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Li H, Li Y, Liu J, Liu J, Han J, Yang L. Vaccination reduces viral load and accelerates viral clearance in SARS-CoV-2 Delta variant-infected patients. Ann Med 2023; 55:419-427. [PMID: 36862600 PMCID: PMC9991402 DOI: 10.1080/07853890.2023.2166681] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
OBJECTIVE The purpose of this study was to investigate vaccine effectiveness in relieving symptoms in patients with the SARS-CoV-2 delta (B.1.617.2) variant. METHODS In this retrospective study, 31 patients did not receive any vaccine (non-vaccination, NV), 21 patients received 1-dose of inactivated vaccine (one-dose vaccination, OV), and 60 patients received at least 2-dose inactivated vaccine (two-dose vaccination, TV). The baseline data, clinical outcomes and vaccination information were collected and analyzed. RESULTS Patients in the OV group were younger than those in the other two groups (p = 0.001), but there was no significant difference in any of the other baseline data among the three groups. The TV group showed higher IgG antibody levels and cycle threshold values of SARS-CoV-2 than the NV and OV groups (p < 0.01), and time to peak viral load was shorter in the TV group (3.5 ± 2.3 d) than in the NV (4.8 ± 2.8 d) and OV groups (4.8 ± 2.9 d, p = 0.03). The patients in the TV group (18%) showed a higher recovery rate without drug therapy (p < 0.001). Viral clearance time and hospital stay were significantly shorter in the TV group than in the NV and OV groups (p < 0.01), and there were no significant differences in these parameters between the OV and NV groups, but IgG values were higher in the OV group (p = 0.025). No severe complications occurred in this study. CONCLUSIONS Our results suggest that 2-dose vaccination can reduce viral load and accelerate viral clearance in patients with the delta variant and enhance the protection afforded by IgG antibodies in vivo.Key MessagesIn this study, our results shows that two-dose vaccination can reduce viral loads and accelerate viral clearance, and two-dose vaccination enhance the protection of IgG antibodies in vivo; however, one-dose vaccination did not confer protective effectiveness.
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Affiliation(s)
- Hongxia Li
- Department of Medical Administration, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yanzi Li
- Department of Medical Administration, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Junhui Liu
- Department of Clinical Laboratory, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianlin Liu
- Department of Vascular Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianfeng Han
- Department of Administrative Office, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lin Yang
- Department of Vascular Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Administrative Office, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Tran HM, Tsai FJ, Lee YL, Chang JH, Chang LT, Chang TY, Chung KF, Kuo HP, Lee KY, Chuang KJ, Chuang HC. The impact of air pollution on respiratory diseases in an era of climate change: A review of the current evidence. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:166340. [PMID: 37591374 DOI: 10.1016/j.scitotenv.2023.166340] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/27/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
The impacts of climate change and air pollution on respiratory diseases present significant global health challenges. This review aims to investigate the effects of the interactions between these challenges focusing on respiratory diseases. Climate change is predicted to increase the frequency and intensity of extreme weather events amplifying air pollution levels and exacerbating respiratory diseases. Air pollution levels are projected to rise due to ongoing economic growth and population expansion in many areas worldwide, resulting in a greater burden of respiratory diseases. This is especially true among vulnerable populations like children, older adults, and those with pre-existing respiratory disorders. These challenges induce inflammation, create oxidative stress, and impair the immune system function of the lungs. Consequently, public health measures are required to mitigate the effects of climate change and air pollution on respiratory health. The review proposes that reducing greenhouse gas emissions contribute to slowing down climate change and lessening the severity of extreme weather events. Enhancing air quality through regulatory and technological innovations also helps reduce the morbidity of respiratory diseases. Moreover, policies and interventions aimed at improving healthcare access and social support can assist in decreasing the vulnerability of populations to the adverse health effects of air pollution and climate change. In conclusion, there is an urgent need for continuous research, establishment of policies, and public health efforts to tackle the complex and multi-dimensional challenges of climate change, air pollution, and respiratory health. Practical and comprehensive interventions can protect respiratory health and enhance public health outcomes for all.
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Affiliation(s)
- Huan Minh Tran
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan; Faculty of Public Health, Da Nang University of Medical Technology and Pharmacy, Viet Nam
| | - Feng-Jen Tsai
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yueh-Lun Lee
- Department of Microbiology and Immunology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jer-Hwa Chang
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Li-Te Chang
- Department of Environmental Engineering and Science, Feng Chia University, Taichung, Taiwan
| | - Ta-Yuan Chang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Kian Fan Chung
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Han-Pin Kuo
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Kai-Jen Chuang
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan; Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Hsiao-Chi Chuang
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; National Heart and Lung Institute, Imperial College London, London, UK; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; Cell Physiology and Molecular Image Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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Wagatsuma K. Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan. Pathogens 2023; 12:1307. [PMID: 38003771 PMCID: PMC10675148 DOI: 10.3390/pathogens12111307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.
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Affiliation(s)
- Keita Wagatsuma
- Division of International Health (Public Health), Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8510, Japan; ; Tel.: +81-25-227-2129
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
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McClymont H, Si X, Hu W. Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model. Heliyon 2023; 9:e13782. [PMID: 36845036 PMCID: PMC9941072 DOI: 10.1016/j.heliyon.2023.e13782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
Background Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.
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Wei Y, Dong Z, Fan W, Xu K, Tang S, Wang Y, Wu F. A narrative review on the role of temperature and humidity in COVID-19: Transmission, persistence, and epidemiological evidence. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:73-85. [PMID: 38013745 PMCID: PMC9181277 DOI: 10.1016/j.eehl.2022.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/30/2022] [Accepted: 04/28/2022] [Indexed: 12/11/2022]
Abstract
Since December 2019, the 2019 coronavirus disease (COVID-19) outbreak has become a global pandemic. Understanding the role of environmental conditions is important in impeding the spread of COVID-19. Given that airborne spread and contact transmission are considered the main pathways for the spread of COVID-19, this narrative review first summarized the role of temperature and humidity in the airborne trajectory of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Meanwhile, we reviewed the persistence of the virus in aerosols and on inert surfaces and summarized how the persistence of SARS-CoV-2 is affected by temperature and humidity. We also examined the existing epidemiological evidence and addressed the limitations of these epidemiological studies. Although uncertainty remains, more evidence may support the idea that high temperature is slightly and negatively associated with COVID-19 growth, while the conclusion for humidity is still conflicting. Nonetheless, the spread of COVID-19 appears to have been controlled primarily by government interventions rather than environmental factors.
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Affiliation(s)
- Yuan Wei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhaomin Dong
- School of Space and Environment, Beihang University, Beijing 102206, China
| | - Wenhong Fan
- School of Space and Environment, Beihang University, Beijing 102206, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China
| | - Kaiqiang Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ying Wang
- School of Space and Environment, Beihang University, Beijing 102206, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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