A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks

Paul, Swarna Kamal; Jana, Saikat Kumar; Bhaumik, Parama

doi:10.1101/2020.04.17.20069898

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…Authors in Ref. [147] forecasted global pandemic outbreak by using multivariate spatiotemporal model based on convolutional LSTM framework. They used the data of Italy and USA, and transformed spatial features into clusters.…”

Section: Covid-19 Outbreak Forecasting Modelsmentioning

confidence: 99%

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Rasheed

Jamıl

Hameed

et al. 2021

Interdiscip Sci Comput Life Sci

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Graphic abstract The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

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Section: Covid-19 Outbreak Forecasting Modelsmentioning

confidence: 99%

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Rasheed

Jamıl

Hameed

et al. 2021

Interdiscip Sci Comput Life Sci

View full text Add to dashboard Cite

show abstract

“…Beyond conventional ML-based approaches, advanced DL techniques have also been used to estimate future cases. Paul et al [136] suggested a convolutional LSTM-based multivariate spatiotemporal model to forecast the pandemic outbreak at the world-level. The proposed framework converted the spatial features into groups of temporal/non-temporal component-based 2D images and used data from USA and Italy to train the network.…”

Section: Ai-based Approaches For Covid-19mentioning

confidence: 99%

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Rasheed

Jamıl

Hameed

et al. 2020

Chaos, Solitons & Fractals

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Highlights The current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak. This paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. Investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. The impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. The investigation of this paper reveals several AI-based approaches that have been proposed as potential ways to help, with the COVID-19 pandemic, covering everything from initial diagnoses via image diagnostics up to the presentation of models that help to understand the spread of COVID-19 and identify potential new outbreak areas.

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“…All the considered papers explicitly state the object of study, albeit with different fortune. For example, 12 of them [37,41,47,48,59,62,69,[120][121][122][123][124] did this only in an implicit way, by distributing the information throughout the text. Only [38] and [125] did this in a partial manner, not mentioning the variables to predict.…”

Section: A Problem Descriptionmentioning

confidence: 99%

COVID-19 forecasting with deep learning: a distressing survey

Gutiérrez¹,

Medrano²,

Aznarte³

2021

Preprint

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<div>This document pretends to provide an overview about the lights and shadows on the latest trends in this specific area.</div><div>Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this field within the last months has become quite large.</div><div>After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened.</div><div>Most of the analysed papers did not meet the common quality standards of scientific work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.</div>

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A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks

Cited by 8 publications

References 13 publications

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

COVID-19 forecasting with deep learning: a distressing survey

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