Application of Big Data in Digital Epidemiology

Epidemiology is the study of dynamics of health and disease in human population. It aims to identify the occurrence, pattern, and etiology of human diseases so that the causes of these diseases can be understood, which in turn will help in preventing their spread. In traditional epidemiology, the data is collected by various public health agencies through various means. Many times, the actual figures vary a lot from the one reported. Sometimes this difference is due to human errors, but most of the time, it is due to intentional underreporting. Big data techniques can be used to analyze this huge amount of data so as to extract useful information from it. The electronic health data is so large and complex that it cannot be processed using traditional software and hardware. It is also not possible to manage this data using traditional data management tools. This data is huge in terms of volume as well as diversity and the speed at which it is being generated. The ability to combine and analyze these different sources of data has huge impact on epidemic tracking.

Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future.

From online data collection to identification of disease mechanisms: The IL-1beta, IL-6 and TNF-alpha; cytokine triad is associated with post-acute sequelae of COVID-19 in a digital research cohort

Post-acute sequelae of COVID-19 (PASC) emerge as a global problem with unknown molecular drivers. In a digital epidemiology approach, we rapidly recruited 8,077 individuals out of 129,733 households in Halle (Saale) to the cohort study for digital health research in Germany (DigiHero). These responded to a basic questionnaire followed by a PASC-focused survey and blood sampling in case of prior positive SARS-CoV-2 testing in their household. The presented analysis is based on the first 318 DigiHero participants, the majority thereof after mild infections. PASC were reported in 67.8% of cases, consisted predominantly in fatigue, dyspnea and concentration deficit, persisted in 60% over the follow-up period of on average eight months and their resolution was unaffected by post-infection vaccination. PASC was not associated with post-COVID-19 autoantibodies, but with elevated levels of IL-1beta, IL-6 and TNF-alpha. Blood profiling and single-cell data from validation cohorts with early infection suggested the induction of these cytokines in COVID-19 lung pro-inflammatory macrophages creating a self-sustaining feedback loop. Our data indicate a long-lasting cytokine triad - potentially underlying PASC symptoms - to be driven by macrophage primed during infection. We demonstrate how the combination of digital epidemiology with selective biobanking can rapidly generate hints towards disease mechanisms.

Comparison of a full arch digital photographic assessment of caries prevalence in 5-year-old children to an established visual assessment method: a cross-sectional study

Introduction Digital epidemiology in dental disease screening has a number of advantages which warrant further exploration. Aim This study aimed to test the examination accuracy of digital images to evaluate child oral health by comparing the new method to a gold standard method. It also investigated the levels of diagnostic accuracy between different examiners, including dental care professionals and a lay examiner, when quantifying dental disease using images. Methods A calibrated dental examiner inspected forty 5-year-olds. In addition, three sets of digital images were taken per child. These images were assessed by six examiners. Sensitivity and specificity of caries diagnosis and inter-examiner reliability were calculated to compare the caries scores derived from examination of the images to those of the gold standard examinations. Results The mean values for sensitivity and specificity scores were 48.0% and 99.1%, respectively. The mean value for kappa showed moderate agreement between 0.43 and 0.73 (0.57). Mean values for agreement using intra-class coefficients were excellent (0.78) and good (0.73) for dt and dmft, respectively. No statistical difference in the validity of the caries scores was shown between the different image assessors. Conclusions These data demonstrate the feasibility of using digital images to screen child oral health and for nondental professionals to be recruited to carry out digital epidemiology for the oral health surveillance of children.

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.

Digital Epidemiology of Innovation (Preprint)

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

From vertical to Horizontal Healthcare: The Next-Generation Medicine (Preprint)

UNSTRUCTURED Approach: This is a theorical reflection about Next-Generation Medicine, which is the first step to begin with an exponential medical healthcare and break with past models. Findings: In the past, the medical healthcare relied on an evidence-based practice to provide the best treatment options for patients, however, since 2010 a strong economic wave has shaped the perspective into a value-based medicine framework. We are facing new social dynamics and megatrends in our society. The emergence of 4.0 technologies is leading us to a pathway where a next-generation medicine will create an exponential value for the overall healthcare ecosystem. Originality: Next-Generation Medicine (NGM) integrates healthcare into digital ecosystems linked by innovative interfaces, advanced analytics, centric customer models and digital epidemiology surrounding a new concept of health and disease management. NGM is based in four core capabilities of physicians: Creativity, Collaboration, Communication and Critical thinking added to advanced digital operations that creates a systemic risk management. This integration is developed using bidirectional and integrative digital platforms operated by AI/ML connected to IoT and data collection in the cloud or in the edge computing. It is time for healthcare visionaries to set prejudice aside and start contemplating the amazing landscape that Next-Generation Medicine could offer.