Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

Ali, G. G. Md. Nawaz; Rahman, Md. Mokhlesur; Hossain, Amjad; Rahman, Md. Shahinoor; Paul, Kamal Chandra; Thill, Jean-Claude; Samuel, Jim

doi:10.3390/healthcare9091110

Cited by 21 publications

(24 citation statements)

References 67 publications

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“…However, the Voronoi map (Theisen polygon) was utilized in only one study [23]. The contribution of 11 articles to the spatial analysis of COVID-19 vaccination was only limited to mapping [24][25][26][27][28][29][30][31][32][33][34]. Although most mapping studies disregarded time components, and used static presentation of data, temporal variations or transmission dynamics were mapped in [24,29,30,[35][36][37].…”

Section: Vaccine Mappingmentioning

confidence: 99%

“…In a spatiotemporal study, Ali et al [24] examined public perceptions of COVID-19 vaccines using choropleth maps. They found that fear sentiment remained unchanged in populous US states, whereas trust sentiment declined slightly in the same states.…”

Section: Vaccine Mappingmentioning

confidence: 99%

“…Root-Bernstein [57] D, R 3 Whitehead, Scott [49] D, R 3 Whitehead, Carr [50] D, R 3 Bauer, Zhang [38] D, R 3 Chakraborty, Sharma [26] D, R 3 Qunaibi, Basheti [33] D, R 3 Lei [58] D 1 Zhou, Zhou [55] D, R, N, M 7 Ali, Rahman [24] D, R 3 Hu, Wang [29] D 1 Grauer, Löwen [28] N, M 4 Krzysztofowicz and Osi ńska-Skotak [23] D, N, M 5…”

Section: Kandula and Shamanmentioning

confidence: 99%

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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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Section: Vaccine Mappingmentioning

confidence: 99%

Section: Vaccine Mappingmentioning

confidence: 99%

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Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Mollalo

Mohammadi

Mavaddati

et al. 2021

IJERPH

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“…Moreover, multiple new variants of the COVID-19 virus that transmit more readily from person to person and change the effectiveness of the vaccines are emerging and circulating in the US and around the world 10 . Additionally, people are hesitant to receive vaccine against COVID-19 infection in the US 11 . Thus, a combination of evidence-based pharmaceutical and non-pharmaceutical public health mitigation measures remains important to curtail viral transmission locally and beyond 12 – 18 .…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Nikparvar

Rahman

Hatami

et al. 2021

Sci Rep

Self Cite

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Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

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“…Despite the overall strength of positive sentiment and the increasing number of Americans who are fully vaccinated, their analysis of social media data from early February to late March 2021 shows that negative sentiment toward COVID-19 vaccines persists among segments of people who are hesitant towards the vaccine. In this study, they have performed sentiment analytics on vaccine tweets, monitored changes in public sentiment over time, contrasted vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explored the influence of maturity of Twitter user-accounts and generated geographic mapping of tweet sentiments (Hossain et al, 2021).…”

mentioning

confidence: 99%

Social Life after Covid-19: a Linguistic Study in light of Shannon-Weaver's Model of Communication

توکل جابر الزغل

2022

مجلة کلیة الآداب جامعة الفیوم

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A lot of people around the world yearn for the day when their lives will return to the better after nearly a year has passed since the emergence of the new Corona virus . This epidemic has radically changed the way people live and their social interaction. It has had a global impact; life has changed a lot after its spread, especially the scientific life where many researchers, scientists and doctors have done many researches for discussing its causes and results. Covid 19 has infected tens of millions of people in the world, killing over 1.5 million people. In addition, prevention measures have paralyzed economy and confused education, entertainment and the rest of the activities that people are familiar with. After the spread of this pandemic, scientific studies and field studies have been increased in various fields to discuss how this virus affected various fields of life such as literature, arts, philosophy, sciences, and social studies. What many scientists expect in 2024 is a return to the lifestyle that all people long for. In this research, the researcher will discuss some aspects of life after the spread of Covid 19 and will shed light on health, social sciences, and field studies through the application of Shannon and Weaver's communication theory which provides a schematic representation of the relationship between the sender, the message, medium of communication, and the recipient to two speeches given by prominent officials and one article written by a unique researcher for portraying life after the pandemic. The chosen theory provides a framework for analyzing how messages are sent and received. It is famous for its ability to explain how messages are mixed and misinterpreted in the process between sending and receiving the message.Thus, the chosen theory will be the main tool for the researcher to communicate speakers' and writers' messages to the whole world.

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Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

Cited by 21 publications

References 67 publications

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Social Life after Covid-19: a Linguistic Study in light of Shannon-Weaver's Model of Communication

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