Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Infodemic Management and Public Health Emergency Preparedness Capacities - Khyber Pakhtunkhwa, Pakistan, 2024

What is already known about this topic?

The infodemics can increase the burden of outbreaks and emergencies. Many of the studies documented the impact and emergence of the infodemic situation during epidemics or pandemics in recent years. There is limited evidence on the preparedness and readiness of health departments to effectively manage the infodemic situation.

What is added by this report?

This research provides a comprehensive assessment of the health department's capabilities in crisis emergency risk communication and infodemic management, identifying key best practices, challenges and bottlenecks. It contributes to the field by offering a framework and methodologies for evaluating these capacities, aiding in improving infectious diseases outbreak response public health emergency management.

What are the implications for public health practice?

Countries and health departments can benefit from these insights by assessing the level of preparedness and readiness in this field and to implement targeted interventions to enhance their preparedness and response capabilities to misinformation and disinformation.

Deep learning model meets community-based surveillance of acute flaccid paralysis

Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (P < 0.001). By bridging community reporting with health system response, this study offers a scalable solution for enhancing AFP surveillance in low-resource settings. The study is limited in terms of the quality of image data collected, necessitating future work on improving data quality. The establishment of a dedicated platform that facilitates data storage, analysis, and future learning can strengthen data quality. Nonetheless, this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images, with substantial implications for addressing global health challenges and disease eradication strategies.

Digital tools and technologies used in food fortification: A scoping review

Food fortification (i.e., industrial fortification and biofortification) increases the micronutrient content of foods to improve population nutrition. Implementing effective fortification programs requires the generation and use of data to inform decision making. The use of digital tools and technologies (DTTs) for such purposes in broader nutrition programs is growing; however, there is limited consolidation of those used in fortification. This scoping review aimed to identify and describe DTTs used in fortification programs. We searched peer-reviewed and gray literature and conducted 17 stakeholder surveys. We then mapped DTTs identified against the fortification and nutrition data value chains. Of 11,741 articles identified, 158 met the inclusion criteria. From the included articles and stakeholder surveys, 125 DTTs were identified across three categories: software and tooling (n = 58), data and information lifecycle (n = 50), and hardware and infrastructure (n = 17). Gaps were identified in processing, post-harvest storage, aggregation, and transport nodes of the fortification value chain, and data prioritization, translation/dissemination, and decision-making nodes of the nutrition data value chain. DTTs have the potential to address challenges faced by fortification stakeholders to generate and use data to improve program decision making and nutritional impact. Further work is needed to standardize terminology, identify relevant DTTs from other sectors, and explore stakeholder needs.

Descriptive analysis of TikTok content on vaccination in Arabic

The extensive impact of social media on communication of public health information is a growing concern. This is particularly worrying in the context of vaccination. Thus, we investigated the quality of TikTok videos regarding vaccination in Arabic, with examination of the association of video source and content type with the information quality and video engagement metrics. The final sample comprised a total of 129 TikTok videos in Arabic posted between January 2021 and July 2024. Videos were categorized based on the source [healthcare professional (HCPs), lay individuals, media], and content type (COVID-19 vaccination, childhood vaccination, general vaccination, others). We utilized a miniaturized version of the DISCERN instrument (mini-DISCERN) scale to evaluate information quality by two independent raters and assessed video engagement metrics (Likes, Comments, Shares, and Saves). The results indicated a statistically significant discrepancy in information quality, with videos from HCPs and media outlets scoring higher on the mini-DISCERN scale compared to those from lay individuals [mean: (4.818 ± 0.726) vs. (4.053 ± 1.441) vs. (2.003 ± 1.640), P < 0.001]. The highest information quality was found for videos on childhood vaccination, whereas content on COVID-19 vaccination was rated significantly lower on mini-DISCERN [mean: (4.510 ± 1.269) vs. (2.542 ± 1.827), P < 0.001]. Videos with higher engagement metrics, particularly those from lay individuals, were negatively correlated with information quality. Linear regression analysis confirmed the significant influence of the creator background (β = -0.618, P < 0.001) and video topic (β = 0.179, P = 0.009) on information quality. This study highlights the critical role of content creator background and topic on the quality of vaccination-related information on TikTok in Arabic. We emphasize the need for stringent verification of TikTok content, especially from lay individuals, as videos with higher engagement metrics often contained lower-quality information regarding vaccination. We recommend enhanced support for content from HCPs and targeted digital literacy programs to combat vaccine misinformation on TikTok effectively.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

BACKGROUND

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies.

OBJECTIVE

This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research.

METHODS

MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English.

RESULTS

With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%).

CONCLUSIONS

This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

Progress Achieved, Landmarks, and Future Concerns in Biomedical and Health Informatics

BACKGROUND

The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges.

METHODS

To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues.

RESULTS

Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches.

CONCLUSIONS

These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.

Pandemics: past, present, and future: multitasking challenges in need of cross-disciplinary, transdisciplinary, and multidisciplinary collaborative solutions

The extensive history of pandemics has spanned many centuries, profoundly impacting societies, economies, and public health, and thereby shaping the course of history in various ways. Advances in medicine, science, and public health practices have played a pivotal role in mitigating the effects of pandemics over time. This review explores the scientific landscape of contemporary pandemics, examining their diverse and complex nature. It goes beyond the biological aspects of pandemics to consider socioeconomic, environmental, and technological factors. Through a scientific lens, this study aims to understand the complexities of pandemics and contribute to the expanding knowledge base that helps humanity strengthen its defenses against global health threats. By elucidating the enigmas of pandemics, the study hopes to foster a more resilient and prepared global health environment. Highlighting the importance of a multidisciplinary, cross-disciplinary, and transdisciplinary approach, this exploration emphasizes the critical need to integrate biological, socioeconomic, environmental, and technological domains to develop more robust defenses against these global health challenges.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic

Introduction

The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy.

Methods

A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method.

Results

Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity.

Discussion

While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.