On-site measurement of tracer gas transmission between horizontal adjacent flats in residential building and cross-infection risk assessment

Abstract Rationale: The spread of severe acute respiratory syndrome coronavirus-2 has suspended many non-COVID-19 related research activities. Where restarting research activities is permitted, investigators need to evaluate the risks and benefits of resuming data collection and adapt procedures to minimize risk. Objectives: In the context of the multicountry Household Air Pollution Intervention (HAPIN) trial, we developed a framework to assess the risk of each trial activity and to guide protective measures. Our goal is to maximize integrity of reseach aims while minimizing infection risk based on the latest understanding of the virus. Methods: We drew on a combination of expert consultations, risk assessment frameworks, institutional guidance and literature to develop our framework. We then systematically graded clinical, behavioral, laboratory and field environmental health research activities in four countries for both adult and child subjects using this framework. Results: Our framework assesses risk based on staff proximity to the participant, exposure time between staff and participants, and potential aerosolization while performing the activity. One of of four risk levels, from minimal to unacceptable, is assigned and guidance on protective measures is provided. Those activities which can potentially aerosolize the virus are deemed the highest risk. Conclusions: By applying a systematic, procedure-specific approach to risk assessment for each trial activity, we can compare trial activities using the same criteria. This approach allows us to protect our participants and research team and to uphold our ability to deliver on the research commitments we have made to our participants, local communities, and funders. The trial is registered with clinicaltrials.gov (NCT02944682).

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DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

10.20944/preprints202009.0404.v1 ◽

2020 ◽

Author(s):

Mahdi Rezaei ◽

Mohsen Azarmi

Keyword(s):

Risk Assessment ◽

High Risk ◽

World Health Organisation ◽

Infection Risk ◽

Superior Performance ◽

World Health ◽

Mapping Technique ◽

People Detection ◽

Social Distancing ◽

Risk Zones

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

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Influence of human breathing modes on airborne cross infection risk

Building and Environment ◽

10.1016/j.buildenv.2016.07.005 ◽

2016 ◽

Vol 106 ◽

pp. 340-351 ◽

Cited By ~ 19

Author(s):

J.M. Villafruela ◽

I. Olmedo ◽

J.F. San José

Keyword(s):

Infection Risk ◽

Cross Infection

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The Risk of Cross Infection in the Emergency Department: A Simulation Study

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2018.61 ◽

2018 ◽

Vol 39 (6) ◽

pp. 688-693 ◽

Cited By ~ 7

Author(s):

Vicki Stover Hertzberg ◽

Yuke A. Wang ◽

Lisa K. Elon ◽

Douglas W. Lowery-North

Keyword(s):

Emergency Department ◽

Health Workers ◽

Detection System ◽

Infection Risk ◽

Chief Complaint ◽

Cross Infection ◽

Care Staff ◽

Health Concern ◽

Influenza Outbreak ◽

Differential Risk

OBJECTIVESThe risk of cross infection in a busy emergency department (ED) is a serious public health concern, especially in times of pandemic threats. We simulated cross infections due to respiratory diseases spread by large droplets using empirical data on contacts (ie, close-proximity interactions of ≤1m) in an ED to quantify risks due to contact and to examine factors with differential risks associated with them.DESIGNProspective study.PARTICIPANTSHealth workers (HCWs) and patients.SETTINGA busy ED.METHODSData on contacts between participants were collected over 6 months by observing two 12-hour shifts per week using a radiofrequency identification proximity detection system. We simulated cross infection due to a novel agent across these contacts to determine risks associated with HCW role, chief complaint category, arrival mode, and ED disposition status.RESULTSCross-infection risk between HCWs was substantially greater than between patients or between patients and HCWs. Providers had the least risk, followed by nurses, and nonpatient care staff had the most risk. There were no differences by patient chief complaint category. We detected differential risk patterns by arrival mode and by HCW role. Although no differential risk was associated with ED disposition status, 0.1 infections were expected per shift among patients admitted to hospital.CONCLUSIONThese simulations demonstrate that, on average, 11 patients who were infected in the ED will be admitted to the hospital over the course of an 8-week local influenza outbreak. These patients are a source of further cross-infection risk once in the hospital.Infect Control Hosp Epidemiol 2018;39:688–693

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Residential Building Models for Seismic Risk Assessment at the Historic Downtown of Mexico City

International Journal of Architectural Heritage ◽

10.1080/15583058.2020.1855680 ◽

2020 ◽

pp. 1-18

Author(s):

L. Gerardo F. Salazar ◽

Tiago Miguel Ferreira

Keyword(s):

Risk Assessment ◽

Mexico City ◽

Seismic Risk ◽

Residential Building ◽

Seismic Risk Assessment ◽

Building Models

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Modeling the Residential Building Inventory in South America for Seismic Risk Assessment

Earthquake Spectra ◽

10.1193/101915eqs155dp ◽

2017 ◽

Vol 33 (1) ◽

pp. 299-322 ◽

Cited By ~ 19

Author(s):

Catalina Yepes-Estrada ◽

Vitor Silva ◽

Jairo Valcárcel ◽

Ana Beatriz Acevedo ◽

Nicola Tarque ◽

...

Keyword(s):

Risk Assessment ◽

South America ◽

Structural Characteristics ◽

Construction Materials ◽

Residential Building ◽

Exposure Model ◽

Earthquake Risk ◽

Building Stock ◽

Building Inventory ◽

Andean Countries

This study presents an open and transparent exposure model for the residential building stock in South America. This model captures the geographical distribution, structural characteristics (including information about construction materials, lateral load resisting system, range of number of stories), average built-up area, replacement cost, expected number of occupants, and number of dwellings and buildings. The methodology utilized to develop this model was based on national population and housing statistics and expert judgment from dozens of local researchers and practitioners. This model has been developed as part of the South America Risk Assessment (SARA) project led by the Global Earthquake Model (GEM), and it can be used to perform earthquake risk analyses. It is available at different geographical scales for seven Andean countries: Argentina, Bolivia, Chile, Colombia, Ecuador, Peru, and Venezuela (DOI: 10.13117/GEM. DATASET.EXP.ANDEAN-v1.0).

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