Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries

Olusanya, Olufunto A.; White, Brianna; Melton, Chad A.; Shaban-Nejad, Arash

doi:10.2196/34363

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…In the near future, our team plans to expand the methods demonstrated in this study into sentiment related to other types of vaccines (eg, human papillomavirus vaccines). We expect these results along with others to be used to develop tools to assist public health officials in monitoring public discourse regarding disease outbreaks, gaining a better understanding of vaccine hesitancy, and developing personalized digital interventions [ 33 , 34 ] and education campaigns.…”

Section: Discussionmentioning

confidence: 99%

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Melton¹,

White²,

Davis³

et al. 2022

J Med Internet Res

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Background The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. Objective Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms—Reddit and Twitter—harvested from January 1, 2020, to March 1, 2022. Methods To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. Results Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population’s expressed sentiments that facilitate digital literacy, health information–seeking behavior, and precision health promotion could aid in clarifying such misinformation.

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Section: Discussionmentioning

confidence: 99%

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Melton¹,

White²,

Davis³

et al. 2022

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, there were transformative tools and technologies implemented to prevent, control, diagnose, communicate, manage, and treat diseases during lockdowns and other tumultuous periods. In addition, wearable smartwatch devices have been adapted to detect physiological responses following COVID-19 vaccination 10 and audio-based digital testing of COVID-19 11 .…”

Section: Introductionmentioning

confidence: 99%

“…These technologies could be telemedicine, artificial intelligence, wearable devices, cloud-based applications, mobile health, big data, and electronic health records. Following the onset of the COVID-19 pandemic, the utility of digital health interventions in combating IDs and NCDs has boldly come into focus [8][9][10] . For example, there were transformative tools and technologies implemented to prevent, control, diagnose, communicate, manage, and treat diseases during lockdowns and other tumultuous periods.…”

Section: Introductionmentioning

confidence: 99%

Mapping digital health ecosystems in Africa in the context of endemic infectious and non-communicable diseases

et al. 2023

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Investments in digital health technologies such as artificial intelligence, wearable devices, and telemedicine may support Africa achieve United Nations (UN) Sustainable Development Goal for Health by 2030. We aimed to characterize and map digital health ecosystems of all 54 countries in Africa in the context of endemic infectious and non-communicable diseases (ID and NCD). We performed a cross-national ecological analysis of digital health ecosystems using 20-year data from the World Bank, UN Economic Commission for Africa, World Health Organization, and Joint UN Programme on HIV/AIDS. Spearman’s rank correlation coefficients were used to characterize ecological correlations between exposure (technology characteristics) and outcome (IDs and NCDs incidence/mortality) variables. Weighted linear combination model was used as the decision rule, combining disease burden, technology access, and economy, to explain, rank, and map digital health ecosystems of a given country. The perspective of our analysis was to support government decision-making. The 20-year trend showed that technology characteristics have been steadily growing in Africa, including internet access, mobile cellular and fixed broadband subscriptions, high-technology manufacturing, GDP per capita, and adult literacy, while many countries have been overwhelmed by a double burden of IDs and NCDs. Inverse correlations exist between technology characteristics and ID burdens, such as fixed broadband subscription and incidence of tuberculosis and malaria, or GDP per capita and incidence of tuberculosis and malaria. Based on our models, countries that should prioritize digital health investments were South Africa, Nigeria, and Tanzania for HIV; Nigeria, South Africa, and Democratic Republic of the Congo (DROC) for tuberculosis; DROC, Nigeria, and Uganda for malaria; and Egypt, Nigeria, and Ethiopia for endemic NCDs including diabetes, cardiovascular disease, respiratory diseases, and malignancies. Countries such as Kenya, Ethiopia, Zambia, Zimbabwe, Angola, and Mozambique were also highly affected by endemic IDs. By mapping digital health ecosystems in Africa, this study provides strategic guidance about where governments should prioritize digital health technology investments that require preliminary analysis of country-specific contexts to bring about sustainable health and economic returns. Building digital infrastructure should be a key part of economic development programs in countries with high disease burdens to ensure more equitable health outcomes. Though infrastructure developments alongside digital health technologies are the responsibility of governments, global health initiatives can cultivate digital health interventions substantially by bridging knowledge and investment gaps, both through technology transfer for local production and negotiation of prices for large-scale deployment of the most impactful digital health technologies.

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Innovation of Digital Health Technologies for Screening and Mitigation of the Mental Health Consequences of Adverse Childhood Experiences: A Scoping Review

White

Prasad

Ammar

et al. 2023

Preprint

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This study presents a scoping review of the use of digital health technologies and artificial intelligence (AI) to screen and mitigate risks and mental health consequences associated with adverse childhood experiences (ACEs) among children and youth. A total of 18 search articles were reviewed thoroughly within four main domains: post-traumatic stress disorder (PTSD), suicidal behavior/ideation, anxiety/depression, and attention deficit hyperactivity disorder (ADHD). Findings from the search suggest that the incorporation of digital health technologies stands to improve the quality of related care provisions for the management of mental health consequences of adverse or traumatic events in childhood including PTSD (5%), suicidal behavior/ideation (22%), anxiety/depression (16%), and ADHD (16%). The use of digital health technologies, machine learning tools, natural learning processing, and AI has a positive and encouraging response in mitigating the risks of ACEs. Under proper legal regulations, security, privacy, and confidentiality assurances, digital technologies could assist in mitigating the effects of ACEs and promoting positive childhood experiences (PCEs) in children and young adults, bolstering resilience, and providing reliable public health resources to serve populations in need.

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Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries

Cited by 3 publications

References 40 publications

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Mapping digital health ecosystems in Africa in the context of endemic infectious and non-communicable diseases

Innovation of Digital Health Technologies for Screening and Mitigation of the Mental Health Consequences of Adverse Childhood Experiences: A Scoping Review

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