Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks

Background Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. Objective We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. Methods We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media–related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Results A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. Conclusions The applications of social media and digital technologies in addressing COVID-19 vaccination–related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

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“…Unsupervised self-organizing map, recurrent neural network, and Stochastic Mixture Density Network [ 147 ]…”

Section: Resultsmentioning

confidence: 99%

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

et al. 2023

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“…Additionally, using models that apply convolutional neural networks and recurring neural networks, applications can be created to predict vaccination patterns in the future [89]. Deterministic and stochastic recurrent neural networks were used to predict the geographic spreading of the active virus using unsupervised learning methods so as to plan vaccine distribution among the USA, as a case study [90].…”

Section: Applying Deep Learning Algorithms In Healthcarementioning

confidence: 99%

“…The multivariate CNN algorithm outperformed the LSTM in terms of validation accuracy and forecasting consistency. CNN has been suggested for long-term forecasting in the absence of seasonality and periodic patterns in time series datasets [90]. The paper mentioned that DL techniques have a significant impact on early detection of COVID-19 with high accuracy rate.…”

Section: Applying Deep Learning Algorithms In Healthcarementioning

confidence: 99%

A Review of Deep Learning Algorithms and Their Applications in Healthcare

et al. 2022

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Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the colorization of black and white images, the addition of sound to silent films, pixel restoration, and deep dreaming. As a review, this paper aims to categorically cover several widely used deep learning algorithms along with their architectures and their practical applications: backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. In addition, challenges of deep learning are also presented in this paper, such as AutoML-Zero, neural architecture search, evolutionary deep learning, and others. The pros and cons of these algorithms and their applications in healthcare are explored, alongside the future direction of this domain. This paper presents a review and a checkpoint to systemize the popular algorithms and to encourage further innovation regarding their applications. For new researchers in the field of deep learning, this review can help them to obtain many details about the advantages, disadvantages, applications, and working mechanisms of a number of deep learning algorithms. In addition, we introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic. By presenting many challenges of deep learning in one section, we hope to increase awareness of these challenges, and how they can be dealt with. This could also motivate researchers to find solutions for these challenges.

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“…One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of the active virus at the time of vaccination. In the US, Davahli et al [54] employed a self-organizing map, long short-term memory (LSTM) model, recurrent neural network model, and stochastic mixture density network model to predict the behavior of the COVID-19 pandemic. They concluded that the deterministic LSTM model could predict the geographic spread of active virus with considerable accuracy.…”

Section: Vaccine Resource Managementmentioning

confidence: 99%

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Mollalo

Mohammadi

Mavaddati

et al. 2021

IJERPH

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Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.

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Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks

Cited by 14 publications

References 32 publications

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

A Review of Deep Learning Algorithms and Their Applications in Healthcare

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

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