Predicting vaccine hesitancy from area‐level indicators: A machine learning approach

Carrieri, Vincenzo; Lagravinese, Raffele; Resce, Giuliano

doi:10.1002/hec.4430

Cited by 24 publications

(16 citation statements)

References 33 publications

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“…The broadening gap between the two vaccines in regions displaying greater interest in "thrombosis" suggests that the VA suspension may have attracted the public's attention to vaccine adverse events related to VA injections, possibly inflating their risk. This evidence is in line with the literature on hesitancy that identifies concerns about the incidence and severity of adverse events as one of the main drivers of the decision to postpone or refuse vaccination (e.g., Brown et al, 2010;Carrieri et al, 2019;Carrieri et al, 2021;Chang, 2018;Qian et al, 2020;Smith et al, 2008). On the other hand, the outbreak's severity likely increased the perceived risk of contracting the disease, in line with previous evidence that outbreaks encourage vaccinations (Oster, 2018;Philipson, 1996).…”

supporting

confidence: 88%

Perceived risk and vaccine hesitancy: Quasi‐experimental evidence from Italy

et al. 2022

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In March 2021, Italian health authorities suspended the Vaxzevria vaccine (VA) for 4 days over reports of very rare blood disorders among recipients. We exploit the quasi‐experimental setting arising from this break to study the drivers of vaccine hesitancy. Before the suspension, the VA vaccination trend followed the same pattern as Pfizer‐Biontech (PB). After the suspension, VA and PB injections started to diverge, with VA daily decreasing by almost 60 doses per 100,000 inhabitants for the following 3 weeks. The resulting vaccination rate was 60 percent lower than the value that would have stemmed from the VA pre‐suspension pattern. We show that the slowdown was weaker and less persistent in regions with higher COVID penetration and steadier and more pronounced in regions displaying greater attention to vaccine side effects as detected through Google searches. The public's interest in vaccine adverse events negatively correlates with COVID cases and deaths across regions.

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supporting

confidence: 88%

Perceived risk and vaccine hesitancy: Quasi‐experimental evidence from Italy

et al. 2022

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“…In order to determine which answers were most associated with a respondent being COVID-19 "vaccine hesitant" we applied a random forest machine learning algorithm, which generates a computational model to predict classification of data based on a discreet variable (e.g., vaccine hesitancy) and other variables [17]. This approach was specifically selected since so many different factors/variables were significantly associated using more traditional statistical methods.…”

Section: Machine Learning Analysismentioning

confidence: 99%

Impact of Local and Demographic Factors on Early COVID-19 Vaccine Hesitancy among Health Care Workers in New York City Public Hospitals

Afzal¹,

Shariff²,

Gutiérrez³

et al. 2022

Vaccines

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Despite the development of several effective vaccines, SARS-CoV-2 continues to spread, causing serious illness among the unvaccinated. Healthcare professionals are trusted sources of information about vaccination, and therefore understanding the attitudes and beliefs of healthcare professionals regarding the vaccines is of utmost importance. We conducted a survey-based study to understand the factors affecting COVID-19 vaccine attitudes among health care professionals in NYC Health and Hospitals, at a time when the vaccine was new, and received 3759 responses. Machine learning and chi-square analyses were applied to determine the factors most predictive of vaccine hesitancy. Demographic factors, education, role at the hospital, perceptions of the pandemic itself, and location of work and residence were all found to significantly contribute to vaccine attitudes. Location of residence was examined for both borough and neighborhood, and was found to have a significant impact on vaccine receptivity. Interestingly, this borough-level data did not correspond to the number or severity of cases in the respective boroughs, indicating that local social or other influences likely have a substantial impact. Local and demographic factors should be strongly considered when preparing pro-vaccine messages or campaigns.

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“…Carrieri et al, ( 2021 ) performed a study based on area level indicators using several machine learning approaches. They identified communities with high risk of vaccine hesitancy based on indicators like waste recycling and employment rate.…”

Section: Related Workmentioning

confidence: 99%

Detecting COVID-19 vaccine hesitancy in India: a multimodal transformer based approach

Borah

2022

J Intell Inf Syst

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COVID-19 has emerged as the greatest threat in recent times, causing extensive mortality and morbidity in the entire world. India is among the highly affected countries suffering severe disruptions due this pandemic. To overcome the adverse effects of COVID-19, vaccination has been identified as the most effective preventive measure globally. However, a growing amount of hesitancy has been observed among the general public regarding the efficacy and possible side-effects of vaccination. Such hesitancy may proved to be the greatest hindrance towards combating this deadly pandemic. This paper introduces a multimodal deep learning method for Indian Twitter user classification, leveraging both content-based and network-based features. To explore the fundamental features of different modalities, improvisations of transformer models, BERT and GraphBERT are utilized to encode the textual and network structure information. The proposed approach thus integrates multiple data representations, utilizing the advances in both transformer based deep learning as well as multimodal learning. Experimental results demonstrates the efficacy of proposed approach over state of the art approaches. Aggregated feature representations from multiple modalities embed additional information that improves the classification results. The findings of the proposed model has been further utilized to perform a study on the dynamics of COVID-19 vaccine hesitancy in India.

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Predicting vaccine hesitancy from area‐level indicators: A machine learning approach

Cited by 24 publications

References 33 publications

Perceived risk and vaccine hesitancy: Quasi‐experimental evidence from Italy

Perceived risk and vaccine hesitancy: Quasi‐experimental evidence from Italy

Impact of Local and Demographic Factors on Early COVID-19 Vaccine Hesitancy among Health Care Workers in New York City Public Hospitals

Detecting COVID-19 vaccine hesitancy in India: a multimodal transformer based approach

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