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Abril GA, Mateos AC, Tavera Busso I, Carreras HA. Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:115938-115949. [PMID: 37897573 DOI: 10.1007/s11356-023-30578-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/17/2023] [Indexed: 10/30/2023]
Abstract
Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.
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Affiliation(s)
- Gabriela Alejandra Abril
- IMBIV, Instituto Multidisciplinario de Biología Vegetal, Av. Vélez Sarsfield 1611, X5016 GCA Cordoba, Argentina.
| | - Ana Carolina Mateos
- IMBIV, Instituto Multidisciplinario de Biología Vegetal, Av. Vélez Sarsfield 1611, X5016 GCA Cordoba, Argentina
| | - Iván Tavera Busso
- IMBIV, Instituto Multidisciplinario de Biología Vegetal, Av. Vélez Sarsfield 1611, X5016 GCA Cordoba, Argentina
| | - Hebe Alejandra Carreras
- IMBIV, Instituto Multidisciplinario de Biología Vegetal, Av. Vélez Sarsfield 1611, X5016 GCA Cordoba, Argentina
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Zoran M, Savastru R, Savastru D, Tautan M, Tenciu D. Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest. Microorganisms 2023; 11:2531. [PMID: 37894189 PMCID: PMC10609195 DOI: 10.3390/microorganisms11102531] [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: 09/07/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023] Open
Abstract
The long-distance spreading and transport of airborne particulate matter (PM) of biogenic or chemical compounds, which are thought to be possible carriers of SARS-CoV-2 virions, can have a negative impact on the incidence and severity of COVID-19 viral disease. Considering the total Aerosol Optical Depth at 550 nm (AOD) as an atmospheric aerosol loading variable, inhalable fine PM with a diameter ≤2.5 µm (PM2.5) or coarse PM with a diameter ≤10 µm (PM10) during 26 February 2020-31 March 2022, and COVID-19's five waves in Romania, the current study investigates the impact of outdoor PM on the COVID-19 pandemic in Bucharest city. Through descriptive statistics analysis applied to average daily time series in situ and satellite data of PM2.5, PM10, and climate parameters, this study found decreased trends of PM2.5 and PM10 concentrations of 24.58% and 18.9%, respectively compared to the pre-pandemic period (2015-2019). Exposure to high levels of PM2.5 and PM10 particles was positively correlated with COVID-19 incidence and mortality. The derived average PM2.5/PM10 ratios during the entire pandemic period are relatively low (<0.44), indicating a dominance of coarse traffic-related particles' fraction. Significant reductions of the averaged AOD levels over Bucharest were recorded during the first and third waves of COVID-19 pandemic and their associated lockdowns (~28.2% and ~16.4%, respectively) compared to pre-pandemic period (2015-2019) average AOD levels. The findings of this research are important for decision-makers implementing COVID-19 safety controls and health measures during viral infections.
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Affiliation(s)
- Maria Zoran
- C Department, National Institute of R&D for Optoelectronics, 409 Atomistilor Street, MG5, 077125 Magurele, Romania; (R.S.); (D.S.); (M.T.); (D.T.)
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Feng B, Wang W, Zhou B, Zhou Y, Wang J, Liao F. Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 324:121418. [PMID: 36898647 PMCID: PMC9994533 DOI: 10.1016/j.envpol.2023.121418] [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: 11/16/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM2.5, O3, SO2, NO2, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO2 and SO2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.
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Affiliation(s)
- Benying Feng
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Wei Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Bo Zhou
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Ying Zhou
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Jinyu Wang
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Fang Liao
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China.
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Bhaskar A, Chandra J, Hashemi H, Butler K, Bennett L, Cellini J, Braun D, Dominici F. A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization. Annu Rev Public Health 2023; 44:1-20. [PMID: 36542771 DOI: 10.1146/annurev-publhealth-071521-120424] [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] [Indexed: 12/24/2022]
Abstract
Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.
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Affiliation(s)
- A Bhaskar
- Department of Government, Harvard University, Cambridge, Massachusetts, USA
| | - J Chandra
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - H Hashemi
- Environmental Systems Research Institute, Redlands, California, USA
| | - K Butler
- Environmental Systems Research Institute, Redlands, California, USA
| | - L Bennett
- Environmental Systems Research Institute, Redlands, California, USA
| | - Jacqueline Cellini
- Countway Library of Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA;
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA;
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Begou P, Kassomenos P. The ecosyndemic framework of the global environmental change and the COVID-19 pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159327. [PMID: 36220476 PMCID: PMC9547397 DOI: 10.1016/j.scitotenv.2022.159327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/03/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
The ecosyndemic theory combines the concept of 'synergy' with 'epidemic' and the term "eco" implies the role of the environmental changes. Each of the conditions enhances the negative impacts of the other in an additive way making our society more vulnerable and heightening individual risk factors. In this study, we analyze the mutually reinforcing links between the environment and health from the complexity angle of the ecosyndemic theory and propose the characterization of the COVID-19 pandemic as ecosyndemic. We use the term 'ecosyndemic' because the global environmental change contributes to local-scale, regional-scale and global-scale alterations of the Earth's systems. These changes have their root causes in the way that people interact with the physical, chemical, and biotic factors of the environment. These interactions disturb nature and the consequences have feedbacks in every living organism.
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Affiliation(s)
- Paraskevi Begou
- Laboratory of Meteorology and Climatology, Department of Physics, University of Ioannina, GR-45110 Ioannina, Greece.
| | - Pavlos Kassomenos
- Laboratory of Meteorology and Climatology, Department of Physics, University of Ioannina, GR-45110 Ioannina, Greece
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Hayet-Otero M, García-García F, Lee DJ, Martínez-Minaya J, España Yandiola PP, Urrutia Landa I, Nieves Ermecheo M, Quintana JM, Menéndez R, Torres A, Zalacain Jorge R, Arostegui I. Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques. PLoS One 2023; 18:e0284150. [PMID: 37053151 PMCID: PMC10101453 DOI: 10.1371/journal.pone.0284150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/26/2023] [Indexed: 04/14/2023] Open
Abstract
With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.
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Affiliation(s)
- Miren Hayet-Otero
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- Department of Electronic Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Basque Research and Technology Alliance (BRTA), TECNALIA, Derio, Basque Country, Spain
| | | | - Dae-Jin Lee
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- School of Science and Technology, IE University, Madrid, Madrid, Spain
| | - Joaquín Martínez-Minaya
- Department of Applied Statistics and Operational Research, and Quality, Universitat Politècnica de València (UPV), Valencia, Valencian Community, Spain
| | | | | | - Mónica Nieves Ermecheo
- BioCruces Bizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Research Unit, Galdakao-Usansolo University Hospital, Galdakao, Basque Country, Spain
| | - José María Quintana
- Research Unit, Galdakao-Usansolo University Hospital, Galdakao, Basque Country, Spain
| | - Rosario Menéndez
- Pneumology Department, La Fe University and Polytechnic Hospital, Valencia, Valencian Community, Spain
| | - Antoni Torres
- Pneumology Department, Hospital Clínic of Barcelona, Barcelona, Catalonia, Spain
| | | | - Inmaculada Arostegui
- Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain
- Department of Mathematics, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
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Zoran MA, Savastru RS, Savastru DM, Tautan MN. Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2022; 166:368-383. [PMID: 36034108 PMCID: PMC9391082 DOI: 10.1016/j.psep.2022.08.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.
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Key Words
- 222Rn
- 222Rn, Radon
- AOD, Total Aerosol Optical Depth at 550 nm
- Aerosol Optical Depth (AOD)
- CAMS, Copernicus Atmosphere Monitoring Service
- CO, Carbon monoxide
- COVID, 19 Coronavirus Disease 2019
- COVID-19 disease
- Climate variables
- DNC, Daily New COVID-19 positive cases
- DND, Daily New COVID-19 Deaths
- MERS, CoV Middle East respiratory syndrome coronavirus
- NO2, Nitrogen dioxide
- NOAA, National Oceanic and Atmospheric Administration U.S.A.
- O3, Ozone
- Outdoor air pollutants
- PBL, Planetary Boundary Layer height
- PM, Particulate Matter: PM1(1 µm), PM2.5 (2.5 µm) and PM10(10.0 µm) diameter
- RH, Air relative humidity
- SARS, CoV Severe Outdoor Respiratory Syndrome Coronavirus
- SARS, CoV-2 Severe Outdoor Respiratory Syndrome Coronavirus 2
- SI, Surface solar global irradiance
- SO2, Sulfur dioxide
- Synoptic meteorological circulation
- T, Air temperature at 2 m height
- p, Air pressure
- w, Wind speed intensity
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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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Abstract
There is an increasing need for the development of low-cost and highly sensitive gas sensors for environmental, commercial, and industrial applications in various areas, such as hazardous gas monitoring, safety, and emission control in combustion processes. Considering this, resistive-based gas sensors using metal oxide semiconductors (MOSs) have gained special attention owing to their high sensing performance, high stability, and low cost of synthesis and fabrication. The relatively low final costs of these gas sensors allow their commercialization; consequently, they are widely used and available at low prices. This review focuses on the important MOSs with different morphologies, including quantum dots, nanowires, nanofibers, nanotubes, hierarchical nanostructures, and other structures for the fabrication of resistive gas sensors.
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