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Investigating the Significance of Aerosols in Determining the Coronavirus Fatality Rate Among Three European Countries

ABSTRACT: The coronavirus pandemic has not only gripped the scientific community in the search for a vaccine or a cure but also in attempts using statistics and association analysis—to identify environmental factors that increase its potency. A study by Ogen (Sci Total Environ 726:138605, 2020a) explored the possible correlation between coronavirus fatality and high nitrogen dioxide exposure in four European countries—France, Germany, Italy and Spain. Meanwhile, another study showed the importance of nitrogen dioxide along with population density in determining the coronavirus pandemic rate in England. In this follow-up study, Aerosol Optical Depth (AOD) was introduced in conjunction with other variables like nitrogen dioxide and population density for further analysis in fifty-four administrative regions of Germany, Italy and Spain. The AOD values were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites while the nitrogen dioxide data were extracted from TROPOMI (TROPOspheric Monitoring Instrument) sensor onboard the Sentinel-5 Precursor satellite. Regression models, as well as multiple statistical tests were used to evaluate the predictive skill and significance of each variable to the fatality rate. The study was conducted for two periods: (1) pre-exposure period (Dec 1, 2019–Feb 29, 2020); (2) complete exposure period (Dec 1, 2019–Jul 1, 2020). Some of the results pointed towards AOD potentially being a factor in estimating the coronavirus fatality rate. The models performed better using the data collected during the complete exposure period, which showed higher AOD values contributed to an increased significance of AOD in the models. Meanwhile, some uncertainties of the analytical results could be attributed to data quality and the absence of other important factors that determine the coronavirus fatality rate.

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The online version of this article (10.1007/s41748-020-00176-4) contains supplementary material, which is available to authorized users.

SUBMITTER: Li W

PROVIDER: S-EPMC7502156 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries.MethodsWe fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries.ResultsWe simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.ConclusionsThis study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

Project description:BackgroundPolicies on waste disposal in Europe are heterogeneous and rapidly changing, with potential health implications that are largely unknown. We conducted a health impact assessment of landfilling and incineration in three European countries: Italy, Slovakia and England.MethodsA total of 49 (Italy), 2 (Slovakia), and 11 (England) incinerators were operating in 2001 while for landfills the figures were 619, 121 and 232, respectively. The study population consisted of residents living within 3 km of an incinerator and 2 km of a landfill. Excess risk estimates from epidemiological studies were used, combined with air pollution dispersion modelling for particulate matter (PM10) and nitrogen dioxide (NO2). For incinerators, we estimated attributable cancer incidence and years of life lost (YoLL), while for landfills we estimated attributable cases of congenital anomalies and low birth weight infants.ResultsAbout 1,000,000, 16,000, and 1,200,000 subjects lived close to incinerators in Italy, Slovakia and England, respectively. The additional contribution to NO2 levels within a 3 km radius was 0.23, 0.15, and 0.14 μg/m3, respectively. Lower values were found for PM10. Assuming that the incinerators continue to operate until 2020, we are moderately confident that the annual number of cancer cases due to exposure in 2001-2020 will reach 11, 0, and 7 in 2020 and then decline to 0 in the three countries in 2050. We are moderately confident that by 2050, the attributable impact on the 2001 cohort of residents will be 3,621 (Italy), 37 (Slovakia) and 3,966 (England) YoLL. The total exposed population to landfills was 1,350,000, 329,000, and 1,425,000 subjects, respectively. We are moderately confident that the annual additional cases of congenital anomalies up to 2030 will be approximately 2, 2, and 3 whereas there will be 42, 13, and 59 additional low-birth weight newborns, respectively.ConclusionsThe current health impacts of landfilling and incineration can be characterized as moderate when compared to other sources of environmental pollution, e.g. traffic or industrial emissions, that have an impact on public health. There are several uncertainties and critical assumptions in the assessment model, but it provides insight into the relative health impact attributable to waste management.

Project description:BackgroundWhen a new disease such starts to spread, the commonly asked questions are how deadly is it? and how many people are likely to die of this outbreak? The World Health Organization (WHO) announced in a press conference on January 29, 2020 that the death rate of COVID-19 was 2% on the case fatality rate (CFR). It was underestimated assuming no lag days from symptom onset to deaths while many CFR formulas have been proposed, the estimation on Bays theorem is worthy of interpretation. Hence, it is hypothesized that the over-loaded burdens of treating patients and capacities to contain the outbreak (LSBHRS) may increase the CFR.MethodsWe downloaded COVID-19 outbreak numbers from January 21 to February 14, 2020, in countries/regions on a daily basis from Github that contains information on confirmed cases in >30 Chinese locations and other countries/regions. The pros and cons were compared among the 5 formula of CFR, including [A] deaths/confirmed; [B] deaths/(deaths + recovered); [C] deaths/(cases x days ago); [D] Bayes estimation based on [A] and the outbreak (LSBHRS) in each country/region; and [E] Bayes estimation based on [C] deaths/(cases x days ago). The coefficients of variance (CV = the ratio of the standard deviation to the mean) were applied to measure the relative variability for each CFR. A dashboard was developed for daily display of the CFR across each region.ResultsThe Bayes based on (A)[D] has the lowest CV (=0.10) followed by the deaths/confirmed (=0.11) [A], deaths/(deaths + recoveries) (=0.42) [B], Bayes based on (C) (=0.49) [E], and deaths/(cases x days ago) (=0.59) [C]. All final CFRs will be equal using the formula (from, A to E). A dashboard was developed for the daily reporting of the CFR. The CFR (3.7%) greater than the prior CFR of 2.2% was evident in LSBHRS, increasing the CFR. A dashboard was created to present the CFRs on COVID-19.ConclusionWe suggest examining both trends of the Bayes based on both deaths/(cases 7 days ago) and deaths/confirmed cases as a reference to the final CFR. An app developed for displaying the provisional CFR with the 2 CFR trends can improve the underestimated CFR reported by WHO and media.

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Investigating the Significance of Aerosols in Determining the Coronavirus Fatality Rate Among Three European Countries

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