scholarly journals Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 951
Author(s):  
Steve Cicala ◽  
Stephen P. Holland ◽  
Erin T. Mansur ◽  
Nicholas Z. Muller ◽  
Andrew J. Yates

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Corentin Cot ◽  
Giacomo Cacciapaglia ◽  
Francesco Sannino

AbstractWe employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US.


2021 ◽  
Author(s):  
Ashlynn R. Daughton ◽  
Courtney Diane Shelley ◽  
Martha Barnard ◽  
Dax Gerts ◽  
Chrysm Watson Ross ◽  
...  

BACKGROUND Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging is also important for minimizing long term effects of an outbreak. OBJECTIVE We used social media data to identify human behaviors relevant to COVID-19 transmission and the perceived impacts of COVID-19 on individuals as a first step toward real time monitoring of public perceptions to inform public health communications. METHODS We develop a coding schema for 6 categories and 11 subcategories, which includes both a wide number of behaviors, as well codes focused on the impacts of the pandemic (e.g., economic and mental health impacts). We use this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that perform adequately are applied to our remaining corpus and temporal and geospatial trends are assessed. We compare the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS We apply our labeling schema to ~7200 tweets. The worst performing classifiers have F1 scores of only 0.18-0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, are much stronger with F1 scores of 0.64-0.66. We applied the social distancing classifiers to over 228 million tweets. We show temporal patterns consistent with real-world events, and show correlations of up to -0.5 between social distancing signals on Twitter and ground-truth mobility throughout the United States. CONCLUSIONS Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior as well as informing public health communication strategies by describing awareness of and compliance with suggested behaviors. CLINICALTRIAL N/A


2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R Benetka ◽  
John Kraemer ◽  
Shweta Bansal

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.


2017 ◽  
Author(s):  
Ulas Im ◽  
Jørgen Brandt ◽  
Camilla Geels ◽  
Kaj Mantzius Hansen ◽  
Jesper Heile Christensen ◽  
...  

Abstract. The impact of air pollution on human health and the associated external costs in Europe and the United States (U.S.) for the year 2010 is modelled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). This is the first study known to use a common health assessment approach across the two continents. The modelled surface concentrations of O3, CO, SO2 and PM2.5 from each model are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs. Along with a base case simulation, additional runs were performed introducing 20 % emission reductions both globally and regionally in Europe, North America and East Asia. Health impacts estimated by different models can vary up to a factor of three in Europe (twelve models) and the United States (three models). In Europe, the multi-model mean number of premature deaths is calculated to be 414 000 while in the U.S., it is estimated to be 160 000, in agreement with previous global and regional studies. In order to estimate the impact of biases coming from each model, two multi-model ensembles were produced, the first attributing an equal weight to each member of the ensemble, and the second where the subset of models that produce the smallest error compared to the surface observations at each time step. The latter results in increase of health impacts by up to 30 % in Europe, thus giving significantly higher mortality estimates compared to available literature. This is mostly due to a 27 % increase in the domain mean PM2.5 levels, along with a slight increase in O3 by ~ 1 %. Over the U.S., the mean PM2.5 and O3 levels decrease by 11 % and 2 %, respectively, when the optimal ensemble mean is used, leading to a decrease in the calculated health impacts by ~ 11 %. These differences encourage the use of optimal-reduced multi-model ensembles over traditional all model-mean ensembles, in particular for policy applications. Finally, the role of domestic versus foreign emission sources on the related health impacts is investigated using the 20 % emission reduction scenarios applied over the source regions as defined in the frame of HTAP2. The differences are calculated based on the models that are common in the basic multi-model ensemble and the perturbation scenarios, resulting in five models in Europe and all three models in the U.S. A 20 % reduction of global anthropogenic emissions avoids 54 000 and 27 500 premature deaths in Europe and the U.S., respectively. A 20 % reduction of North American emissions foreign emissions avoids ~ 1000 premature deaths in Europe and 25 000 premature deaths in the U.S. A 20 % decrease of emissions within the European source region avoids 47 000 premature deaths in Europe. Reducing the East Asian emission by 20 % avoids ~ 2000 premature deaths in the U.S. These results show that the domestic emissions make the largest impacts on premature death, while foreign sources make a minor contributing to adverse impacts of air pollution.


PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253071
Author(s):  
Liana R. Woskie ◽  
Jonathan Hennessy ◽  
Valeria Espinosa ◽  
Thomas C. Tsai ◽  
Swapnil Vispute ◽  
...  

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.


2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R. Benetka ◽  
John Kraemer ◽  
Shweta Bansal

AbstractImportanceEliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic.ObjectiveTo assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations.Design, Setting, and ParticipantsWe used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density.Main outcomes and ResultsWe find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations.Conclusions and relevanceSocio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.


Author(s):  
Nickolas Dreher ◽  
Zachary Spiera ◽  
Fiona M. McAuley ◽  
Lindsey Kuohn ◽  
John R. Durbin ◽  
...  

AbstractBackgroundPolicymakers have employed various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures to limit the spread of Coronavirus disease (COVID-19). However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states.MethodsIn order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables.ResultsStates that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002).DiscussionFew studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics.


2021 ◽  
Vol 3 (1) ◽  
pp. 25-31
Author(s):  
Ade Suherman ◽  
Tetep Tetep ◽  
Asep Supriyatna ◽  
Eldi Mulyana ◽  
Triani Widyanti ◽  
...  

The purpose of this study is to analyze and explain public perceptions of the implementation of social distancing during the pandemic as the implementation of social capital. This study was motivated by the phenomenon of the outbreak of the Covid-19 pandemic in a number of countries, including Indonesia. This condition not only affects the economic condition of a country, hinders social interaction among the community, and also has an impact on the health condition of every human being. To avoid the wider spread of Covid-19, the government was forced to adopt social distancing and physical distancing policies in the form of staying at home, working from home, studying, and worshiping at home. This research approach is descriptive qualitative. The data of this research is the impact of social distancing for the community in Tarogong Kidul District, Garut Regency. Sources of data come from several communities with a total of 50 respondents. Collecting data in this study using interview techniques, record, and continue to take notes. The results of the research can be concluded that with the implementation of social distancing in the pandemic period, at least the community can implement social capital which includes informal values ​​or norms that are shared among members of an interrelated community group, which is based on the values ​​of beliefs, norms and networks social and they respect each other, the development of social capital is the creation of increasingly independent groups of people who are able to participate more meaningfully. Social capital can solve citizens' problems, especially with regard to strengthening friendship, repairing and maintaining public service facilities because it has advantages and is the most appropriate, even though there are other social capital in the community.


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