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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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Fu LT, Qu ZL, Zeng X, Li LZ, Lan R, Zhou Y. Spatiotemporal dynamics of confirmed case distribution during the COVID-19 pandemic in China: data comparison between 2020/04-2020/08 and 2021/04-2021/08. Sci Rep 2023; 13:11896. [PMID: 37482580 PMCID: PMC10363524 DOI: 10.1038/s41598-023-39139-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 07/20/2023] [Indexed: 07/25/2023] Open
Abstract
The COVID-19 pandemic across Chinese mainland was gradually stabilized at a low level with sporadic outbreaks, before the emergence of Omicron variant. Apart from non-pharmacological interventions (NPIs), COVID-19 vaccine has also been implemented to prevent and control the pandemic since early 2021. Although many aspects have been focused, the change of the spatiotemporal distribution of COVID-19 epidemic across Chinese mainland responding to the change of prevention and control measures were less concerned. Here, we collected the confirmed case data (including domestic cases and overseas imported cases) across Chinese mainland during both 2020/04-2020/08 and 2021/04-2021/08, and then conducted a preliminary data comparison on the spatiotemporal distribution of confirmed cases during the identical period between the two years. Distribution patterns were evaluated both qualitatively by classification method and quantitatively through employing coefficient of variation. Results revealed significant differences in the homogeneity of spatiotemporal distributions of imported or domestic cases between the two years, indicating that the important effect of the adjustment of prevention and control measures on the epidemic evolution. The findings here enriched our practical experience of COVID-19 prevention and control. And, the collected data here might be helpful for improving or verifying spatiotemporally dynamic models of infectious diseases.
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Affiliation(s)
- Lin-Tao Fu
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China.
| | - Zhong-Ling Qu
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Xin Zeng
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Liang-Zhi Li
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Run Lan
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Yu Zhou
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
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Wang P, Zheng X, Liu H. Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review. Front Public Health 2022; 10:1033432. [PMID: 36330112 PMCID: PMC9623320 DOI: 10.3389/fpubh.2022.1033432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.
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Affiliation(s)
- Peipei Wang
- School of Information Engineering, China University of Geosciences, Beijing, China
| | - Xinqi Zheng
- School of Information Engineering, China University of Geosciences, Beijing, China
- Technology Innovation Center for Territory Spatial Big-Data, MNR of China, Beijing, China
| | - Haiyan Liu
- School of Economic and Management, China University of Geosciences, Beijing, China
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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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Fabiano B, Hailwood M, Thomas P. Safety, environmental and risk management related to Covid-19. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2022; 160:397-399. [PMID: 35194340 PMCID: PMC8849899 DOI: 10.1016/j.psep.2022.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Affiliation(s)
- Bruno Fabiano
- University of Genoa, DICCA - Department of Civil, Chemical and Environmental Engineering, Genoa, Italy
| | - Mark Hailwood
- LUBW Landesanstalt für Umwelt Baden-Württemberg, Karlsruhe, Germany
| | - Philip Thomas
- University of Bristol, Faculty of Engineering, Bristol, United Kingdom
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Zoran MA, Savastru RS, Savastru DM, Tautan MN, Baschir LA, Tenciu DV. Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain. ENVIRONMENTAL RESEARCH 2022; 203:111849. [PMID: 34370990 PMCID: PMC8343379 DOI: 10.1016/j.envres.2021.111849] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 05/17/2023]
Abstract
While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.
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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
| | - Laurentiu A Baschir
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
| | - Daniel V Tenciu
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
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Marzouk M, Elshaboury N, Abdel-Latif A, Azab S. Deep learning model for forecasting COVID-19 outbreak in Egypt. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2021; 153:363-375. [PMID: 34334966 PMCID: PMC8305306 DOI: 10.1016/j.psep.2021.07.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 07/16/2021] [Accepted: 07/22/2021] [Indexed: 05/21/2023]
Abstract
The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.
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Affiliation(s)
- Mohamed Marzouk
- Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt
| | - Nehal Elshaboury
- Construction and Project Management Research Institute, Housing and Building National Research Center, Giza, Egypt
| | - Amr Abdel-Latif
- Project Management Division, Alsafa Real Estate Development Inc., Cairo, Egypt
| | - Shimaa Azab
- Environmental Planning and Development Center, Institute of National Planning, (INP), Cairo, Egypt
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Zoran MA, Savastru RS, Savastru DM, Tautan MN, Baschir LA, Tenciu DV. Exploring the linkage between seasonality of environmental factors and COVID-19 waves in Madrid, Spain. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2021; 152:583-600. [PMID: 36285289 PMCID: PMC9584827 DOI: 10.1016/j.psep.2021.06.043] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/14/2021] [Accepted: 06/27/2021] [Indexed: 05/07/2023]
Abstract
Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.
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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
| | - Laurentiu A Baschir
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
| | - Daniel V Tenciu
- IT Department, National Institute of R&D for Optoelectronics, Atomistilor Street 409, MG5, Magurele-Bucharest, 077125, Romania
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