A review on the biological, epidemiological, and statistical relevance of COVID-19 paired with air pollution

Yates, Elizabeth; Zhang, Kenneth; Naus, Abbie; Forbes, Callum; Wu, Xiao; Dey, Tanujit

doi:10.1016/j.envadv.2022.100250

Cited by 14 publications

(10 citation statements)

References 35 publications

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“…Indoor ventilation technologies may affect the distribution and colony of indoor microbial pathogens and 222 Rn in air concentrations, and sometimes SARS-CoV-2 pathogens transmission [15][16]. The findings of this study support the wide epidemiological evidence that COVID-19 viral disease incidence and severity is associated with high concentration levels of the ambient particulate matter PM, that worse COVID-19 outcomes [17][18][19][20].…”

Section: Particulate Matter Pm25 Pm10 222 Rn and Covid-19 Viral Diseasesupporting

confidence: 73%

Climate Effects of Aerosols and Radon on Covid-19 Pandemic in Bucharest Metropolitan Area

Zoran,

Savastru,

Savastru

et al. 2023

RAP Conference Proceedings

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This paper investigated the influences of urban aerosols and radon ( 222 Rn) together climate parameters variability at both local and regional scales in relationship with COVID-19 pandemic incidence and mortality in Bucharest metropolitan area of Romania, considered one of the European's most polluted hotspots cities. A spatiotemporal analysis of the daily particulate matter in two size fractions PM10 and PM2.5 in relation with daily radon concentrations and meteorological parameters was done through synergy of in-situ monitoring data and MODIS Terra/Aqua time-series satellite data for March 2020-April 2022 time period. This study investigated the COVID-19 waves patterns under different air quality and meteorological conditions, highlighting the role of synoptic anticyclonic stagnant conditions during each COVID-19 wave for SARS-CoV-2 virus spreading. These results contribute to a better understanding of urban decision makers and epidemiologists through considering the specific characteristics of different urban sectors for air quality improvement.

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Section: Particulate Matter Pm25 Pm10 222 Rn and Covid-19 Viral Diseasesupporting

confidence: 73%

Climate Effects of Aerosols and Radon on Covid-19 Pandemic in Bucharest Metropolitan Area

Zoran,

Savastru,

Savastru

et al. 2023

RAP Conference Proceedings

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“…Second, considering that the relationship between multiple pollutants and Covid-19 is still controversial [41], we verify the influence of PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 on the spread of Covid-19. We find that, all air pollutants accelerate the spread of Covid-19 except SO 2 , which is basically consistent with the results of Zhu et al (2020) [40].…”

Section: Discussionmentioning

confidence: 88%

The Impact of City Lockdown and Air Pollution on the Spread of Covid-19

Fan,

Wang,

Zhou

et al. 2023

Pol. J. Environ. Stud.

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Considering the high variability of Covid-19 and unknown sequelae of rebound infections. We may be caught in a long-term struggle against the virus. The evaluation and summary of past anti-epidemic policies highlight profound practical significance for either decision makers or individuals. This paper applies both theoretical and empirical approaches to study the impact of city lockdown and air pollution on Covid-19. First, we apply an extended Susceptible-Infected-Removed (SIR) model to identify the relationship between lockdown, air pollution, and Covid-19 cases. Second, we apply the Differences-in-Differences (DID) model and Poisson Pseudo-Maximum Likelihood (PPML) model to test the impact of lockdown on Covid-19, by sorting out lockdown policies implemented by cities during the epidemic. Third, we empirically analyze the impact of air pollution on Covid-19, based on daily data from 257 cities in China. Finally, we examine the mechanism by which lockdown impacts Covid-19. Newly confirmed Covid-19 cases are reduced by 31.1% nationwide (excluding Wuhan) during the lockdown period. Regions with lower air pollution experience fewer Covid-19 cases. Air pollution aggravates Covid-19, with the pollutants PM2.5, PM10, NO2, CO, and O3 having the greatest impact. The results indicate that an increase in one unit of air quality index (AQI) concentrations is associated with 1.723 more newly confirmed cases. The effects of air pollution on the spread of Covid-19 diminish as the population size increases. Lockdown can inhibit the spread of Covid-19 by reducing air pollution and population movement.

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“…Besides, [ 88 ] showed high CO concentrations to be positively correlated with COVID-19’s reproductive ratio R 0 , whereas [ 89 ] observed positive correlations among the mean values of PM 10 , NO 2 , CO, and SO 2 and the number of COVID-19 cases, mortality rates and critical cases. A review [ 90 ] suggested biological mechanisms for the exposure to air pollutants (such as CO, NO 2 , O 3 , PM 2.5 , PM 10 , and SO 2 ) to increase the risk of contracting the SARS-CoV-2 virus. Besides, [ 91 ] found that –among different pollutants– NO 2 , PM 2.5 , PM 10 , and O 3 had the strongest correlation with the infection risk of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

Hayet-Otero

García-García²,

Lee³

et al. 2023

PLoS ONE

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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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A review on the biological, epidemiological, and statistical relevance of COVID-19 paired with air pollution

Cited by 14 publications

References 35 publications

Climate Effects of Aerosols and Radon on Covid-19 Pandemic in Bucharest Metropolitan Area

Climate Effects of Aerosols and Radon on Covid-19 Pandemic in Bucharest Metropolitan Area

The Impact of City Lockdown and Air Pollution on the Spread of Covid-19

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

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