COVID-19 Real Time Impact Analysis India vs USA

Author(s):  
Govind Agarwal ◽  
Loveleen Gaur ◽  
Ankur Singh Bist
Keyword(s):  
Author(s):  
Yasmina Maizi ◽  
Ygal Bendavid

With the fast development of IoT technologies and the potential of real-time data gathering, allowing decision makers to take advantage of real-time visibility on their processes, the rise of Digital Twins (DT) has attracted several research interests. DT are among the highest technological trends for the near future and their evolution is expected to transform the face of several industries and applications and opens the door to a huge number of possibilities. However, DT concept application remains at a cradle stage and it is mainly restricted to the manufacturing sector. In fact, its true potential will be revealed in many other sectors. In this research paper, we aim to propose a DT prototype for instore daily operations management and test its impact on daily operations management performances. More specifically, for this specific research work, we focus the impact analysis of DT in the fitting rooms’ area.


2020 ◽  
Vol 12 (2) ◽  
pp. 726 ◽  
Author(s):  
Stefano de Luca ◽  
Roberta Di Pace ◽  
Silvio Memoli ◽  
Luigi Pariota

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.


2020 ◽  
Author(s):  
Ashish Kalraiya ◽  
Ben Walker ◽  
Shiron Rajendran ◽  
Sayinthen Vivekanantham ◽  
Danny Sharpe ◽  
...  

BACKGROUND Covid-19 is exacerbating pre-existing pressures on healthcare systems. Frontline staff are relying more than usual on effective logistics and infrastructure to deliver patient care, for example provision of PPE, stock, facilities and equipment. Staff must adapt their ways of working in response to new challenges. Traditional communication channels within hospitals are often inefficient and not digitised, preventing healthcare organisations from adequately supporting staff and providing efficient solutions to problems. OBJECTIVE This study deployed the MediShout mobile phone application (app) to capture real-time data, on problems with logistics and infrastructure occurring in hospitals during the Covid-19 pandemic. The main objectives were to determine whether; healthcare staff would use the app, reporting led to immediate improvements, and data-collection could drive long-term transformational change and improve responses to future pandemics. METHODS The app was used by staff to report issues with logistics and infrastructure across two hospital emergency departments (EDs) at Imperial College Healthcare Trust, UK. These reports were acted upon by senior physicians and nurses, operational managers and service helpdesks. Data was collected from the start of the first peak of Covid-19 in the UK, between March and April 2020. Data from each report were retrospectively analysed across multiple categories, including problem description and time of submission. To gauge the impact of each issue on clinical care, reports were scored against an impact scoring tool using a modified version of the World Health Organisation’s ‘quality of care’ definition. RESULTS During this study, 94 reports were submitted. Reporting peaks were observed at times corresponding to clinical handovers. Peaks were also observed when changes had occurred to existing processes within the EDs. Impact analysis highlighted that every report sent had ‘impact’ or ‘significant impact’ on various aspects of care, including efficiency, patient safety and timely treatment. CONCLUSIONS The MediShout app captured valuable real-time data from frontline staff during the peak of Covid-19. Staff readily adopted the digital technology as it provided a more efficient way to resolve issues. This enabled hospitals to better allocate scarce resources, such as PPE, to those who needed it most. This study suggests listening to the voice of frontline staff during times of crisis allows more effective responses. Capturing data during pandemics is critical for healthcare organisations to learn lessons and maintain control. During this study, it was established that most problems occurred due to changes in practice, such as dividing EDs into Covid-19 and non-Covid-19 zones, rather than increased caseload. Logistical and infrastructure issues were categorised as being “material” (stock, equipment, medicines, or estates and facilities) or “workflow” (task-management, new ways of working, infection control and communication) in nature. This provides healthcare organisations with a methodical tool for risk-assessing and coordinating future pandemic responses. CLINICALTRIAL n/a


2020 ◽  
Vol 08 (11) ◽  
pp. E1545-E1552
Author(s):  
Jamie Catlow ◽  
Linda Sharp ◽  
Adetayo Kasim ◽  
Liya Lu ◽  
Matthew Brookes ◽  
...  

Abstract Background and study aims Colonoscopists with low polyp detection have higher post colonoscopy colorectal cancer incidence and mortality rates. The United Kingdom’s National Endoscopy Database (NED) automatically captures patient level data in real time and provides endoscopy key performance indicators (KPI) at a national, endoscopy center, and individual level. Using an electronic behavior change intervention, the primary objective of this study is to assess if automated feedback of endoscopist and endoscopy center-level optimal procedure-adjusted detection KPI (opadKPI) improves polyp detection performance. Methods This multicenter, prospective, cluster-randomized controlled trial is randomizing NHS endoscopy centres to either intervention or control. The intervention is targeted at independent colonoscopists and each center’s endoscopy lead. The intervention reports are evidence-based from endoscopist qualitative interviews and informed by psychological theories of behavior. NED automatically creates monthly reports providing an opadKPI, using mean number of polyps, and an action plan. The primary outcome is opadKPI comparing endoscopists in intervention and control centers at 9 months. Secondary outcomes include other KPI and proximal detection measures at 9 and 12 months. A nested histological validation study will correlate opadKPI to adenoma detection rate at the center level. A cost-effectiveness and budget impact analysis will be undertaken. Conclusion If the intervention is efficacious and cost-effective, we will showcase the potential of this learning health system, which can be implemented at local and national levels to improve colonoscopy quality, and demonstrate that an automated system that collects, analyses, and disseminates real-time clinical data can deliver evidence- and theory-informed feedback.


2020 ◽  
Vol 35 (1) ◽  
pp. 76-82
Author(s):  
YingYing Yew ◽  
Pedro Arcos González ◽  
Rafael Castro Delgado

AbstractIntroduction:The Richter Scale measures the magnitude of the seismic activity for an earthquake; however, it does not quantify the humanitarian need at the point of impact. This poses a challenge for humanitarian stakeholders in decision and policy making, especially in risk reduction, response, recovery, and reconstruction. The new disaster metrics tool titled “The YEW Disaster Severity Index” (DSI) was developed and presented at the 2017 World Congress of Disaster and Emergency Medicine, May 2017, Toronto, Canada. It uses a median score of three for vulnerability and exposure indicators, a median score percentage of 100%, and medium YEW DSI scoring of four to five as baseline, indicating the ability to cope within local capacity. Therefore, scoring more than baseline coping capacity indicates that external assistance is needed. This special real-time report was presented at the 2nd National Pre-Hospital Care Conference and Championship, October 2018, Malaysia.Report:The aim of this analysis is to present the real-time humanitarian impact and response to the 2018 earthquake and tsunami at Donggala and Palu, Sulawesi in Indonesia using the new disaster metrics YEW DSI. Based on the earthquake (measuring 7.7 on the Richter Scale) and tsunami at Donggala, the humanitarian impact calculated on September 29, 2018 scored 7.4 High in the YEW DSI with 11 of the total 17 indicators scoring more than the baseline coping capacity. The same YEW DSI score of 7.4 was scored on the earthquake and tsunami at Palu, with 13 of the total 17 indicators scoring more than baseline ability to cope within local capacity. Impact analysis reports were sent to relevant authorities on September 30, 2018.Discussion & Conclusion:A State of Emergency was declared for a national response, which indicated an inability to cope within the local capacity, shown by the YEW DSI. The strong correlation between the earthquake magnitude, intensities, and the humanitarian impact at Donggala and Palu reported could be added into the science of knowledge in prehospital care and disaster medicine research and practice. As a conclusion, the real-time disaster response was found to be almost an exact fit with the YEW DSI indicators, demonstrating the inability to cope within the local capacity.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Tianqi Zhang ◽  
Lishan Sun ◽  
Liya Yao ◽  
Jian Rong

This paper proposed a new method to describe, compare, and classify the traffic congestion points in Beijing, China, by using the online map data and further revealed the relationship between traffic congestion and land use. The data of the point of interest (POI) and the real-time traffic was extracted from an electronic map of the area in the fourth ring road of Beijing. The POIs were quantified based on the architectural area of the land use; the congestion points were identified based on real-time traffic. Then, the cluster analysis using the attributes of congestion time was conducted to identify the main traffic congestion areas. The result of a linear regression analysis between the congestion time and the land use showed that the influence of the high proportion of commercial land use on the traffic congestion was significant. Also, we considered five types of land use through performing a linear regression analysis between the congestion time and the ratio of four types of land use. The results showed that the reasonable ratio of land use types could efficiently reduce congestion time. This study makes contributions to the policy-making of urban land use.


2018 ◽  
Vol 49 (3) ◽  
pp. 1321-1333
Author(s):  
Jun Liu ◽  
Tianshu Li ◽  
Shukai Duan ◽  
Lidan Wang

Sign in / Sign up

Export Citation Format

Share Document