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1.
Int J Environ Res Public Health ; 19(9)2022 04 21.
Article in English | MEDLINE | ID: covidwho-1953272

ABSTRACT

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Wildfires , Air Pollutants/analysis , Air Pollution/adverse effects , Air Pollution/analysis , COVID-19/epidemiology , Humans , Pandemics , Particulate Matter/analysis , Seasons
2.
International Journal of Environmental Research and Public Health ; 19(9):5057, 2022.
Article in English | ProQuest Central | ID: covidwho-1837040

ABSTRACT

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

3.
Stoch Environ Res Risk Assess ; : 1-16, 2022 Apr 23.
Article in English | MEDLINE | ID: covidwho-1802730

ABSTRACT

Climate and air quality change due to COVID-19 lockdown (LCD) are extremely concerned subjects of several research recently. The contribution of meteorological factors and emission reduction to air pollution change over the north of Morocco has been investigated in this study using the framework generalized additive models, that have been proved to be a robust technique for the environmental data sets, focusing on main atmospheric pollutants in the region including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), secondary inorganic aerosols (SIA), nom-methane volatile organic compounds and carbon monoxide (CO) from the regional air pollution dataset of the Copernicus Atmosphere Monitoring Service. Our results, indicate that secondary air pollutants (PM2.5, PM10 and O3) are more influenced by metrological factors and the other air pollutants reported by this study (NO2 and SO2). We show a negative effect for PBHL, total precipitation and NW10M on PM (PM2.5 and PM10 ), this meteorological parameters contribute to decrease in PM2.5 by 9, 2 and 9% respectively, before LCD and 8, 1 and 5% respectively during LCD. However, a positive marginal effect was found for SAT, Irradiance and RH that contribute to increase PM2.5 by 9, 12 and 18% respectively, before LCD and 17, 54 and 34% respectively during LCD. We found also that meteorological factors contribute to O3, PM2.5, PM10 and SIA average mass concentration by 22, 5, 3 and 34% before LCD and by 28, 19, 5 and 42% during LCD respectively. The increase in meteorological factors marginal effect during LCD shows the contribution of photochemical oxidation to air pollution due to increase in atmospheric oxidant (O3 and OH radical) during LCD, which can explain the response of PM to emission reduction. This study indicates that PM (PM2.5, PM10) has more controlled by SO2 due to the formation of sulfate particles especially under high oxidants level. The positive correlation between westward wind at 10 m (WW10M), Northward Wind at 10 m (NW10M) and PM indicates the implication of sea salt particles transported from Mediterranean Sea and Atlantic Ocean. The Ozone mass concentration shows a positive trend with Irradiance, Total and SAT during LCD; because temperature and irradiance enhance tropospheric ozone formation via photochemical reaction.This study shows the contribution of atmospheric oxidation capacity to air pollution change. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02224-z.

4.
IEEE Open Access Journal of Power and Energy ; 2022.
Article in English | Scopus | ID: covidwho-1779148

ABSTRACT

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach. Author

5.
Fields Institute Communications ; 85:153-171, 2022.
Article in English | Scopus | ID: covidwho-1705451

ABSTRACT

To capture the death rates and strong weekly, biweekly and probably monthly patterns in the Canada COVID-19, we utilize the generalized additive models in the absence of direct statistically based measurement of infection rates. By examining the death rates of Canada in general and Quebec, Ontario and Alberta in particular, that there are substantial overdispersion relative to the Poisson so that the negative binomial distribution is an appropriate choice for the analysis. Generalized additive models (GAMs) are one of the main modeling tools for data analysis. © 2022, Springer Nature Switzerland AG.

6.
Sci Total Environ ; 811: 152334, 2022 Mar 10.
Article in English | MEDLINE | ID: covidwho-1638653

ABSTRACT

The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Viral load detected in the wastewater and the epidemiological data from A Coruña health system served as main sources for statistical models developing. Regression models described here allowed us to estimate the number of infected people (R2 = 0.9), including symptomatic and asymptomatic individuals. These models have helped to understand the real magnitude of the epidemic in a population at any given time and have been used as an effective early warning tool for predicting outbreaks in A Coruña municipality. The methodology of the present work could be used to develop a similar wastewater-based epidemiological model to track the evolution of the COVID-19 epidemic anywhere in the world where centralized water-based sanitation systems exist.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , RNA, Viral , Spain/epidemiology , Viral Load , Waste Water
7.
Drug Alcohol Depend ; 229(Pt A): 109176, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1517124

ABSTRACT

BACKGROUND: COVID-19 and resulting mitigation measures in the United States (US) brought about limited access to medical care that has been linked to increases in mental health problems, excessive substance use, and drug overdoses. The increase in co-prescription of benzodiazepines and opioids may indicate population-level changes in health behaviors that can be exacerbated by limited access, hence necessitating the tracking of these drugs during COVID-19. We evaluated the impact of the declaration of COVID-19 as a US national emergency on prescription patterns in 2020. METHODS: Prescriptions of benzodiazepines and opioids were analyzed using data aggregated on a weekly basis across 38 states over the January 2019-December 2020 period. Data were from Bamboo Health Prescription Drug Monitoring Program and covered all individuals regardless of insurance status. Generalized additive models estimated the effects of the March 13, 2020 declaration on proportion of prescriptions to all controlled substances by comparing volumes before to after the week of March 13 in 2020 (range: January 27-May 24) and comparing this trend to its 2019 counterpart. RESULTS: When comparing the January 27-March 9 period to the March 16-May 24 period in 2020, there was a statistically significant 2.0% increase in the proportion of benzodiazepine dispensations to all controlled substances, and a significant 1.7% mean decrease in proportion of opioid dispensations to all controlled substances. A significant return approaching pre-declaration levels was observed only for opioids (beginning week of May 18, 2020). CONCLUSIONS: The results suggest significant impacts of the COVID-19 pandemic on dispensations of benzodiazepines and opioids across the US. Continued monitoring of prescription trends and maintenance of adequate and accessible access to mental healthcare are important for understanding public health crises related to substance use.


Subject(s)
Analgesics, Opioid , COVID-19 , Analgesics, Opioid/therapeutic use , Benzodiazepines , Controlled Substances , Drug Prescriptions , Humans , Pandemics , SARS-CoV-2 , United States/epidemiology
8.
Biom J ; 63(8): 1623-1632, 2021 12.
Article in English | MEDLINE | ID: covidwho-1351200

ABSTRACT

The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020.


Subject(s)
COVID-19 , Pandemics , Humans , Models, Statistical , SARS-CoV-2
9.
Environ Pollut ; 289: 117899, 2021 Nov 15.
Article in English | MEDLINE | ID: covidwho-1336407

ABSTRACT

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM2.5, PM10, NO2, SO2, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015-2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NOx at least 43 %. However, O3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O3, O3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NOx reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Air Pollution/prevention & control , China , Communicable Disease Control , Environmental Monitoring , Humans , Particulate Matter/analysis , SARS-CoV-2
10.
BMC Public Health ; 20(1): 1585, 2020 Oct 21.
Article in English | MEDLINE | ID: covidwho-883573

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. METHODS: We obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 cases. RESULTS: We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 µg/m3 increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 µg/m3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. CONCLUSIONS: Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.


Subject(s)
Coronavirus Infections/epidemiology , Particulate Matter/adverse effects , Pneumonia, Viral/epidemiology , Population Dynamics/statistics & numerical data , COVID-19 , China/epidemiology , Cities/epidemiology , Humans , Pandemics , Particulate Matter/analysis , Risk Assessment
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