Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 51
Filter
1.
Sci Rep ; 11(1): 22120, 2021 11 11.
Article in English | MEDLINE | ID: covidwho-1758321

ABSTRACT

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape's scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in "patchy" parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city's morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.


Subject(s)
COVID-19/epidemiology , Cities/epidemiology , Human Activities , Humans , Israel/epidemiology , SARS-CoV-2/isolation & purification , Satellite Imagery , Urban Population
2.
Nat Commun ; 13(1): 959, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1699459

ABSTRACT

Record rainfall and severe flooding struck eastern China in the summer of 2020. The extreme summer rainfall occurred during the COVID-19 pandemic, which started in China in early 2020 and spread rapidly across the globe. By disrupting human activities, substantial reductions in anthropogenic emissions of greenhouse gases and aerosols might have affected regional precipitation in many ways. Here, we investigate such connections and show that the abrupt emissions reductions during the pandemic strengthened the summer atmospheric convection over eastern China, resulting in a positive sea level pressure anomaly over northwestern Pacific Ocean. The latter enhanced moisture convergence to eastern China and further intensified rainfall in that region. Modeling experiments show that the reduction in aerosols had a stronger impact on precipitation than the decrease of greenhouse gases did. We conclude that through abrupt emissions reductions, the COVID-19 pandemic contributed importantly to the 2020 extreme summer rainfall in eastern China.


Subject(s)
Aerosols/analysis , COVID-19/epidemiology , Greenhouse Gases/analysis , Rain , Vehicle Emissions/analysis , China/epidemiology , Floods , Human Activities/statistics & numerical data , Humans , Pandemics/statistics & numerical data , SARS-CoV-2 , Seasons
3.
Sensors (Basel) ; 21(24)2021 Dec 16.
Article in English | MEDLINE | ID: covidwho-1576973

ABSTRACT

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.


Subject(s)
Human Activities , Humans
4.
Sci Rep ; 11(1): 23378, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585808

ABSTRACT

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , COVID-19/prevention & control , Particulate Matter/analysis , SARS-CoV-2/isolation & purification , Soot/analysis , Algorithms , Atmosphere/analysis , COVID-19/epidemiology , COVID-19/virology , China , Climate Change , Environmental Monitoring/methods , Environmental Monitoring/statistics & numerical data , Geography , Human Activities , Humans , Models, Theoretical , Pandemics , Residence Characteristics , SARS-CoV-2/physiology , Seasons , Wind
5.
Epidemiol Infect ; 150: e9, 2021 11 17.
Article in English | MEDLINE | ID: covidwho-1521671

ABSTRACT

Identification of societal activities associated with SARS-CoV-2 infection may provide an evidence base for implementing preventive measures. Here, we investigated potential determinants for infection in Denmark in a situation where society was only partially open. We conducted a national matched case-control study. Cases were recent RT-PCR test-positives, while controls, individually matched on age, sex and residence, had not previously tested positive for SARS-CoV-2. Questions concerned person contact and community exposures. Telephone interviews were performed over a 7-day period in December 2020. We included 300 cases and 317 controls and determined odds ratios (ORs) and 95% confidence intervals (95% CI) by conditional logistical regression with adjustment for household size and country of origin. Contact (OR 4.9, 95% CI 2.4-10) and close contact (OR 13, 95% CI 6.7-25) with a person with a known SARS-CoV-2 infection were main determinants. Contact most often took place in the household or work place. Community determinants included events with singing (OR 2.1, 95% CI 1.1-4.1), attending fitness centres (OR 1.8, 95% CI 1.1-2.8) and consumption of alcohol in a bar (OR 10, 95% CI 1.5-65). Other community exposures appeared not to be associated with infection, these included shopping at supermarkets, travel by public transport, dining at restaurants and private social events with few participants. Overall, the restrictions in place at the time of the study appeared to be sufficient to reduce transmission of disease in the public space, which instead largely took place following direct exposures to people with known SARS-CoV-2 infections.


Subject(s)
COVID-19/epidemiology , Human Activities/statistics & numerical data , Adult , Case-Control Studies , Denmark/epidemiology , Female , Humans , Male , Middle Aged , Quarantine/organization & administration , Young Adult
6.
Sensors (Basel) ; 21(22)2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1512573

ABSTRACT

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants' body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.


Subject(s)
COVID-19 , Activities of Daily Living , Algorithms , Body Temperature , Human Activities , Humans , SARS-CoV-2
7.
Sci Rep ; 11(1): 20791, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479819

ABSTRACT

Implementation of various restrictions to eradicate viral diseases has globally affected human activity and subsequently nature. But how can the altered routines of human activity (restrictions, lockdowns) affect wildlife behaviour? This study compared the differences between human and wildlife occurrences in the study forest area with acreage of 5430.6 ha in 2018 (African swine fever outbreak, complete entrance ban), 2019 (standard pattern) and 2020 (COVID-19 restrictions) during the breeding season. The number of visitors was lower by 64% in 2018 (non-respecting of the entry ban by forest visitors) compared to standard 2019, while in 2020, the number of visitors increased to 151%. In the COVID-19 period, distinct peaks in the number of visitors were observed between 8-11 AM and 4-7 PM. The peaks of wildlife activity were recorded between 4-7 AM and 9-12 PM. Animals avoided the localities that were visited by humans during the people-influenced time (24 h after people visit), which confirmed the direct negative impact of human activities on wildlife.


Subject(s)
African Swine Fever/epidemiology , Animals, Wild , COVID-19/epidemiology , Communicable Disease Control/methods , Disease Outbreaks , Human Activities , Animals , Female , Geography , Humans , Male , Pandemics , Regression Analysis , SARS-CoV-2 , Swine , Temperature , Virus Diseases/epidemiology
8.
Proc Natl Acad Sci U S A ; 118(43)2021 10 26.
Article in English | MEDLINE | ID: covidwho-1475568

ABSTRACT

Fire is a common ecosystem process in forests and grasslands worldwide. Increasingly, ignitions are controlled by human activities either through suppression of wildfires or intentional ignition of prescribed fires. The southeastern United States leads the nation in prescribed fire, burning ca. 80% of the country's extent annually. The COVID-19 pandemic radically changed human behavior as workplaces implemented social-distancing guidelines and provided an opportunity to evaluate relationships between humans and fire as fire management plans were postponed or cancelled. Using active fire data from satellite-based observations, we found that in the southeastern United States, COVID-19 led to a 21% reduction in fire activity compared to the 2003 to 2019 average. The reduction was more pronounced for federally managed lands, up to 41% below average compared to the past 20 y (38% below average compared to the past decade). Declines in fire activity were partly affected by an unusually wet February before the COVID-19 shutdown began in mid-March 2020. Despite the wet spring, the predicted number of active fire detections was still lower than expected, confirming a COVID-19 signal on ignitions. In addition, prescribed fire management statistics reported by US federal agencies confirmed the satellite observations and showed that, following the wet February and before the mid-March COVID-19 shutdown, cumulative burned area was approaching record highs across the region. With fire return intervals in the southeastern United States as frequent as 1 to 2 y, COVID-19 fire impacts will contribute to an increasing backlog in necessary fire management activities, affecting biodiversity and future fire danger.


Subject(s)
COVID-19/prevention & control , Pandemics , Physical Distancing , SARS-CoV-2 , Wildfires/prevention & control , Biodiversity , COVID-19/epidemiology , Droughts/statistics & numerical data , Ecosystem , Forests , Human Activities , Humans , Models, Statistical , Pandemics/prevention & control , Southeastern United States/epidemiology , Weather , Wildfires/statistics & numerical data
9.
Sensors (Basel) ; 20(21)2020 Oct 22.
Article in English | MEDLINE | ID: covidwho-1450862

ABSTRACT

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government's support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.


Subject(s)
Computers , Technology , China , Environment Design , Human Activities , Humans , Internet of Things
10.
Sensors (Basel) ; 21(17)2021 Aug 25.
Article in English | MEDLINE | ID: covidwho-1379985

ABSTRACT

The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically: 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.


Subject(s)
Human Activities , Photography , COVID-19 , Humans , Pandemics
12.
Vet Rec ; 187(10): 375, 2020 11 14.
Article in English | MEDLINE | ID: covidwho-1354590
13.
PLoS One ; 16(8): e0255236, 2021.
Article in English | MEDLINE | ID: covidwho-1341501

ABSTRACT

Behavioral epidemiology suggests that there is a tight dynamic coupling between the timeline of an epidemic outbreak, and the social response in the affected population (with a typical course involving physical distancing between individuals, avoidance of large gatherings, wearing masks, etc). We study the bidirectional coupling between the epidemic dynamics of COVID-19 and the population social response in the state of New York, between March 1, 2020 (which marks the first confirmed positive diagnosis in the state), until June 20, 2020. This window captures the first state-wide epidemic wave, which peaked to over 11,000 confirmed cases daily in April (making New York one of the US states most severely affected by this first wave), and subsided by the start of June to a count of consistently under 1,500 confirmed cases per day (suggesting temporary state-wide control of the epidemic). In response to the surge in cases, social distancing measures were gradually introduced over two weeks in March, culminating with the PAUSE directive on March 22nd, which mandated statewide shutdown of all nonessential activity. The mandates were then gradually relaxed in stages throughout summer, based on how epidemic benchmarks were met in various New York regions. In our study, we aim to examine on one hand, whether different counties exhibited different responses to the PAUSE centralized measures depending on their epidemic situation immediately preceding PAUSE. On the other hand, we explore whether these different county-wide responses may have contributed in turn to modulating the counties' epidemic timelines. We used the public domain to extract county-wise epidemic measures (such as cumulative and daily incidence of COVID-19), and social mobility measures for different modalities (driving, walking, public transit) and to different destinations. Our correlation analyses between the epidemic and the mobility time series found significant correlations between the size of the epidemic and the degree of mobility drop after PAUSE, as well as between the mobility comeback patterns and the epidemic recovery timeline. In line with existing literature on the role of the population behavioral response during an epidemic outbreak, our results support the potential importance of the PAUSE measures to the control of the first epidemic wave in New York State.


Subject(s)
COVID-19/epidemiology , Health Behavior/physiology , Infection Control , Disease Outbreaks , Epidemics , History, 21st Century , Human Activities/statistics & numerical data , Humans , Infection Control/legislation & jurisprudence , Infection Control/methods , Mandatory Programs/legislation & jurisprudence , Masks , New York/epidemiology , Physical Distancing , Quarantine/psychology , Quarantine/statistics & numerical data , SARS-CoV-2/physiology , Time Factors , Transportation/statistics & numerical data
14.
PLoS Comput Biol ; 17(7): e1009162, 2021 07.
Article in English | MEDLINE | ID: covidwho-1305574

ABSTRACT

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions.


Subject(s)
COVID-19 , Communicable Disease Control/statistics & numerical data , Travel/statistics & numerical data , Algorithms , COVID-19/epidemiology , COVID-19/prevention & control , Computational Biology , Human Activities/statistics & numerical data , Humans , SARS-CoV-2 , Social Media/statistics & numerical data , United Kingdom
15.
Toxins (Basel) ; 13(7)2021 07 08.
Article in English | MEDLINE | ID: covidwho-1302455

ABSTRACT

Cyanobacteria are ubiquitous photosynthetic microorganisms considered as important contributors to the formation of Earth's atmosphere and to the process of nitrogen fixation. However, they are also frequently associated with toxic blooms, named cyanobacterial harmful algal blooms (cyanoHABs). This paper reports on an unusual out-of-season cyanoHAB and its dynamics during the COVID-19 pandemic, in Lake Avernus, South Italy. Fast detection strategy (FDS) was used to assess this phenomenon, through the integration of satellite imagery and biomolecular investigation of the environmental samples. Data obtained unveiled a widespread Microcystis sp. bloom in February 2020 (i.e., winter season in Italy), which completely disappeared at the end of the following COVID-19 lockdown, when almost all urban activities were suspended. Due to potential harmfulness of cyanoHABs, crude extracts from the "winter bloom" were evaluated for their cytotoxicity in two different human cell lines, namely normal dermal fibroblasts (NHDF) and breast adenocarcinoma cells (MCF-7). The chloroform extract was shown to exert the highest cytotoxic activity, which has been correlated to the presence of cyanotoxins, i.e., microcystins, micropeptins, anabaenopeptins, and aeruginopeptins, detected by molecular networking analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data.


Subject(s)
Cyanobacteria , Harmful Algal Bloom , Lakes/microbiology , Bacterial Toxins/analysis , Bacterial Toxins/toxicity , COVID-19/epidemiology , Cell Line , Cell Survival/drug effects , Cyanobacteria/genetics , DNA, Bacterial/analysis , Environmental Monitoring , Human Activities , Humans , Italy/epidemiology , Microcystis , Pandemics , SARS-CoV-2 , Satellite Imagery
16.
Microb Ecol ; 82(2): 365-376, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1293356

ABSTRACT

The unprecedented COVID-19 pandemic has had major impact on human health worldwide. Whilst national and international COVID-19 lockdown and travel restriction measures have had widespread negative impact on economies and mental health, they may have beneficial effect on the environment, reducing air and water pollution. Mass bathing events (MBE) also known as Kumbh Mela are known to cause perturbations of the ecosystem affecting resilient bacterial populations within water of rivers in India. Lockdowns and travel restrictions provide a unique opportunity to evaluate the impact of minimum anthropogenic activity on the river water ecosystem and changes in bacterial populations including antibiotic-resistant strains. We performed a spatiotemporal meta-analysis of bacterial communities of the Godavari River, India. Targeted metagenomics revealed a 0.87-fold increase in the bacterial diversity during the restricted activity of lockdown. A significant increase in the resilient phyla, viz. Proteobacteria (70.6%), Bacteroidetes (22.5%), Verrucomicrobia (1.8%), Actinobacteria (1.2%) and Cyanobacteria (1.1%), was observed. There was minimal incorporation of allochthonous bacterial communities of human origin. Functional profiling using imputed metagenomics showed reduction in infection and drug resistance genes by - 0.71-fold and - 0.64-fold, respectively. These observations may collectively indicate the positive implications of COVID-19 lockdown measures which restrict MBE, allowing restoration of the river ecosystem and minimise the associated public health risk.


Subject(s)
Bacteria/isolation & purification , Communicable Disease Control/legislation & jurisprudence , Ecosystem , Rivers/microbiology , Bacteria/classification , COVID-19/epidemiology , COVID-19/prevention & control , Drug Resistance, Bacterial , Environmental Monitoring , Hinduism , Human Activities , India/epidemiology , Principal Component Analysis
17.
Int J Environ Res Public Health ; 18(13)2021 06 26.
Article in English | MEDLINE | ID: covidwho-1288864

ABSTRACT

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO2 and PM2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO2, PM2.5, park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO2 and PM2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Epidemics , Air Pollutants/analysis , Air Pollutants/toxicity , Air Pollution/adverse effects , Air Pollution/analysis , Environmental Exposure/analysis , Human Activities , Humans , Mortality , Particulate Matter/analysis , Particulate Matter/toxicity , SARS-CoV-2
18.
Nat Commun ; 12(1): 2415, 2021 04 27.
Article in English | MEDLINE | ID: covidwho-1205443

ABSTRACT

The COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have been impacted due to severe restrictions on human movements and changes in consumption. Here, we quantify and map global change in marine traffic during the first half of 2020. There were decreases in 70.2% of Exclusive Economic Zones but changes varied spatially and temporally in alignment with confinement measures. Global declines peaked in April, with a reduction in traffic occupancy of 1.4% and decreases found across 54.8% of the sampling units. Passenger vessels presented more marked and longer lasting decreases. A regional assessment in the Western Mediterranean Sea gave further insights regarding the pace of recovery and long-term changes. Our approach provides guidance for large-scale monitoring of the progress and potential effects of COVID-19 on vessel traffic that may subsequently influence the blue economy and ocean health.


Subject(s)
COVID-19/epidemiology , Oceans and Seas , Ships/statistics & numerical data , COVID-19/prevention & control , Ecosystem , Human Activities , Humans , SARS-CoV-2 , Ships/classification , Ships/economics
19.
J Acoust Soc Am ; 149(3): 1796, 2021 03.
Article in English | MEDLINE | ID: covidwho-1159104

ABSTRACT

While studies of urban acoustics are typically restricted to the audio range, anthropogenic activity also generates infrasound (<20 Hz, roughly at the lower end of the range of human hearing). Shutdowns related to the COVID-19 pandemic unintentionally created ideal conditions for the study of urban infrasound and low frequency audio (20-500 Hz), as closures reduced human-generated ambient noise, while natural signals remained relatively unaffected. An array of infrasound sensors deployed in Las Vegas, NV, provides data for a case study in monitoring human activity during the pandemic through urban acoustics. The array records a sharp decline in acoustic power following the temporary shutdown of businesses deemed nonessential by the state of Nevada. This decline varies spatially across the array, with stations close to McCarran International Airport generally recording the greatest declines in acoustic power. Further, declines in acoustic power fluctuate with the time of day. As only signals associated with anthropogenic activity are expected to decline, this gives a rough indication of periodicities in urban acoustics throughout Las Vegas. The results of this study reflect the city's response to the pandemic and suggest spatiotemporal trends in acoustics outside of shutdowns.


Subject(s)
Acoustics/instrumentation , COVID-19/prevention & control , Environmental Monitoring , Human Activities , Cities , Communicable Disease Control , Environmental Monitoring/instrumentation , Environmental Monitoring/methods , Humans , Nevada , Noise , Pandemics , SARS-CoV-2
20.
BMC Public Health ; 21(1): 604, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-1158201

ABSTRACT

BACKGROUND: The effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting. METHODS: We employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China's business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. Two-sample t-test was performed in order to affirm that the pick values in different scenarios are different. RESULTS: We found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50 and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high. CONCLUSIONS: Business resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.


Subject(s)
COVID-19/prevention & control , Disease Outbreaks/prevention & control , Pandemics , COVID-19/epidemiology , China/epidemiology , Communicable Disease Control , Human Activities/statistics & numerical data , Humans , Models, Statistical , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL