020] [Accepted: 04/24/2020] [Indexed: 11/24/2022]
Abstract
Understanding water intake variation is crucial for assessing human exposure to water pollutants. The correlation between water intake and demographic factors warrants further exploration. A cross-sectional study was conducted to estimate urban water consumption and its associated factors among adults in Wuxi, China, in 2015. The water consumption information was obtained by a 24-h self-report diary over seven consecutive days. A classification and regression tree (CART) analysis was applied to detect how water consumption varied with the demographic variables. Finally, a total of 1188 adults (18–87 years old) were included. The results demonstrated that the median water consumption of the adults was 1525 mL/day in summer and 1217 mL/day in winter. The results of the CART analysis demonstrated that body mass index (BMI) and age were the leading factors that were associated with water consumption in summer and winter, respectively. The water consumption threshold of BMI for men differed from women (23 kg/m2 vs. 18 kg/m2) in summer, and the threshold of age for men was also different from women (43 years vs. 21 years) in winter. In conclusion, the findings are useful for accurately assessing human exposure to water pollutants and identifying the high-risk subgroups.
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Affiliation(s)
- Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China; (H.Z.); (Z.D.)
| | - Weijie Zhou
- Department of Environmental Health, Wuxi Center for Disease Control and Prevention, Wuxi 214023, China;
| | - Lan Zhang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China;
| | - Xiaobo Li
- School of Public Health, Southeast University, Nanjing 210009, China;
| | - Jian Cheng
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, Australia;
| | - Zhen Ding
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China; (H.Z.); (Z.D.)
| | - Yan Xu
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China; (H.Z.); (Z.D.)
- Correspondence: (Y.X.); (W.H.); Tel.: +86-25-8375-9520 (Y.X.); +61-7-3138-5724 (W.H.)
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, Australia;
- Correspondence: (Y.X.); (W.H.); Tel.: +86-25-8375-9520 (Y.X.); +61-7-3138-5724 (W.H.)
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29
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Liu H, Zhang Y, Tian Y, Zheng Y, Gou F, Yang X, He J, Liu X, Meng L, Hu W. Epidemic features of seasonal influenza transmission among eight different climate zones in Gansu, China. ENVIRONMENTAL RESEARCH 2020; 183:109189. [PMID: 32050127 DOI: 10.1016/j.envres.2020.109189] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 01/22/2020] [Accepted: 01/25/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUNDS Seasonal influenza remains epidemic globally with a substantial health burden. Understanding the transmission patterns and epidemic features of influenza may facilitate the improvement of preventive and control measures. This study aims to assess the epidemic features of influenza among different climate zones and identify high-risk zones across Gansu province, China. METHODS We collected weekly influenza cases at county-level between 1st January 2012 and 31st December 2016, as well as climate zones classification shapefile data from Köppen-Geiger climate map. We compared the epidemic features (Frequency index (α), Duration index (β) and Intensity index (γ)) of influenza among different climate zones. Spatial cluster analysis was used to examine the high-risk areas of transmission of influenza. RESULTS The distribution of cases existed significant differences among eight climate zones (F-test: 267.02, p < 0.05). The highest mean weekly incidence rate (per 100,000 population) was 0.59 in snow climate with dry winter and warm summer (Dwb). The primary (relative risk (RR): 3.61, p < 0.001) and secondary (RR: 2.45, p < 0.001) clusters were located in Dwb. The highest values of α, β and γ were 1.00, 261 and 154.38 in Dwb. The hot spots (high-high clusters) of the epidemic indices were detected in Dwb. CONCLUSIONS This study found the variability of epidemic features of influenza among eight climate zones. We highlight that Dwb was the high-risk zone where influenza clustered with the highest incidence rate and epidemic temporal indices. This provide further insight into potential improvement of preventive measures by climate zones to minimize the impact of epidemics.
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Affiliation(s)
- Haixia Liu
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Yuzhou Zhang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Yanjun Tian
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Yunhe Zheng
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Faxiang Gou
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Xiaoting Yang
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Jian He
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Xinfeng Liu
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China
| | - Lei Meng
- Division of Infectious Disease, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China.
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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