Evaluation of the exposure risk of SARS-CoV-2 in different hospital environment

Ge, Xin; Pu, Ying; Liao, Ce Heng; Huang, Wen Fen; Zeng, Qi; Zhou, Hui; Yi, Bin; Wang, Aimin; Dou, Qing Ya; Zhou, Peng Cheng; Chen, Huiling; Liu, Hui Xia; Xu, Di; Chen, Xiang; Huang, Xun

doi:10.1016/j.scs.2020.102413

Cited by 63 publications

(44 citation statements)

References 43 publications

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“…The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and first reported in December 2019 in Wuhan city of China, has soon become a new public health concern across the world ( Ge et al, 2020 ; Jin et al, 2020 ; Rumpler, Venkataraman, & Göransson, 2020 ; Sun & Zhai, 2020 ). The virus poses serious potential threats to the medical protection system all over the world ( European Centre for Disease Prevention & Control, 2020 ; World Health Organization, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

121

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Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29, 2021, there have been 100, 819, 363 confirmed cases and 2, 176, 159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling has appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R 2 values (for cases, R 2 = 0.961; for deaths, R 2 = 0.962), compared to GWR’s Adj. R 2 values (for cases, R 2 = 0.954; for deaths, R 2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since COVID-19 is not the first time we are facing public health emergency, the findings of the present research therefore could be used as a reference for policy designing and effective decision making.

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Section: Introductionmentioning

confidence: 99%

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

121

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“…Wearing a mask is essential, particularly for those people who are at a greater risk of severe illness from COVID-19 diseases. It is found that the spread of COVID-19 is mainly among people who are in immediate contact with one another (nearly about 6 feet), it can be spread by people who do not have symptoms and are unaware of the fact that they are infected ( Ge et al, 2020 ). So Centers for Disease Control and Prevention (CDC) 1 recommended all people 2 years of age and older to wear a mask in public areas especially when other social distancing ( Sun & Zhai, 2020 ) measures are difficult to maintain.…”

Section: Introductionmentioning

confidence: 99%

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Nagrath

Jain

Madan

et al. 2021

Sustainable Cities and Society

349

133

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“…In Xiangya Hospital (H-X), Chenzhou Second People’s Hospital (H-C), and Shaoyang Central Hospital (H-S), the exposure risk to SARS-CoV-2 was evaluated by Ge et al ( 2020 ). All air samples were collected for 30 min using the NIOSH BC251 bioaerosol sampler (National Institute for Occupational Safety and Health) and air pumps (XR5000, SKC) at 3.5 L/min.…”

Section: Air Sampling For Sars-cov-2 Detectionmentioning

confidence: 99%

“…Rooms with viral particles in the air also presented surface contamination Chia et al ( 2020 ) 33 air samples were collected for 30 min using the NIOSH BC 251 bioaerosol sampler (National Institute for Occupational Safety and Health) with air pumps (XR5000, SKC) at 3.5 L/min. The NIOSH sampler segregates air sample into large (≥ 4 μm), medium (1–4 μm), and small (< 1 μm) particles 7.7% and 82.6% of samples were positive for SARS-CoV-2 in COVID-19 respiratory investigation wards and ICUs with confirmed COVID-19 patients, respectively Ge et al ( 2020 ) 28 samples were collected using 2-stage cyclone samplers (NIOSH BC 251), filter cassette containing a Teflon® filter ( D = 37 mm and 2 μm porosity), and a sampling pump (PCXR-4, SKC, Eighty-Four, PA) at a flow rate of 3.5 L/min for 315–360 min All samples were negative for SARS-CoV-2 Lane et al ( 2020 ) Liquid impactors Air samples were collected in COVID-19 isolation rooms used for three patients with severe pneumonia, using a SKC BioSampler at 12.5 L/min and at 10 L/min, both for 20 min, placed 1.2 m from the floor, and at a 1-m distance from patients All samples were negative for SARS-CoV-2 Ahn et al ( 2020 ) 10 air samples were collected during 1 h, using a vacuum pump at 1.5 L/min, in SKC sterile standard midget impingers at a height of 1.5–1.8 m from the floor and at a distance of 2–5 m from beds of patients with severe and critical symptoms All samples were negative for SARS-CoV-2 Faridi et al ( 2020 ) Air samples were collected using a SASS 2300 wetted wall cyclone sampler (Research International, Inc., https://www.resrchintl.com ) at 300 L/min for 30 min SARS-CoV-2 was detected in air at a 4-m distance from patients Guo et al ( 2020 ) Air samples were collected using a high-volume WA 400 Portable viral aerosol sampler (Dingblue Tech, Inc.), at 400 L/min for 15 min SARS-CoV-2 was detected in 01 out of 02 air samples Jin et al ( 2020 ) Air samples were collected using the SKC impinger-type biosampler at a flow rate of 12 L/min, placed 1.5 m from the floor 02 out of 14 air samples were positive for SARS-CoV-2 Kenarkoohi et al ( 2020 ) 135 air samples were collected using an impinger sampler (BIO-Capturer-6, Bioenrichment Co., Hangzhou, China), for 30 min at 80 L/min, and placed 1.0–1.5 m from the floor All samples were negative for SARS-CoV-2 Li et al ( 2020 ) 26 air sam...…”

Section: Air Sampling For Sars-cov-2 Detectionmentioning

confidence: 99%

SARS-CoV-2: a systematic review of indoor air sampling for virus detection

Borges

Nakada

Maniero

et al. 2021

Environ Sci Pollut Res

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In a post-pandemic scenario, indoor air monitoring may be required seeking to safeguard public health, and therefore well-defined methods, protocols, and equipment play an important role. Considering the COVID-19 pandemic, this manuscript presents a literature review on indoor air sampling methods to detect viruses, especially SARS-CoV-2. The review was conducted using the following online databases: Web of Science, Science Direct, and PubMed, and the Boolean operators “AND” and “OR” to combine the following keywords: air sampler, coronavirus, COVID-19, indoor, and SARS-CoV-2. This review included 25 published papers reporting sampling and detection methods for SARS-CoV-2 in indoor environments. Most of the papers focused on sampling and analysis of viruses in aerosols present in contaminated areas and potential transmission to adjacent areas. Negative results were found in 10 studies, while 15 papers showed positive results in at least one sample. Overall, papers report several sampling devices and methods for SARS-CoV-2 detection, using different approaches for distance, height from the floor, flow rates, and sampled air volumes. Regarding the efficacy of each mechanism as measured by the percentage of investigations with positive samples, the literature review indicates that solid impactors are more effective than liquid impactors, or filters, and the combination of various methods may be recommended. As a final remark, determining the sampling method is not a trivial task, as the samplers and the environment influence the presence and viability of viruses in the samples, and thus a case-by-case assessment is required for the selection of sampling systems.

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Evaluation of the exposure risk of SARS-CoV-2 in different hospital environment

Cited by 63 publications

References 43 publications

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

SARS-CoV-2: a systematic review of indoor air sampling for virus detection

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