Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review

BACKGROUND

Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach.

OBJECTIVE

This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches.

METHODS

A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies.

RESULTS

The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use.

CONCLUSIONS

The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows.

TRIAL REGISTRATION

PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study

BACKGROUND

Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.

OBJECTIVE

This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.

METHODS

The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.

RESULTS

We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.

CONCLUSIONS

The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Insights from ARCOS-V's Transition to Remote Data Collection during the COVID-19 Pandemic: A Descriptive Study

INTRODUCTION

The ARCOS-V study, an epidemiological study on stroke and transient ischaemic attack (TIA), faced the challenge of continuing data collection amidst the COVID-19 pandemic. This study aimed to describe the methodological changes and challenges encountered during the transition from paper-based methods to digital data collection for the ARCOS-V study and to provide insights into the potential of using digital tools to transform epidemiological research.

METHODS

The study adapted to remote data collection using REDCap and Zoom, involving daily health record reviews, direct data entry by trained researchers, and remote follow-up assessments. The process was secured with encryption and role-based access controls. The transition period was analysed to evaluate the effectiveness and challenges of the new approach.

RESULTS

The digital transition allowed for uninterrupted monitoring of stroke and TIA cases during lockdowns. Using REDCap and Zoom improved data reach, accuracy, and security. However, it also revealed issues such as the potential for systematic data entry errors and the need for robust security measures to protect sensitive health information.

CONCLUSION

The ARCOS-V study's digital transformation exemplifies the resilience of epidemiological research in the face of a global crisis. The successful adaptation to digital data collection methods highlights the potential benefits of such tools, particularly as we enter a new age of artificial intelligence (AI).

Surveillance of Antimicrobial Resistance in the ECOWAS Region: Setting the Scene for Critical Interventions Needed

Antimicrobial resistance poses a significant challenge to public health globally, leading to increased morbidity and mortality. AMR surveillance involves the systematic collection, analysis, and interpretation of data on the occurrence and distribution of AMR in humans, animals, and the environment for action. The West African Health Organization, part of the Economic Community of West African States (ECOWAS), is committed to addressing AMR in the region. This paper examines the status of AMR surveillance in ECOWAS countries using available WHO data from the TrACSS survey and GLASS enrollments. The analysis reveals that while progress has been made, significant challenges remain. Twelve of the fifteen ECOWAS countries are enrolled in GLASS, and ten have developed national action plans (NAPs) for AMR. However, there is a need to ensure all countries fully implement their NAPs, continue reporting to GLASS, and use the data for evidence-based actions and decision making. Surveillance systems for AMR and antimicrobial consumption/use vary across countries with some demonstrating limited capacity. All countries, except Cabo Verde, reported having a reference laboratory for AMR testing. Strengthening laboratory capabilities, data management and use, and multisectoral coordination are crucial for effective AMR surveillance and response. Based on the findings and the regional context, it is essential to prioritize capacity building, data utilization, and the adoption of standardized guidelines for AMR surveillance. Collaboration among ECOWAS countries, the WAHO, and international partners is essential to address AMR comprehensively. Ensuring a consistent supply of essential antimicrobial medications and reagents is vital.

Availability of published evidence on coverage, cost components, and funding support for digitalisation of infectious disease surveillance in Africa, 2003-2022: a systematic review

BACKGROUND

The implementation of digital disease surveillance systems at national levels in Africa have been challenged by many factors. These include user applicability, utility of IT features but also stable financial support. Funding closely intertwines with implementations in terms of geographical reach, disease focus, and sustainability. However, the practice of evidence sharing on geographical and disease coverage, costs, and funding sources for improving the implementation of these systems on the continent is unclear.

OBJECTIVES

To analyse the key characteristics and availability of evidence for implementing digital infectious disease surveillance systems in Africa namely their disease focus, geographical reach, cost reporting, and external funding support.

METHODS

We conducted a systematic review of peer-reviewed and grey literature for the period 2003 to 2022 (PROSPERO registration number: CRD42022300849). We searched five databases (PubMed, MEDLINE over Ovid, EMBASE, Web of Science, and Google Scholar) and websites of WHO, Africa CDC, and public health institutes of African countries. We mapped the distribution of projects by country; identified reported implementation cost components; categorised the availability of data on cost components; and identified supporting funding institutions outside Africa.

RESULTS

A total of 29 reports from 2,033 search results were eligible for analysis. We identified 27 projects implemented in 13 countries, across 32 sites. Of these, 24 (75%) were pilot projects with a median duration of 16 months, (IQR: 5-40). Of the 27 projects, 5 (19%) were implemented for HIV/AIDs and tuberculosis, 4 (15%) for malaria, 4 (15%) for all notifiable diseases, and 4 (15%) for One Health. We identified 17 cost components across the 29 reports. Of these, 11 (38%) reported quantified costs for start-up capital, 10 (34%) for health personnel compensation, 9 (31%) for training and capacity building, 8 (28%) for software maintenance, and 7(24%) for surveillance data transmission. Of 65 counts of external funding sources, 35 (54%) were governmental agencies, 15 (23%) foundations, and 7 (11%) UN agencies.

CONCLUSIONS

The evidence on costing data for the digitalisation of surveillance and outbreak response in the published literature is sparse in quantity, limited in detail, and without a standardised reporting format. Most initial direct project costs are substantially donor dependent, short lived, and thus unsustainable.

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

BACKGROUND

European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.

METHODS

We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.

RESULTS

Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.

CONCLUSIONS

The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Monitoring the effective reproduction number [Formula: see text] of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating [Formula: see text] at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases.

The role of digital surveillance during outbreaks: the Ghana experience from COVID-19 response

Over the years, Ghana has made notable strides in adopting digital approaches to address societal challenges and meet demands. While the health sector, particularly the disease surveillance structure, has embraced digitization to enhance case detection, reporting, analysis, and information dissemination, critical aspects remain to be addressed. Although the Integrated Disease Surveillance and Response (IDSR) structure has experienced remarkable growth in digitization, certain areas require further attention as was observed during the COVID-19 pandemic. Ghana during the COVID-19 pandemic, recognized the importance of leveraging digital technologies to bolster the public health response. To this end, Ghana implemented various digital surveillance tools to combat the pandemic. These included the 'Surveillance Outbreak Response Management and Analysis System (SORMAS)', the digitalized health declaration form, ArcGIS Survey123, Talkwalker, 'Lightwave Health information Management System' (LHIMS), and the 'District Health Information Management System (DHIMS)'. These digital systems significantly contributed to the country's success in responding to the COVID-19 pandemic. One key area where digital systems have proved invaluable is in the timely production of daily COVID-19 situational updates. This task would have been arduous and delayed if reliant solely on paper-based forms, which hinder efficient reporting to other levels within the health system. By adopting these digital systems, Ghana has been able to overcome such challenges and provide up-to-date information for making informed public health decisions. This paper attempts to provide an extensive description of the digital systems currently employed to enhance Ghana's paper-based disease surveillance system in the context of its response to COVID-19. The article explores the strengths and challenges or limitations associated with these digital systems for responding to outbreaks, offering valuable lessons that can be learned from their implementation.

Geoepidemiological perspective on COVID-19 pandemic review, an insight into the global impact

The COVID-19 pandemic showed major impacts, on societies worldwide, challenging healthcare systems, economies, and daily life of people. Geoepidemiology, an emerging field that combines geography and epidemiology, has played a vital role in understanding and combatting the spread of the virus. This interdisciplinary approach has provided insights into the spatial patterns, risk factors, and transmission dynamics of the COVID-19 pandemic at different scales, from local communities to global populations. Spatial patterns have revealed variations in incidence rates, with urban-rural divides and regional hotspots playing significant roles. Cross-border transmission has highlighted the importance of travel restrictions and coordinated public health responses. Risk factors such as age, underlying health conditions, socioeconomic factors, occupation, demographics, and behavior have influenced vulnerability and outcomes. Geoepidemiology has also provided insights into the transmissibility and spread of COVID-19, emphasizing the importance of asymptomatic and pre-symptomatic transmission, super-spreading events, and the impact of variants. Geoepidemiology should be vital in understanding and responding to evolving new viral challenges of this and future pandemics.

Attitude towards dengue control efforts with the potential of digital technology during COVID-19: partial least squares-structural equation modeling

Background: Dengue fever is still a public health issue in Indonesia, and during the coronavirus disease 2019 (COVID-19) pandemic, integrated digital technology will be required for its control. This study aims to identify critical indicators influencing attitudes towards dengue control related to the potential for implementing digital technology. Methods: This was a cross-sectional survey, with 515 people willing to fill out an online questionnaire. The analysis was conducted using Partial Least Square-Structural Equation Modelling (PLS-SEM). There were 46 indicators used to assess attitudes toward dengue control, which were organized into six variables: the need for digital information systems, perceptions of being threatened with dengue, the benefits of dengue control programs, program constraints, environmental factors and attitudes in dengue control. Results:  The source of information needed for dengue control was mainly through social media. There was a positive relationship between perception of environmental factors to perception of dengue threat, perception of program constraints, perception of program benefits, and perception of digital technology needs. Perception of program benefits and threatened perception of dengue have a positive relationship with perception of digital technology needs. Conclusions: This model showed the variables perception of digital technology and perception of benefits had a positive association with attitude towards dengue control.

Current ethical and social issues in epidemiology

PURPOSE

The American College of Epidemiology held its 2021 Annual Meeting virtually, September 8-10, with a conference theme of 'From Womb to Tomb: Insights from Health Emergencies'. The American College of Epidemiology Ethics Committee hosted a symposium session in recognition of the ethical and social challenges brought to light by the coronavirus disease 2019 pandemic and on the occasion of the publication of the third edition of the classic text, Ethics and Epidemiology. The American College of Epidemiology Ethics Committee invited the book editor and contributing authors to present at the symposium session titled 'Current Ethical and Social Issues in Epidemiology.' The purpose of this paper is to further highlight the ethical challenges and presentations.

METHODS

Three speakers with expertise in ethics, health law, health policy, global health, health information technology, and translational research in epidemiology and public health were selected to present on the social and ethical issues in the current landscape. Dr. S Coughlin presented on the 'Ethical and Social Issues in Epidemiology', Dr. L Beskow presented on 'Ethical Challenges in Genetic Epidemiology', and Dr. K Goodman presented on the 'Ethics of Health Informatics'.

RESULTS

New digital sources of data and technologies are driving the ethical challenges and opportunities in epidemiology and public health as it relates to the three emerging topic areas identified: (1) digital epidemiology, (2) genetic epidemiology, and (3) health informatics. New complexities such as the reliance on social media to control infectious disease outbreaks and the introduction of computing advancements are requiring re-evaluation of traditional bioethical frameworks for epidemiology research and public health practice. We identified several cross-cutting ethical and social issues related to informed consent, benefits, risks and harms, and privacy and confidentiality and summarized these alongside more nuanced ethical considerations such as algorithmic bias, group harms related to data (mis)representation, risks of misinformation, return of genomic research results, maintaining data security, and data sharing. We offered an integrated synthesis of the stages of epidemiology research planning and conduct with the ethical issues that are most relevant in these emerging topic areas.

CONCLUSIONS

New realities exist for epidemiology and public health as professional groups who are faced with addressing population health, and especially given the recent pandemic and the widespread use of digital tools and technologies. Many ethical issues can be understood in the context of existing ethical frameworks; however, they have yet to be clearly identified or connected with the new technical and methodological applications of digital tools and technologies currently in use for epidemiology research and public health practice. To address current ethical challenges, we offered a synthesis of traditional ethical principles in public health science alongside more nuanced ethical considerations for emerging technologies and aligned these with lifecycle stages of epidemiology research. By critically reflecting on the impact of new digital sources of data and technologies on epidemiology research and public health practice, specifically in the control of infectious outbreaks, we offered insights on cultivating these new areas of professional growth while striving to improve population health.

Promoting telemedicine in Latin America in light of COVID-19

The Faculty of Health Sciences at the Universitat Oberta de Catalunya (Barcelona, Spain) was officially designated a 'World Health Organization (WHO) Collaborating Centre in eHealth' on 5 April 2018. The Centre aims to provide support to countries willing to develop new telemedicine services; to promote the use of eHealth; and to study the adoption and use of mobile health in countries of both the Region of the Americas and Europe. On 11 March 2020, WHO declared COVID-19 a global pandemic given the significant increase in the number of cases worldwide. Since then, the Centre has played an important role in addressing COVID-19 by undertaking fruitful cooperative activities. Lockdowns and social distancing in response to the high contagion rate of COVID-19 were the main triggers for a challenging digital transformation in many sectors, especially in healthcare. In this extreme crisis scenario, the rapid adoption of digital health solutions and technological tools was key to responding to the enormous pressure on healthcare systems. Telemedicine has become a necessary component of clinical practice for the purpose of providing safer patient care, and it has been used to support the healthcare needs of COVID-19 patients and routine primary care patients alike. This article describes the Centre's contribution to the work of the Pan American Health Organization (PAHO) and WHO in supporting Latin American and European countries to develop new telemedicine services and guidance on how to address COVID-19 through digital health solutions. Future actions are also highlighted.

Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru

Nowadays it is necessary to strengthen health information systems and data-based solutions. However, there are few graduate training programs in Peru to use tools and methods of data science applied in public health. This article describes the development process and the initial assessment regarding the experience of the participants in an international multidisciplinary diploma in data intelligence for pandemics and epidemics preparedness, which was carried out from January to May 2021. The diploma was structured in 7 modules and 40 Peruvian professionals participated, of which 11 (27.5%) were women, and 16 (40%) came from regions outside of Lima and Callao. We discussed the need to strengthen institutional and health professionals' capacity to adequately manage large volumes of data, information, and knowledge through the application of emerging technologies to optimize data management processes to improve decision-making in health.

In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology

The COVID-19 pandemic witnessed a surge in the use of health data to combat the public health threat. As a result, the use of digital technologies for epidemic surveillance showed great potential to collect vast volumes of data, and thereby respond more effectively to the healthcare challenges. However, the deployment of these technologies raised legitimate concerns over risks to individual privacy. While the ethical and governance debate focused primarily on these concerns, other relevant issues remained in the shadows. Leveraging examples from the COVID-19 pandemic, this perspective article aims to investigate these overlooked issues and their ethical implications. Accordingly, we explore the problem of the digital divide, the role played by tech companies in the public health domain and their power dynamics with the government and public research sector, and the re-use of personal data, especially in the absence of adequate public involvement. Even if individual privacy is ensured, failure to properly engage with these other issues will result in digital epidemiology tools that undermine equity, fairness, public trust, just distribution of benefits, autonomy, and minimization of group harm. On the contrary, a better understanding of these issues, a broader ethical and data governance approach, and meaningful public engagement will encourage adoption of these technologies and the use of personal data for public health research, thus increasing their power to tackle epidemics.

Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.