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

Epidemics and Geographical Information System: Case of the Coronavirus disease 2019

Non peer reviewe

What Can COVID-19 Teach Us about Using AI in Pandemics?

The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI\u27s weaknesses as key factors affecting AI in future pandemic preparedness and response

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

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics

Ik ga op reis en neem mee …

Oratie uitgesproken door Prof.dr. L.G. Visser bij de aanvaarding van het ambt van hoogleraar in de Infectieziekten, in het bijzonder reizigersgeneeskunde aan de Universiteit Leiden op vrijdag 4 september 2015LUMC / Geneeskund

Governing digital health for infectious disease outbreaks

How can governing digital health for infectious disease outbreaks be enhanced? In many ways, the COVID-19 pandemic has simultaneously represented both the potential and marked limitations of digital health practices for infectious disease outbreaks. During the pandemic’s initial stages, states along with Big Data and Big Tech actors unleashed a scope of both established and experimental digital technologies for tracking infections, hospitalisations, and deaths from COVID-19 – and sometimes exposure to the virus SARS-CoV-2. Despite the proliferation of these technologies at the global level, transnational and cross-border integration, and cooperation within digital health responses to COVID-19 often faltered, while digital health regulations were fragmented, contested, and uncoordinated. This article presents a critiquing reflection of approaches to conceptualising, understanding, and implementing digital health for infectious disease outbreaks, observed from COVID-19 and previous examples. In assessing the strengths and limitations of existing practices of governing digital health for infectious disease outbreaks, this article particularly examines ‘informal’ digital health to build upon and consider how digitised responses to addressing and governing infectious disease outbreaks may be reconceptualised, revisited, or revised

Catching the flu: syndromic surveillance, algorithmic governmentality and global health security

This thesis offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The thesis traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This thesis demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This thesis argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the thesis also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this thesis demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance

Infectious diseases management framework for Saudi Arabia (SAIF)

A Thesis Submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosopyInfectious disease management system area is considered as an emerging field of modern healthcare in the Gulf region. Significant technical and clinical progress and advanced technologies can be utilized to enhance the performance and ubiquity of such systems. Effective infectious disease management (IDM) can be achieved by analysing the disease management issues from the perspectives of healthcare personnel and patients. Hence, it is necessary to identify the needs and requirements of both healthcare personnel and patients for managing the infectious disease. The basic idea behind the proposed mobile IDM system in this thesis is to improve the healthcare processes in managing infectious diseases more effectively. For this purpose, internet and mobile technologies are integrated with social networking, mapping and IDM applications to improve the processes efficiency. Hence, the patients submit their health related data through their devices remotely using our application to our system database (so-called SAIF). The main objective of this PhD project was the design and development of a novel web based architecture of next-generation infectious disease management system embedding the concept of social networking tailored for Saudi patients. Following a detailed literature review which identifies the current status and potential impact of using infectious diseases management system in KSA, this thesis conducts a feasibility user perspective study for identifying the needs and the requirements of healthcare personnel and the patients for managing infectious diseases. Moreover, this thesis proposes a design and development of a novel architecture of next-generation web based infectious disease management system tailored for Saudi patients (i.e., called SAIF – infectious diseases management framework for Saudi Arabia). Further, this thesis introduces a usability study for the SAIF system to validate the acceptability of using mobile technologies amongst infected patient in KSA and Gulf region. The preliminary results of the study indicated general acceptance of the patients in using the system with higher usability rating in high affected patients. In general, the study concluded that the concept of SAIF system is considered acceptable tool in particularly with infected patients

Evaluation des systèmes d'intelligence épidémiologique appliqués à la détection précoce des maladies infectieuses au niveau mondial.

Our work demonstrated the performance of the epidemic intelligence systems used for the early detection of infectious diseases in the world, the specific added value of each system, the greater intrinsic sensitivity of moderated systems and the variability of the type information source’s used. The creation of a combined virtual system incorporating the best result of the seven systems showed gains in terms of sensitivity and timeliness that would result from the integration of these individual systems into a supra-system. They have shown the limits of these tools and in particular: the low positive predictive value of the raw signals detected, the variability of the detection capacities for the same disease, but also the significant influence played by the type of pathology, the language and the region of occurrence on the detection of infectious events. They established the wide variety of epidemic intelligence strategies used by public health institutions to meet their specific needs and the impact of these strategies on the nature, the geographic origin and the number of events reported. As well, they illustrated that under conditions close to the routine, epidemic intelligence permitted the detection of infectious events on average one to two weeks before their official notification, hence allowing to alert health authorities and therefore the anticipating the implementation of eventual control measures. Our work opens new fields of investigation which applications could be important for both users systems.Nos travaux ont démontré les performances des systèmes d’intelligence épidémiologique en matière de détection précoce des évènements infectieux au niveau mondial, la valeur ajoutée spécifique de chaque système, la plus grande sensibilité intrinsèque des systèmes modérés et la variabilité du type de source d’information utilisé. La création d’un système virtuel combiné intégrant le meilleur résultat des sept systèmes a démontré les gains en termes de sensibilité et de réactivité, qui résulterait de l’intégration de ces systèmes individuels dans un supra-système. Ils ont illustrés les limites de ces outils et en particulier la faible valeur prédictive positive des signaux bruts détectés, la variabilité les capacités de détection pour une même pathologie, mais également l’influence significative jouée par le type de pathologie, la langue et la région de survenue sur les capacités de détection des évènements infectieux. Ils ont établis la grande diversité des stratégies d’intelligence épidémiologique mises en œuvre par les institutions de santé publique pour répondre à leurs besoins spécifiques et l’impact de ces stratégies sur la nature, l’origine géographique et le nombre des évènements rapportés. Ils ont également montré que dans des conditions proches de la routine, l’intelligence épidémiologique permettait la détection d’évènements infectieux en moyenne une à deux semaines avant leur notification officielle, permettant ainsi d’alerter les autorités sanitaires et d’anticiper la mise en œuvre d’éventuelles mesures de contrôle. Nos travaux ouvrent de nouveaux champs d’investigations dont les applications pourraient être importantes pour les utilisateurs comme pour les systèmes