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Grillenzoni C. Robust time-series analysis of the effects of environmental factors on the CoViD-19 pandemic in the area of Milan (Italy) in the years 2020-21. HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022; 4:100026. [PMID: 37520076 PMCID: PMC9458756 DOI: 10.1016/j.heha.2022.100026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/03/2022] [Accepted: 09/08/2022] [Indexed: 08/01/2023]
Abstract
The effects of environmental factors on the spread of the CoViD-19 pandemic have been widely debated in the scientific literature. The results are important for understanding the outbreak dynamics and for defining health measures of prevention and containment. Using multivariate autoregressive (AR) models and robust statistics of causality, this paper analyzes the effect of 19 time series (10 physical and 9 social) on 3 daily CoViD-19 series (infected, hospitalized, deaths) in the Milan area for about 16 months. Robust M-estimation shows the weak effect of climatic and pollution factors, while authority restrictions, people mobility, smart working and vaccination rate have a significant impact. In particular, the vaccination campaign is important for reducing hospitalizations and deaths.
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Hernandez Carballo I, Bakola M, Stuckler D. The impact of air pollution on COVID-19 incidence, severity, and mortality: A systematic review of studies in Europe and North America. ENVIRONMENTAL RESEARCH 2022; 215:114155. [PMID: 36030916 PMCID: PMC9420033 DOI: 10.1016/j.envres.2022.114155] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 05/29/2023]
Abstract
BACKGROUND Air pollution is speculated to increase the risks of COVID-19 spread, severity, and mortality. OBJECTIVES We systematically reviewed studies investigating the relationship between air pollution and COVID-19 cases, non-fatal severity, and mortality in North America and Europe. METHODS We searched PubMed, Web of Science, and Scopus for studies investigating the effects of harmful pollutants, including particulate matter with diameter ≤2.5 or 10 μm (PM2.5 or PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO), on COVID-19 cases, severity, and deaths in Europe and North America through to June 19, 2021. Articles were included if they quantitatively measured the relationship between exposure to air pollution and COVID-19 health outcomes. RESULTS From 2,482 articles screened, we included 116 studies reporting 355 separate pollutant-COVID-19 estimates. Approximately half of all evaluations on incidence were positive and significant associations (52.7%); for mortality the corresponding figure was similar (48.1%), while for non-fatal severity this figure was lower (41.2%). Longer-term exposure to pollutants appeared more likely to be positively associated with COVID-19 incidence (63.8%). PM2.5, PM10, O3, NO2, and CO were most strongly positively associated with COVID-19 incidence, while PM2.5 and NO2 with COVID-19 deaths. All studies were observational and most exhibited high risk of confounding and outcome measurement bias. DISCUSSION Air pollution may be associated with worse COVID-19 outcomes. Future research is needed to better test the air pollution-COVID-19 hypothesis, particularly using more robust study designs and COVID-19 measures that are less prone to measurement error and by considering co-pollutant interactions.
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Affiliation(s)
- Ireri Hernandez Carballo
- Department of Social and Political Sciences, Bocconi University, Milan, Lombardy, Italy; RFF-CMCC European Institute of Economics and the Environment, Centro Euro-Mediterraneo Sui Cambiamenti Climatici, Milan, Lombardy, Italy.
| | - Maria Bakola
- Research Unit for General Medicine and Primary Health Care, Faculty of Medicine, School of Health Science, University of Ioannina, Ioannina, Greece
| | - David Stuckler
- Department of Social and Political Sciences, Bocconi University, Milan, Lombardy, Italy; DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Lombardy, Italy
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Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces. Sci Rep 2022; 12:13317. [PMID: 35922645 PMCID: PMC9349267 DOI: 10.1038/s41598-022-17215-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/21/2022] [Indexed: 12/15/2022] Open
Abstract
This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO2, PM2.5, PM10, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO2, O3, PM2.5, PM10, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.
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Pivato A, Formenton G, Di Maria F, Baldovin T, Amoruso I, Bonato T, Mancini P, Bonanno Ferraro G, Veneri C, Iaconelli M, Bonadonna L, Vicenza T, La Rosa G, Suffredini E. SARS-CoV-2 in Atmospheric Particulate Matter: An Experimental Survey in the Province of Venice in Northern Italy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9462. [PMID: 35954818 PMCID: PMC9367860 DOI: 10.3390/ijerph19159462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Analysis of atmospheric particulate matter (PM) has been proposed for the environmental surveillance of SARS-CoV-2. The aim of this study was to increase the current knowledge about the occurrence of SARS-CoV-2 in atmospheric PM, introduce a dedicated sampling method, and perform a simultaneous assessment of human seasonal coronavirus 229E. Thirty-two PM samples were collected on quartz fiber filters and six on Teflon using a low- and high-volumetric rate sampler, respectively, adopting a novel procedure for optimized virus detection. Sampling was performed at different sites in the Venice area (Italy) between 21 February and 8 March 2020 (n = 16) and between 27 October and 25 November 2020 (n = 22). A total of 14 samples were positive for Coronavirus 229E, 11 of which were collected in October-November 2020 (11/22; positivity rate 50%) and 3 in February-March 2020 (3/16 samples, 19%). A total of 24 samples (63%) were positive for SARS-CoV-2. Most of the positive filters were collected in October-November 2020 (19/22; positivity rate, 86%), whereas the remaining five were collected in February-March 2020 at two distinct sites (5/16, 31%). These findings suggest that outdoor PM analysis could be a promising tool for environmental surveillance. The results report a low concentration of SARS-CoV-2 in outdoor air, supporting a scarce contribution to the spread of infection.
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Affiliation(s)
- Alberto Pivato
- Department of Civil, Environmental and Architectural Engineering (ICEA), University of Padua, 35131 Padova, Italy;
| | - Gianni Formenton
- Environmental Agency of Veneto Region (ARPAV), 30171 Mestre, Italy;
| | - Francesco Di Maria
- LAR Laboratory, Dipartimento di Ingegneria, University of Perugia, 06125 Perugia, Italy;
| | - Tatjana Baldovin
- Hygiene and Public Health Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, 35131 Padova, Italy;
| | - Irene Amoruso
- Hygiene and Public Health Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, 35131 Padova, Italy;
| | - Tiziano Bonato
- Società Estense Servizi Ambientali (S.E.S.A. S.p.A.), 35042 Este, Italy;
| | - Pamela Mancini
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Giusy Bonanno Ferraro
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Carolina Veneri
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Marcello Iaconelli
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Lucia Bonadonna
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Teresa Vicenza
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (T.V.); (E.S.)
| | - Giuseppina La Rosa
- Department of Environment and Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (P.M.); (G.B.F.); (C.V.); (M.I.); (L.B.); (G.L.R.)
| | - Elisabetta Suffredini
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (T.V.); (E.S.)
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Tateo F, Fiorino S, Peruzzo L, Zippi M, De Biase D, Lari F, Melucci D. Effects of environmental parameters and their interactions on the spreading of SARS-CoV-2 in North Italy under different social restrictions. A new approach based on multivariate analysis. ENVIRONMENTAL RESEARCH 2022; 210:112921. [PMID: 35150709 PMCID: PMC8828377 DOI: 10.1016/j.envres.2022.112921] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/13/2022] [Accepted: 02/06/2022] [Indexed: 02/07/2023]
Abstract
In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May-December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection ("free/summer period"); b) increasing incidence of disease, social restrictions and use of personal protections ("confined/autumn period"). The "hospitalized people in medical area wards/100000 residents" was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions. The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function.
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Affiliation(s)
- Fabio Tateo
- Institute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. Gradenigo, 6, 35131, Padova, Italy
| | - Sirio Fiorino
- Internal Medicine Unit, Budrio Hospital, Azienda USL, Via Benni, 44, 40054, Bologna, Italy
| | - Luca Peruzzo
- Institute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. Gradenigo, 6, 35131, Padova, Italy.
| | - Maddalena Zippi
- Unit of Gastroenterology and Digestive Endoscopy, Sandro Pertini Hospital, Via dei Monti Tiburtini 385, 00157, Rome, Italy
| | - Dario De Biase
- Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy
| | - Federico Lari
- Internal Medicine Unit, Budrio Hospital, Azienda USL, Via Benni, 44, 40054, Bologna, Italy
| | - Dora Melucci
- Department of Chemistry Ciamician, University of Bologna, Via Selmi, 2, 40126, Bologna, Italy
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Becchetti L, Beccari G, Conzo G, Conzo P, De Santis D, Salustri F. Particulate matter and COVID-19 excess deaths: Decomposing long-term exposure and short-term effects. ECOLOGICAL ECONOMICS : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR ECOLOGICAL ECONOMICS 2022; 194:107340. [PMID: 35017790 PMCID: PMC8739034 DOI: 10.1016/j.ecolecon.2022.107340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/18/2021] [Accepted: 01/04/2022] [Indexed: 05/12/2023]
Abstract
We investigate the time-varying effect of particulate matter (PM) on COVID-19 deaths in Italian municipalities. We find that the lagged moving averages of PM2.5 and PM10 are significantly related to higher excess deceases during the first wave of the disease, after controlling, among other factors, for time-varying mobility, regional and municipality fixed effects, the nonlinear contagion trend, and lockdown effects. Our findings are confirmed after accounting for potential endogeneity, heterogeneous pandemic dynamics, and spatial correlation through pooled and fixed-effect instrumental variable estimates using municipal and provincial data. In addition, we decompose the overall PM effect and find that both pre-COVID long-term exposure and short-term variation during the pandemic matter. In terms of magnitude, we observe that a 1 μg/m3 increase in PM2.5 can lead to up to 20% more deaths in Italian municipalities, which is equivalent to a 5.9% increase in mortality rate.
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Affiliation(s)
- Leonardo Becchetti
- University of Rome Tor Vergata, Department of Economics and Finance, Italy
| | - Gabriele Beccari
- University of Rome Tor Vergata, Department of Economics and Finance, Italy
| | - Gianluigi Conzo
- University of Rome Tor Vergata, Department of Economics and Finance, Italy
| | - Pierluigi Conzo
- University of Turin, Department of Economics and Statistics "Cognetti de Martiis" & Collegio Carlo Alberto, Italy
| | - Davide De Santis
- University of Rome Tor Vergata, Department of Civil Engineering and Computer Science Engineering, Italy
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Changing Air Quality and the Ozone Weekend Effect during the COVID-19 Pandemic in Toronto, Ontario, Canada. CLIMATE 2022. [DOI: 10.3390/cli10030041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Air pollutants, NO, NO2, and O3, were examined from April to June 2020 and compared to a 10-year (2010–2019) climatology of these pollutants for two monitoring sites in Toronto, Ontario, Canada, coinciding with local lockdown measures during the first wave of the COVID-19 pandemic. NO and NO2 values were lower than any of the preceding 10 years at the two Toronto sites for both weekdays and weekends. Ozone concentrations did not have a corresponding decrease and in fact increased for weekdays, similar to other parts of the world. The well-documented ozone weekend effect was considerably muted during the morning rush hour throughout this pandemic period. A Fisher exact test on hourly averaged data revealed statistically significant record hourly minimums for NO and NO2, but this was not found for ozone, consistent with the aggregate ranking results. These findings are likely the result of considerably reduced vehicular traffic during this time and ozone chemistry in a NOx-saturated (VOC limited) environment. This has important implications for ozone abatement strategies.
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Martinez-Boubeta C, Simeonidis K. Airborne magnetic nanoparticles may contribute to COVID-19 outbreak: Relationships in Greece and Iran. ENVIRONMENTAL RESEARCH 2022; 204:112054. [PMID: 34547249 PMCID: PMC8450134 DOI: 10.1016/j.envres.2021.112054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 05/22/2023]
Abstract
This work attempts to shed light on whether the COVID-19 pandemic rides on airborne pollution. In particular, a two-city study provides evidence that PM2.5 contributes to the timing and severity of the epidemic, without adjustment for confounders. The publicly available data of deaths between March and October 2020, updated it on May 30, 2021, and the average seasonal concentrations of PM2.5 pollution over the previous years in Thessaloniki, the second-largest city of Greece, were investigated. It was found that changes in coronavirus-related deaths follow changes in air pollution and that the correlation between the two data sets is maximized at the lag time of one month. Similar data from Tehran were gathered for comparison. The results of this study underscore that it is possible, if not likely, that pollution nanoparticles are related to COVID-19 fatalities (Granger causality, p < 0.05), contributing to the understanding of the environmental impact on pandemics.
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Affiliation(s)
- C Martinez-Boubeta
- Ecoresources P.C, Giannitson-Santaroza Str. 15-17, 54627, Thessaloniki, Greece.
| | - K Simeonidis
- Ecoresources P.C, Giannitson-Santaroza Str. 15-17, 54627, Thessaloniki, Greece; Department of Physics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
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9
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Kang Q, Song X, Xin X, Chen B, Chen Y, Ye X, Zhang B. Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:13400-13410. [PMID: 34559516 DOI: 10.1021/acs.est.1c02204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO2, O3, PM2.5, PM10, SO2, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.
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Affiliation(s)
- Qiao Kang
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
| | - Xing Song
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
| | - Xiaying Xin
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
| | - Bing Chen
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
| | - Yuanzhu Chen
- School of Computing, Queen's University, Kingston K7L 2N8, Ontario, Canada
| | - Xudong Ye
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
| | - Baiyu Zhang
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's A1B 3X5, Newfoundland and Labrador, Canada
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Milano M, Zucco C, Cannataro M. COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data. ACTA ACUST UNITED AC 2021; 10:46. [PMID: 34249598 PMCID: PMC8253246 DOI: 10.1007/s13721-021-00323-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/21/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022]
Abstract
Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.
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Affiliation(s)
- Marianna Milano
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
| | - Chiara Zucco
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
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Anand U, Adelodun B, Pivato A, Suresh S, Indari O, Jakhmola S, Jha HC, Jha PK, Tripathi V, Di Maria F. A review of the presence of SARS-CoV-2 RNA in wastewater and airborne particulates and its use for virus spreading surveillance. ENVIRONMENTAL RESEARCH 2021; 196:110929. [PMID: 33640498 PMCID: PMC7906514 DOI: 10.1016/j.envres.2021.110929] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 05/08/2023]
Abstract
According to the WHO, on October 16, 2020, the spreading of the SARS-CoV-2, responsible for the COVID-19 pandemic, reached 235 countries and territories, and resulting in more than 39 million confirmed cases and 1.09 million deaths globally. Monitoring of the virus outbreak is one of the main activities pursued to limiting the number of infected people and decreasing the number of deaths that have caused high pressure on the health care, social, and economic systems of different countries. Wastewater based epidemiology (WBE), already adopted for the surveillance of life style and health conditions of communities, shows interesting features for the monitoring of the COVID-19 diffusion. Together with wastewater, the analysis of airborne particles has been recently suggested as another useful tool for detecting the presence of SARS-CoV-2 in given areas. The present review reports the status of research currently performed concerning the monitoring of SARS-CoV-2 spreading by WBE and airborne particles. The former have been more investigated, whereas the latter is still at a very early stage, with a limited number of very recent studies. Nevertheless, the main results highlights in both cases necessitate more research activity for better understating and defining the biomarkers and the related sampling and analysis procedures to be used for this important aim.
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Affiliation(s)
- Uttpal Anand
- Department of Life Sciences, National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Bashir Adelodun
- Department of Agricultural and Biosystems Engineering, University of Ilorin, PMB 1515, Ilorin, Nigeria; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Alberto Pivato
- DICEA - Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, 35131, Padova, Italy
| | - S Suresh
- Department of Chemical Engineering, Maulana Azad National Institute of Technology, Bhopal, 462 003, Madhya Pradesh, India
| | - Omkar Indari
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, 453552, Indore, Madhya Pradesh, India
| | - Shweta Jakhmola
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, 453552, Indore, Madhya Pradesh, India
| | - Hem Chandra Jha
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, 453552, Indore, Madhya Pradesh, India
| | - Pawan Kumar Jha
- Centre for Environmental Studies, University of Allahabad, Prayagraj, 211002, Uttar Pradesh, India
| | - Vijay Tripathi
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India.
| | - Francesco Di Maria
- LAR(5) Laboratory - Dipartimento di Ingegneria - University of Perugia, via G. Duranti 93, 06125, Perugia, Italy.
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Collivignarelli MC, Abbà A, Caccamo FM, Bertanza G, Pedrazzani R, Baldi M, Ricciardi P, Carnevale Miino M. Can particulate matter be identified as the primary cause of the rapid spread of CoViD-19 in some areas of Northern Italy? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:10.1007/s11356-021-12735-x. [PMID: 33638072 PMCID: PMC7909738 DOI: 10.1007/s11356-021-12735-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/26/2021] [Indexed: 05/24/2023]
Abstract
Northern Italy was the most affected by CoViD-19 compared to other Italian areas and comprises zones where air pollutants concentration was higher than in the rest of Italy. The aim of the research is to determine if particulate matter (PM) has been the primary cause of the high CoViD-19 spread rapidity in some areas of Northern Italy. Data of PM for all the 41 studied cities were collected from the local environmental protection agencies. To compare air quality data with epidemiological data, a statistical analysis was conducted identifying the correlation matrices of Pearson and Spearman, considering also the possible incubation period of the disease. Moreover, a model for the evaluation of the epidemic risk, already proposed in literature, was used to evaluate a possible influence of PM on CoViD-19 spread rapidity. The results exclude that PM alone was the primary cause of the high CoVid-19 spread rapidity in some areas of Northern Italy. Further developments are necessary for a better comprehension of the influence of atmospheric pollution parameters on the rapidity of spread of the virus SARS-CoV-2, since a synergistic action with other factors (such as meteorological, socio-economic and cultural factors) could not be excluded by the present study.
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Affiliation(s)
- Maria Cristina Collivignarelli
- Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100, Pavia, Italy
- Interdepartmental Centre for Water Research, University of Pavia, via Ferrata 3, 27100, Pavia, Italy
| | - Alessandro Abbà
- Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, via Branze 43, 25123, Brescia, Italy
| | - Francesca Maria Caccamo
- Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100, Pavia, Italy
| | - Giorgio Bertanza
- Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, via Branze 43, 25123, Brescia, Italy
| | - Roberta Pedrazzani
- Department of Mechanical and Industrial Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy
| | - Marco Baldi
- Department of Chemistry, University of Pavia, viale Taramelli 10, 27100, Pavia, Italy
| | - Paola Ricciardi
- Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100, Pavia, Italy
| | - Marco Carnevale Miino
- Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100, Pavia, Italy.
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Becchetti L, Beccari G, Conzo G, Conzo P, De Santis D, Salustri F. Air quality and COVID-19 adverse outcomes: Divergent views and experimental findings. ENVIRONMENTAL RESEARCH 2021; 193:110556. [PMID: 33278470 PMCID: PMC7711169 DOI: 10.1016/j.envres.2020.110556] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 11/24/2020] [Indexed: 05/24/2023]
Abstract
BACKGROUND The questioned link between air pollution and coronavirus disease 2019 (COVID-19) spreading or related mortality represents a hot topic that has immediately been regarded in the light of divergent views. A first "school of thought" advocates that what matters are only standard epidemiological variables (i.e. frequency of interactions in proportion of the viral charge). A second school of thought argues that co-factors such as quality of air play an important role too. METHODS We analyzed available literature concerning the link between air quality, as measured by different pollutants and a number of COVID-19 outcomes, such as number of positive cases, deaths, and excess mortality rates. We reviewed several studies conducted worldwide and discussing many different methodological approaches aimed at investigating causality associations. RESULTS Our paper reviewed the most recent empirical researches documenting the existence of a huge evidence produced worldwide concerning the role played by air pollution on health in general and on COVID-19 outcomes in particular. These results support both research hypotheses, i.e. long-term exposure effects and short-term consequences (including the hypothesis of particulate matter acting as viral "carrier") according to the two schools of thought, respectively. CONCLUSIONS The link between air pollution and COVID-19 outcomes is strong and robust as resulting from many different research methodologies. Policy implications should be drawn from a "rational" assessment of these findings as "not taking any action" represents an action itself.
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Affiliation(s)
| | | | | | - Pierluigi Conzo
- University of Turin, Turin, Italy; Collegio Carlo Alberto, Turin, Italy
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Casini L, Roccetti M. A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7319. [PMID: 33352802 PMCID: PMC7766224 DOI: 10.3390/s20247319] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/13/2020] [Accepted: 12/17/2020] [Indexed: 12/11/2022]
Abstract
On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease.
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Affiliation(s)
| | - Marco Roccetti
- Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy;
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Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning. COMPUTATION 2020. [DOI: 10.3390/computation8030074] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society.
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