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Zhang H, Wang J, Liang Z, Wu Y. Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas. Heliyon 2024; 10:e31160. [PMID: 38778977 PMCID: PMC11109897 DOI: 10.1016/j.heliyon.2024.e31160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
Background In the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate. Methods To mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021. Results The linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively. Conclusion The influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.
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Affiliation(s)
- Hao Zhang
- School of Geography, Nanjing Normal University, Nanjing, Jiangsu, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, 210023, China
| | - Jian Wang
- School of Geography, Jiangsu Second Normal University, Nanjing, Jiangsu, 211200, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, 210023, China
| | - Zhong Liang
- School of Geography, Nanjing Normal University, Nanjing, Jiangsu, 210023, China
| | - Yuting Wu
- School of Geography, Nanjing Normal University, Nanjing, Jiangsu, 210023, China
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2
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Lo YTE, Mitchell DM, Gasparrini A. Compound mortality impacts from extreme temperatures and the COVID-19 pandemic. Nat Commun 2024; 15:4289. [PMID: 38782899 PMCID: PMC11116452 DOI: 10.1038/s41467-024-48207-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Extreme weather and coronavirus-type pandemics are both leading global health concerns. Until now, no study has quantified the compound health consequences of the co-occurrence of them. We estimate the mortality attributable to extreme heat and cold events, which dominate the UK health burden from weather hazards, in England and Wales in the period 2020-2022, during which the COVID-19 pandemic peaked in terms of mortality. We show that temperature-related mortality exceeded COVID-19 mortality by 8% in South West England. Combined, extreme temperatures and COVID-19 led to 19 (95% confidence interval: 16-22 in North West England) to 24 (95% confidence interval: 20-29 in Wales) excess deaths per 100,000 population during heatwaves, and 80 (95% confidence interval: 75-86 in Yorkshire and the Humber) to 127 (95% confidence interval: 123-132 in East of England) excess deaths per 100,000 population during cold snaps. These numbers are at least ~2 times higher than the previous decade. Society must increase preparedness for compound health crises such as extreme weather coinciding with pandemics.
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Affiliation(s)
- Y T Eunice Lo
- Cabot Institute for the Environment, University of Bristol, Bristol, UK.
- Elizabeth Blackwell Institute for Health Research, University of Bristol, Bristol, UK.
| | - Dann M Mitchell
- Cabot Institute for the Environment, University of Bristol, Bristol, UK
- School of Geographical Sciences, University of Bristol, Bristol, UK
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
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3
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Pan J, Villalan AK, Ni G, Wu R, Sui S, Wu X, Wang X. Assessing eco-geographic influences on COVID-19 transmission: a global analysis. Sci Rep 2024; 14:11728. [PMID: 38777817 PMCID: PMC11111805 DOI: 10.1038/s41598-024-62300-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.
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Affiliation(s)
- Jing Pan
- Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province, Heilongjiang Province, Harbin, 150040, People's Republic of China
- College of Wildlife and Protected Area, Northeast Forestry University, Heilongjiang Province, Harbin, 150040, People's Republic of China
| | - Arivizhivendhan Kannan Villalan
- Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province, Heilongjiang Province, Harbin, 150040, People's Republic of China
- College of Wildlife and Protected Area, Northeast Forestry University, Heilongjiang Province, Harbin, 150040, People's Republic of China
| | - Guanying Ni
- HaiXi Animal Disease Control Center, Qinghai Province, Delingha, 817099, People's Republic of China
| | - Renna Wu
- HaiXi Animal Disease Control Center, Qinghai Province, Delingha, 817099, People's Republic of China
| | - ShiFeng Sui
- Zhaoyuan Forest Resources Monitoring and Protection Service Center, Shandong Province, Zhaoyuan, 265400, People's Republic of China
| | - Xiaodong Wu
- China Animal Health and Epidemiology Center, Shandong Province, Qingdao, 266032, People's Republic of China.
| | - XiaoLong Wang
- Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province, Heilongjiang Province, Harbin, 150040, People's Republic of China.
- College of Wildlife and Protected Area, Northeast Forestry University, Heilongjiang Province, Harbin, 150040, People's Republic of China.
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4
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Tang X, Yang T, Yu D, Xiong H, Zhang S. Current insights and future perspectives of ultraviolet radiation (UV) exposure: Friends and foes to the skin and beyond the skin. ENVIRONMENT INTERNATIONAL 2024; 185:108535. [PMID: 38428192 DOI: 10.1016/j.envint.2024.108535] [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: 11/08/2023] [Revised: 01/25/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Ultraviolet (UV) radiation is ubiquitous in the environment, which has been classified as an established human carcinogen. As the largest and outermost organ of the body, direct exposure of skin to sunlight or UV radiation can result in sunburn, inflammation, photo-immunosuppression, photoaging and even skin cancers. To date, there are tactics to protect the skin by preventing UV radiation and reducing the amount of UV radiation to the skin. Nevertheless, deciphering the essential regulatory mechanisms may pave the way for therapeutic interventions against UV-induced skin disorders. Additionally, UV light is considered beneficial for specific skin-related conditions in medical UV therapy. Recent evidence indicates that the biological effects of UV exposure extend beyond the skin and include the treatment of inflammatory diseases, solid tumors and certain abnormal behaviors. This review mainly focuses on the effects of UV on the skin. Moreover, novel findings of the biological effects of UV in other organs and systems are also summarized. Nevertheless, the mechanisms through which UV affects the human organism remain to be fully elucidated to achieve a more comprehensive understanding of its biological effects.
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Affiliation(s)
- Xiaoyou Tang
- Medical College of Tibet University, Lasa 850000, China; Laboratory of Radiation Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Tingyi Yang
- Laboratory of Radiation Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Daojiang Yu
- Laboratory of Radiation Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu 610051, China
| | - Hai Xiong
- Medical College of Tibet University, Lasa 850000, China; West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
| | - Shuyu Zhang
- Medical College of Tibet University, Lasa 850000, China; Laboratory of Radiation Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu 610051, China; NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital), Mianyang 621099, China.
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Maureira L, Urquidi C, Sepúlveda-Peñaloza A, Soto-Marchant M, Matus P. Towards closing socio-economic status disparities in COVID-19 premature mortality: a nationwide and trend analysis in Chile. Int J Epidemiol 2024; 53:dyad183. [PMID: 38224273 DOI: 10.1093/ije/dyad183] [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: 08/04/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024] Open
Abstract
BACKGROUND Socio-economic status (SES) disparities in coronavirus disease 2019 (COVID-19) mortality have been reported but complete information and time trends are scarce. In this study, we analysed the years of life lost (YLL) due to COVID-19 premature mortality during the pandemic in Chile and its evolution according to SES and sex compared with a counterfactual scenario [cerebrovascular accidents (stroke)]. METHOD We used Chile's national mortality databases from 2020 to 2022. YLL and age-standardized YLL and mortality rates by sex and by epidemic waves were determined. The 346 communes were stratified into SES groups according to their poverty index quintile. Negative binomial regression models were used to test trends. RESULTS In >2 years of the pandemic, the COVID-19 YLL was 975 937, corresponding to 61 174 deaths. The YLL rate per 100 000 inhabitants was 1027 for males and 594 for females. There was a heterogeneous distribution of YLL rates and the regional level. Communes in the most advantaged SES quintile (Q5) had the highest YLL during the first wave compared with those in the lowest SES quintile (Q1) (P < 0.001) but the opposite was true during the second wave. COVID-19 YLL trends declined and differences between Q1 and Q2 vs Q5 converged from the second to the fourth waves (0.33 and 0.15, Ptrend < 0.001 and Ptrend = 0.024). YLL declined but differences persisted in stroke (-0.002, Ptrend = 0.979). CONCLUSIONS COVID-19 deaths resulted in a higher impact on premature death in Chile, especially in men, with a heterogeneous geographic distribution along the territory. SES and sex disparities in COVID-19 premature mortality had narrowed by the end of the pandemic.
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Affiliation(s)
- Lea Maureira
- Instituto de Ciencia e Innovación en Medicina, Universidad del Desarrollo, Santiago, Chile
| | - Cinthya Urquidi
- Departamento de Epidemiología y Estudios en Salud, Universidad de los Andes, Chile
| | | | - Mario Soto-Marchant
- Escuela de Tecnología Médica, Facultad de Salud y Odontología, Universidad Diego Portales, Santiago, Chile
| | - Patricia Matus
- Departamento de Epidemiología y Estudios en Salud, Universidad de los Andes, Chile
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Gupta M, Sharma A, Sharma DK, Nirola M, Dhungel P, Patel A, Singh H, Gupta A. Tracing the COVID-19 spread pattern in India through a GIS-based spatio-temporal analysis of interconnected clusters. Sci Rep 2024; 14:847. [PMID: 38191902 PMCID: PMC10774287 DOI: 10.1038/s41598-023-50933-4] [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: 06/05/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024] Open
Abstract
Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation's data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran's I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran's I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with [Formula: see text] of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.
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Affiliation(s)
- Mousumi Gupta
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India.
| | - Arpan Sharma
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Dhruva Kumar Sharma
- Department of Pharmacology, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Tadong Campus, Gangtok, 737102, India
| | - Madhab Nirola
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Prasanna Dhungel
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Ashok Patel
- Kusuma School of Biological Sciences, Indian Institute of Technology, Delhi, 110016, India
| | - Harpreet Singh
- Division of Biomedical Informatics, Indian Council of Medical Research, Delhi, 110029, India
| | - Amlan Gupta
- Department of Transfusion Medicine, Jay Prabha Medanta Super Speciality Hospital, Patna, 800020, India
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7
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Chen J, Chen S, Duan G, Zhang T, Zhao H, Wu Z, Yang H, Ding S. Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis. Epidemics 2023; 45:100719. [PMID: 37783112 DOI: 10.1016/j.epidem.2023.100719] [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: 03/09/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the "dynamic zero-COVID" policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions. METHODS Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period. RESULTS (1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature. (2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days. (3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90-1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4-6 days in more than 50% of mass outbreaks. (4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping. CONCLUSION Overall, our findings suggest the variation in the infection rates during the "dynamic zero-COVID" policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.
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Affiliation(s)
- Jingfeng Chen
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuaiyin Chen
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Guangcai Duan
- College of Public Health, Zhengzhou University, Zhengzhou, China.
| | - Teng Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Haitao Zhao
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Zhuoqing Wu
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
| | - Haiyan Yang
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Suying Ding
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Lorenz C, Libonati R, Belém LBC, Oliveira A, Chiaravalloti RM, Nunes AV, Batista EKL, Fernandes GW, Chiaravalloti-Neto F, Damasceno-Junior GA, Berlinck CN, Roque FO. Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil. Public Health 2023; 225:311-319. [PMID: 37972494 DOI: 10.1016/j.puhe.2023.10.032] [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: 05/23/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal. STUDY DESIGN Ecological retrospective study. METHODS We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables. RESULTS We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality. CONCLUSIONS The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.
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Affiliation(s)
- C Lorenz
- Instituto de Estudos Avançados, Universidade de São Paulo, R. do Anfiteatro, 513 - Butantã, São Paulo/SP, 05508-060, São Paulo, Brazil.
| | - R Libonati
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - L B C Belém
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - A Oliveira
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - R M Chiaravalloti
- University College London, Anthropology Department, 14 Taviton Street, WC1H 0BW, London, United Kingdom
| | - A V Nunes
- Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil
| | - E K L Batista
- National Research Center for Carnivores Conservation, Chico Mendes Institute for the Conservation of Biodiversity, Estrada Municipal Hisaichi Takebayashi 8600, Atibaia, 12952-011, São Paulo, Brazil
| | - G W Fernandes
- Evolutionary Ecology & Biodiversity (DGEE ICB) Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, 31270-901, Minas Gerais, Brazil
| | - F Chiaravalloti-Neto
- Departamento de Epidemiologia, Faculdade de Saúde Pública da Universidade de São Paulo, Av. Dr. Arnaldo 715, 01246-904, São Paulo/SP, Brazil
| | - G A Damasceno-Junior
- Laboratório de Botânica/Laboratório de Ecologia Vegetal, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil
| | - C N Berlinck
- National Research Center for Carnivores Conservation, Chico Mendes Institute for the Conservation of Biodiversity, Estrada Municipal Hisaichi Takebayashi 8600, Atibaia, 12952-011, São Paulo, Brazil
| | - F O Roque
- Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil; Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, 1 James Cook Dr, Douglas, Cairns, 4811, Queensland, Australia
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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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10
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Villatoro-García JA, López-Domínguez R, Martorell-Marugán J, Luna JDD, Lorente JA, Carmona-Sáez P. Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165487. [PMID: 37451463 DOI: 10.1016/j.scitotenv.2023.165487] [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: 04/11/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARS-CoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions.
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Affiliation(s)
- Juan Antonio Villatoro-García
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain
| | - Raúl López-Domínguez
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain
| | - Jordi Martorell-Marugán
- GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; Fundación para la Investigación Biosanitaria de Andalucía Oriental-Alejandro Otero (FIBAO), Spain
| | - Juan de Dios Luna
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - José Antonio Lorente
- GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; Department of Legal Medicine and Toxicology, Faculty of Medicine, University of Granada, PTS Granada, 18016 Granada, Spain
| | - Pedro Carmona-Sáez
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain.
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11
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Wagatsuma K, Koolhof IS, Saito R. Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan. Viruses 2023; 15:1914. [PMID: 37766320 PMCID: PMC10535838 DOI: 10.3390/v15091914] [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: 08/09/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era 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;
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
| | - Iain S. Koolhof
- College of Health and Medicine, School of Medicine, University of Tasmania, Hobart 7000, Australia;
| | - Reiko Saito
- Division of International Health (Public Health), Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8510, Japan;
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12
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Vicedo-Cabrera AM, de Schrijver E, Schumacher DL, Ragettli MS, Fischer EM, Seneviratne SI. The footprint of human-induced climate change on heat-related deaths in the summer of 2022 in Switzerland. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2023; 18:074037. [PMID: 38476980 PMCID: PMC7615730 DOI: 10.1088/1748-9326/ace0d0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Human-induced climate change is leading to an increase in the intensity and frequency of extreme weather events, which are severely affecting the health of the population. The exceptional heat during the summer of 2022 in Europe is an example, with record-breaking temperatures only below the infamous 2003 summer. High ambient temperatures are associated with many health outcomes, including premature mortality. However, there is limited quantitative evidence on the contribution of anthropogenic activities to the substantial heat-related mortality observed in recent times. Here we combined methods in climate epidemiology and attribution to quantify the heat-related mortality burden attributed to human-induced climate change in Switzerland during the summer of 2022. We first estimated heat-mortality association in each canton and age/sex population between 1990 and 2017 in a two-stage time-series analysis. We then calculated the mortality attributed to heat in the summer of 2022 using observed mortality, and compared it with the hypothetical heat-related burden that would have occurred in absence of human-induced climate change. This counterfactual scenario was derived by regressing the Swiss average temperature against global mean temperature in both observations and CMIP6 models. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151-1068] due to heat between June and August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133-644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in the 2022 summer in Switzerland.
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Affiliation(s)
- Ana M Vicedo-Cabrera
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Evan de Schrijver
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
- Graduate School of Health Sciences, University of Bern, Bern, Switzerland
| | | | - Martina S Ragettli
- Swiss Tropical and Public Health Institute (SwissTPH), Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Erich M Fischer
- Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
| | - Sonia I Seneviratne
- Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
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13
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Alaniz AJ, Vergara PM, Carvajal JG, Carvajal MA. Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27969-0. [PMID: 37310602 DOI: 10.1007/s11356-023-27969-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 05/24/2023] [Indexed: 06/14/2023]
Abstract
The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.
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Affiliation(s)
- Alberto J Alaniz
- Departamento de Ingeniería Geoespacial y Ambiental, Universidad de Santiago de Chile, Santiago, Chile.
- Centro de Formación Técnica del Medio ambiente, IDMA, Santiago, Chile.
- Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Departamento de Gestión Agraria, Facultad Tecnolִógica, Universidad de Santiago de Chile, Santiago, Chile.
| | - Pablo M Vergara
- Departamento de Gestión Agraria, Facultad Tecnolִógica, Universidad de Santiago de Chile, Santiago, Chile
| | - Jorge G Carvajal
- Departamento de Gestión Agraria, Facultad Tecnolִógica, Universidad de Santiago de Chile, Santiago, Chile
| | - Mario A Carvajal
- Departamento de Gestión Agraria, Facultad Tecnolִógica, Universidad de Santiago de Chile, Santiago, Chile
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14
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Yao XA, Crooks A, Jiang B, Krisp J, Liu X, Huang H. An overview of urban analytical approaches to combating the Covid-19 pandemic. ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE 2023; 50:1133-1143. [PMID: 38602958 PMCID: PMC10160829 DOI: 10.1177/23998083231174748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Affiliation(s)
- X Angela Yao
- Department of Geography, University of Georgia, Athens, GA, USA
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, NY, USA
| | - Bin Jiang
- Urban Governance and Design Thrust, The Hong Kong University of Science and Technology, Guangzhou, China
| | - Jukka Krisp
- Institute of Geography, Applied Geoinformatics, Augsburg University, Germany
| | - Xintao Liu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR
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15
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Santurtún A, Shaman J. Work accidents, climate change and COVID-19. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:162129. [PMID: 36773906 PMCID: PMC9911145 DOI: 10.1016/j.scitotenv.2023.162129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/17/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
The effects brought by climate change and the pandemic upon worker health and wellbeing are varied and necessitate the identification and implementation of improved strategic interventions. This review aims, firstly, to assess how climate change affects occupational accidents, focusing on the impacts of extreme air temperatures and natural disasters; and, secondly, to analyze the role of the pandemic in this context. Our results show that the manifestations of climate change affect workers physically while on the job, psychologically, and by modifying the work environment and conditions; all these factors can cause stress, in turn increasing the risk of suffering a work accident. There is no consensus on the impact of the COVID-19 pandemic on work accidents; however, an increase in adverse mental effects on workers in contact with the public (specifically in healthcare) has been described. It has also been shown that this strain affects the risk of suffering an accident. During the pandemic, many people began to work remotely, and what initially appeared to be a provisional situation has been made permanent or semi-permanent in some positions and companies. However, we found no studies evaluating the working conditions of those who telework. In relation to the combined impact of climate change and the pandemic on occupational health, only publications focusing on the synergistic effect of heat due to the obligation to wear COVID-19-specific PPE, either outdoors or in poorly acclimatized indoor environments, were found. It is essential that preventive services establish new measures, train workers, and determine new priorities for adapting working conditions to these altered circumstances.
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Affiliation(s)
- Ana Santurtún
- Unit of Legal Medicine, Department of Physiology and Pharmacology, University of Cantabria, IDIVAL, Santander, Spain.
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA; Columbia Climate School, Columbia University, New York, NY, USA
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16
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Zoran MA, Savastru RS, Savastru DM, Tautan MN. Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis. ENVIRONMENTAL RESEARCH 2023; 228:115907. [PMID: 37080275 PMCID: PMC10111861 DOI: 10.1016/j.envres.2023.115907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.
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Affiliation(s)
- Maria A Zoran
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania.
| | - Roxana S Savastru
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
| | - Dan M Savastru
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
| | - Marina N Tautan
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
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17
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Balboni E, Filippini T, Rothman KJ, Costanzini S, Bellino S, Pezzotti P, Brusaferro S, Ferrari F, Orsini N, Teggi S, Vinceti M. The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave. ENVIRONMENTAL RESEARCH 2023; 228:115796. [PMID: 37019296 PMCID: PMC10069087 DOI: 10.1016/j.envres.2023.115796] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 05/14/2023]
Abstract
The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.
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Affiliation(s)
- Erica Balboni
- Environmental, Genetic and Nutritional Epidemiology Research Center (CREAGEN), Section of Public Health, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Health Physics Unit, Modena Policlinico University Hospital, Modena, Italy
| | - Tommaso Filippini
- Environmental, Genetic and Nutritional Epidemiology Research Center (CREAGEN), Section of Public Health, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; School of Public Health, University of California Berkeley, Berkeley, CA, USA
| | - Kenneth J Rothman
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sofia Costanzini
- Department of Engineering 'Enzo Ferrari', University of Modena and Reggio Emilia, Modena, Italy
| | - Stefania Bellino
- Department of Infectious Diseases, Italian National Institute of Health, Rome, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Italian National Institute of Health, Rome, Italy
| | - Silvio Brusaferro
- Presidency, Italian National Institute of Health, Rome, Italy; Department of Medicine, University of Udine, Udine, Italy
| | | | - Nicola Orsini
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Sergio Teggi
- Department of Engineering 'Enzo Ferrari', University of Modena and Reggio Emilia, Modena, Italy
| | - Marco Vinceti
- Environmental, Genetic and Nutritional Epidemiology Research Center (CREAGEN), Section of Public Health, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
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18
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Scapini V, Torres S, Rubilar-Torrealba R. Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 322:120961. [PMID: 36621713 PMCID: PMC9813498 DOI: 10.1016/j.envpol.2022.120961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/11/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.
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19
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Colston JM, Hinson P, Nguyen NLH, Chen YT, Badr HS, Kerr GH, Gardner LM, Martin DN, Quispe AM, Schiaffino F, Kosek MN, Zaitchik BF. Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis. IJID REGIONS 2023; 6:29-41. [PMID: 36437857 PMCID: PMC9675637 DOI: 10.1016/j.ijregi.2022.11.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 06/09/2023]
Abstract
Background The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted. Findings Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance. Interpretation Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding NASA's Group on Earth Observations Work Programme (16-GEO16-0047).
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Affiliation(s)
- Josh M. Colston
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Patrick Hinson
- College of Arts and Sciences, University of Virginia, VA, USA
| | | | - Yen Ting Chen
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Hamada S. Badr
- Department of Earth and Planetary Sciences, Johns Hopkins Krieger School of Arts and Sciences, Baltimore, MD, 21218, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Lauren M. Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - David N. Martin
- Claude Moore Health Sciences Library, University of Virginia School of Medicine, VA, USA
| | | | - Francesca Schiaffino
- Faculty of Veterinary Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Division of Infectious Diseases and International Health and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Margaret N. Kosek
- Division of Infectious Diseases and International Health and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Benjamin F. Zaitchik
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
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20
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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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21
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Haga L, Ruuhela R, Auranen K, Lakkala K, Heikkilä A, Gregow H. Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13398. [PMID: 36293991 PMCID: PMC9603127 DOI: 10.3390/ijerph192013398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/06/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m3. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.
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Affiliation(s)
- Lisa Haga
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
| | - Reija Ruuhela
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
| | - Kari Auranen
- The Center of Statistics, University of Turku, 20500 Turku, Finland
| | - Kaisa Lakkala
- Finnish Meteorological Institute, Space and Earth Observation Centre, Earth Observation Research, P.O. Box 503, 00101 Helsinki, Finland
- Finnish Meteorological Institute, Climate Research Programme, Atmospheric Research Center of Eastern Finland, P.O. Box 503, 00101 Helsinki, Finland
| | - Anu Heikkilä
- Finnish Meteorological Institute, Climate Research Programme, Atmospheric Research Center of Eastern Finland, P.O. Box 503, 00101 Helsinki, Finland
| | - Hilppa Gregow
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
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