Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting

Kaplan, Michael; Kneifel, Charles; Orlikowski, Victor; Dorff, James; Newton, M.A.; Howard, Andy J.; Shinn, Don; Bishawi, Muath; Chidyagwai, Simbarashe; Balogh, Péter; Randles, Amanda

doi:10.1109/mcse.2020.3024062

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…AI and cloud computing have been effectively used in drug research and development and made important contributions to epidemic prevention and control [ 247 , 248 ]. Researchers use AI and big data database to create models to predict the development and spread of the COVID-19, help medical staff improve prevention and control measures, and slow down the further spread of the epidemic [ 249 , 250 ]. AI plays an irreplaceable role in improving the efficiency of virus detection and speeding up drug development [ 251 , 252 ].…”

Section: Applications and Impacts Of Digital Technology In Fighting Covid-19mentioning

confidence: 99%

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Wang

Zhang

et al. 2021

IJERPH

151

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Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China’s success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

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Section: Applications and Impacts Of Digital Technology In Fighting Covid-19mentioning

confidence: 99%

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Wang

Zhang

et al. 2021

IJERPH

151

View full text Add to dashboard Cite

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“…Kaplan et al. ( 2020 ) worked on adjusting the cloud architecture to accommodate the needs of the problem rather than manipulating the problem itself to make it suitable for the platform and its limitations. So, they are able to be prepared to rapidly deploy the model and to rapidly implement the model at scale for COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Distributed messaging and light streaming system for combating pandemics

Özgüven

Eken

2021

J Ambient Intell Human Comput

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Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study on spatial analysis of the COVID-19 geo-tagged Twitter dataset. In this system, three of the major components are the translation of tweets matching with user-defined bounding boxes, name entity recognition in tweets, and skyline queries. Apache Pulsar addresses all these components in this paper. With the proposed system, end-users have the capability of getting COVID-19 related information within foreign regions, filtering/searching location, organization, person, and miscellaneous based tweets, and performing skyline based queries. The evaluation of the proposed system is done based on certain characteristics and performance metrics. The study differs greatly from other studies in terms of using distributed computing and big data technologies on spatial data to combat COVID-19. It is concluded that Pulsar is designed to handle large amounts of long-term on disk persistence.

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“…It is color intensity change method with sensors. The detection of kidney disease is tool kit [19] which monitors the creatinine, protein concentrate of urine and display immediately which helps to cure in early stages. The model is designed in such a way that is user friendly which monitors and display amount of protein leaked in urine.…”

Section: Related Workmentioning

confidence: 99%

COVID-19: Role of Deep Learning and Cloud Through Identification of Kidney, Pancreas and Intestine

Arulmozhiselvan

Jayasri

2021

Advances in Parallel Computing Technologies and Applications

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Worldwide human health and economic has been affected due to the ongoing pandemic of corona virus (COVID-19). The major COVID-19 challenges are prevention, monitoring and FDA approved vaccines. IOT and cloud computing play vital role in epidemic prevention and blocking COVID-19 transmission. Mostly lungs and hearts are affected. Other than lungs many parts are affected which are not considered as prominent conversational cue. In this paper, we have proposed smart system that is effective through detection of pancreas, kidney and intestine. It detects acute pancreatitis, protein leak, microscopic blood leak, post infectious dysmotility and gastrointestinal bleeding. The data from the edge devices are collected and mapped into the cloud layer. The cloud consists of COVID-19 patients medical records which compare the user data with the existing patient records. Once the data matches it sends warning message to the user regarding the result of affected parts. Based on the result from KPI system, it analyzes with all data and using deep Convolutional Neural Network (CNN) it classifies whether the pancreas, kidney and intestine are affected or not due to COVID-19.

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Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting

Cited by 14 publications

References 6 publications

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Distributed messaging and light streaming system for combating pandemics

COVID-19: Role of Deep Learning and Cloud Through Identification of Kidney, Pancreas and Intestine

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