Megatrends and equity gaps in global digital health: A bibliometric review (2010-2025).

The COVID-19 pandemic demonstrated the potential role of digital health tools in enhancing pandemic preparedness and response. These tools became essential, supporting not only health care delivery but also decision-making, communication, case identification, contact tracing, surveillance, vaccination rollout, and intervention evaluation. The interest in applying digital health tools to pandemic preparedness and response motivated conversations about digital epidemiology-a field of study that aims to provide insight into health and disease determinants by leveraging diverse digital data sources. In a globalized world, effective preparedness and response to pandemics require coordinated global action. This study investigates experts' opinions on strategies for improving global health security through the effective use of digital epidemiology, considering the current landscape of digital determinants of health. Epidemiologists, public health specialists, data scientists, and professionals with expertise in various components of digital health were recruited through convenience and snowball sampling methods. Their opinions were elicited using an electronic questionnaire developed by the authors in Research Electronic Data Capture (REDCap; Vanderbilt University). To ensure a global perspective, participants were recruited from Africa, North America, Oceania, and Europe. Thematic analysis and the strengths, weaknesses, opportunities, and threats (SWOT) analysis framework were used to analyze participants' responses. Most participants were familiar with the concept of digital epidemiology and expressed positive sentiments about its potential in strengthening global health security. Privacy and security, along with ethical and legal considerations, were ranked by most experts as high priority areas that decision-makers and implementers must consider to ensure sustainable integration of digital epidemiology tools in future pandemic preparedness and response. A SWOT analysis of participants' views on the promise of digital epidemiology revealed fewer strengths and more weaknesses compared to other components of the analysis framework. This study highlights the growing recognition of digital epidemiology as a critical tool for enhancing global health security, particularly using nontraditional data sources and emerging technologies, including artificial intelligence. The study affirms the need for a globally coordinated approach to governance, regulation, and investment in digital health infrastructure to ensure the responsible and effective application of digital innovations in epidemiological practice.

A cannabis use Reddit dataset for aspect-based sentiment analysis

Assoziation studies revolutionized genomics by rigorously screening large feature sets against health outcomes. Digital medicine now produces similarly high-dimensional, longitudinal sensor data from wearables, smartphones, and connected environments. We propose Sensor-Wide Association Studies (SWAS): structured, feature-wide, hypothesis-generating scans of a pre-specified library of sensor-derived features against one or more pre-defined clinical phenotypes, with transparent feature documentation, appropriate longitudinal modeling, and principled control of multiplicity. This perspective outlines minimal standards, common failure modes, and ethical considerations to help SWAS become a reproducible foundation for digital epidemiology and personalized medicine.

Infectious disease dynamics are deeply intertwined with social structures, behaviors, and information systems. This narrative review examines how social factors conceptualized through the integrated lens of "social contagion" within a syndemic framework shape infectious disease patterns from January 2000 to January 2024. Literature was retrieved from PubMed, Scopus, Web of Science, and Google Scholar using targeted combinations of terms related to social determinants, networks, communication, and digital epidemiology. Foundational pre-2000 works were consulted for theoretical grounding. Studies were selected based on their relevance to understanding how social processes influence infectious disease dynamics. The selection process prioritized high-impact empirical studies, meta-analyses, and seminal theoretical works that collectively informed the four thematic areas presented. As this is a narrative review, no statistical analyses were performed. The synthesis approach followed established guidelines for narrative reviews. Four major themes emerged from the synthesis of identified literature: (1) structural and commercial determinants socioeconomic inequality, racism, and commercial practices produce environments where infectious and non-communicable diseases interact synergistically; (2) network effects household, occupational, and mobility patterns shape transmission pathways and superspreading events; (3) belief systems misinformation, behavioral contagion, and varying levels of institutional trust influence vaccine uptake and protective behaviors; and (4) diagnosis and intervention digital epidemiology and agent-based models offer tools for integrating social data into disease surveillance and response. Infectious diseases are fundamentally biosocial phenomena. Effective control requires moving beyond biomedical models toward approaches centered on social epidemiology, equity, and trust-building. Future research must employ transdisciplinary collaboration to better measure and model the complex interactions that constitute social contagion.

Evaluation of interest trends among the general population and scientific community in migraine surgery.

The U.S. public health infrastructure includes real-time infectious disease surveillance as one of its pillars, but its effectiveness is conditioned by a complicated interaction between structural fragmentation, workforce shortages, data lapses, and technological advancement. This narrative review summarizes the existing literature on national surveillance systems, such as NSSP/ESSENCE, NNDSS, wastewater monitoring, genomic sequencing networks, and immunization registries, and how they can be used and in what ways they are limited in a decentralized context. Although the modernization efforts like the CDC data modernization Initiative and electronic expansion of case reporting have enhanced interoperability and timeliness, the fact that the systems of the states differ, some datasets are still missing, and reporting delays still occur, hinder prompt identification. Other constraints to the capacity of health departments to utilize advanced analytics are workforce shortages and low informatics capacity. Recent developments, such as the use of digital epidemiology, machine learning-based forecasts, and extended genomic and wastewater surveillance have high potential in detecting the outbreak earlier and providing better situational awareness. Nonetheless, biased coverage, artificial preferences, privacy issues, and governmental restrictions constrain the fair usage of these tools. The development of the future needs to be based on the integration of the models of analysis, long-term financing, enhanced human resources, and the coordinated system of sharing data. Combined, these results indicate the potential transformations and challenging issues that define real-time surveillance of infectious diseases in the United States.

Disasters such as earthquakes create conditions that amplify the risk of ectoparasitic infestations due to overcrowding, disrupted sanitation, and limited hygiene resources. This study explores ectoparasite-related online search interests in Türkiye before and after the earthquakes of February 2023, offering insights into potential secondary health concerns during disaster recovery. Weekly Google Trends data on scabies, lice, bed bugs, ticks, and insect bites were extracted for Türkiye and its provinces. Relative search volumes (RSV) were analysed to compare two years pre- and post-earthquake periods. Statistical analyses included time series comparisons. Following the earthquake, RSV for scabies and bed bugs increased sharply, while lice RSV significantly declined; tick and insect bite queries maintained consistent seasonal cycles. Integrating digital epidemiology insights with field-based surveillance could enhance the early detection and response to outbreaks following disasters. Long-term monitoring should be included in surveillance systems.

SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.

Large-scale societal disruptions, including pandemics and economic crises, profoundly influence healthcare-seeking behavior. Oral and maxillofacial surgery (OMS) comprises both essential and elective procedures and is therefore particularly sensitive to external pressures. This study investigated how the COVID-19 pandemic and subsequent economic downturn affected public interest in OMS-related procedures in Türkiye via Google Trends. This observational digital epidemiology time series study analyzed weekly relative search volume (RSV) data for four OMS-related keywords (“wisdom tooth extraction,” “oral surgery,” “implant,” and “jaw surgery”) retrieved from Google Trends between January 2020 and June 2025. Societal periods were categorized as pandemic restrictions/recovery, transition/normalization buffers, economic downturns, and extended follow-up periods. Data normality was assessed via the Shapiro–Wilk test. Between-period comparisons were conducted via the Kruskal–Wallis test with Dunn’s post hoc pairwise comparisons (Holm–Bonferroni adjustment). Temporal changes were evaluated using segmented regression (interrupted time series analysis), enabling the assessment of level and trend changes across predefined societal periods with robust Newey–West standard errors. Interest in “wisdom tooth extraction” and “oral surgery” increased significantly during the postpandemic recovery period (p < 0.05), which is consistent with the rebound demand for deferred essential care. In contrast, interest in “implant” and “jaw surgery” declined significantly during the economic downturn (p < 0.01), indicating reduced public attention to high-cost elective procedures due to financial constraints. Public online interest in OMS procedures in Türkiye was shaped differently by pandemic-related service disruptions and subsequent economic pressures. Google Trends appears to be a valuable complementary tool for monitoring population-level shifts in healthcare interest and may support proactive clinical planning and health policy decision making during periods of societal instability.

The exponential growth in academic and digital health data as well as analytical methods has ushered in a new age of digital epidemiology, however, the conceptual foundations, operational boundaries and translation mechanisms of this emerging field still remain insufficiently consolidated. This narrative review critically examines the historical underpinnings of digital epidemiology, tracing the evolution of its definitions and identifying its key challenges and policy implications through the lens of public health dashboards. Digital epidemiology began as simply the use of digital sources of data for epidemiology, but has over the years developed into a much larger domain that incorporates several other related concepts such as infodemiology, infoveillance, participatory surveillance, dashboards, etc. As we foray into this new domain, it is essential to confront the persisting challenges related to data quality, bias, representativeness, ethics and governance that are inherent in digital epidemiology. At the same time, the expanding role of public health dashboards within this field and key emerging innovations such as artificial intelligence and large language models need to be taken into account keeping in mind the risks of automation and need for evaluation framework. By framing dashboards as the operational nerve centre of digital epidemiology, this study provides a unified conceptual foundation for advancing the field of digital epidemiology towards sustainable, equitable and evidence-backed public health action.

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA), has demonstrated significant efficacy in weight reduction and glycemic control in patients with type 2 diabetes and obesity. However, concerns have emerged regarding its potential association with ophthalmic adverse events, particularly non-arteritic anterior ischemic optic neuropathy (NAION). This study aimed to investigate the possible link between tirzepatide and ischemic optic neuropathy (ION) through pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) and to complement these findings with an infodemiology assessment using Google Trends. FAERS reports from January 2022 to June 2025 were analyzed using OpenVigil 2.1 to identify cases of ION with tirzepatide as the primary suspect drug. Disproportionality analyses were performed using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Relative Reporting Ratio (RRR), and Evans criteria were applied for causality assessment. In parallel, global search interest in tirzepatide was evaluated using Google Trends data from 2020 to 2025 to explore public awareness and its potential impact on reporting patterns. A total of 28 ION cases were identified for tirzepatide. The event is rare but serious. Disproportionality analysis yielded significant signals (ROR: 2.599, 95% CI: 1.778; 3.799; PRR: 2.598 95% CI: 1.778; 3.797; RRR: 2.522, 95% CI: 1.726; 3.685; Chi-Squared: 24.692), with Evans criteria supporting a "probable" drug-event association. Google Trends demonstrated an exponential rise in global search interest for tirzepatide, particularly in Western countries with high prevalence of obesity and type 2 diabetes, reflecting increased accessibility and use. The pharmacovigilance analysis suggests a potential association between tirzepatide and ION, warranting cautious clinical consideration and further investigation. The event is rare but serious. Integrating pharmacovigilance data with digital epidemiology may enhance early signal detection and risk management for rare but clinically significant adverse events such as NAION.

Background: Digital Epidemiology has emerged as a transformative approach to infectious disease surveillance, leveraging digital data streams such as social media, search queries, and mobility patterns. While these methods offer speed and scale, they introduce significant statistical and ethical challenges, particularly bias and fairness concerns in predictive modeling. Aim: This review aims to examine algorithmic fairness in clinical predictive models within epidemiological research, focusing on bias audits and mitigation strategies in the context of Digital Epidemiology. Methods: A comprehensive literature review was conducted, analyzing methodological differences between classical and digital approaches, sources of bias, and corrective strategies. Key themes include representativeness, measurement error, and algorithmic bias in machine learning models trained on digital data. Results: Findings reveal that Digital Epidemiology offers real-time, large-scale data collection but suffers from structural biases due to self-selection, platform design, and digital divides. Bias mitigation is often retrospective, relying on weighting, normalization, and cross-validation. Ethical concerns such as privacy and informed consent intersect with fairness, as predictive models risk amplifying inequities. Integration of classical rigor with digital flexibility and continuous bias audits is essential for equitable outcomes. Conclusion: Digital Epidemiology complements classical methods but requires robust frameworks for bias detection, ethical governance, and algorithmic transparency. Sustained collaboration, standardization, and inclusive data practices are critical to ensure predictive models support fair and actionable public health decisions.

Seasonal influenza continues to pose a substantial burden on health systems worldwide, with an estimated 1 billion infections each year, including 3-5 million severe cases and hundreds of thousands of deaths. In Central Asia, this viral landscape is further complicated by the co circulation of multiple respiratory pathogens, heterogeneous climates and unequal access to laboratory diagnostics. At the same time, internet penetration and smartphone use have grown rapidly across the region, creating dense streams of search queries and other digital traces that potentially mirror population level concern about respiratory symptoms. Digital epidemiology uses such nontraditional data streams to complement, rather than replace, established surveillance networks. This article develops a regional framework for harnessing web search data to track influenza-like illness trends in Central Asia in close alignment with existing laboratory-based systems. The approach integrates global experience from search-based influenza surveillance with the specific institutional, linguistic and infrastructural features of Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan. The results present a structured set of design outcomes: a data source matrix, a multilingual query taxonomy, and a maturity index for integrating digital indicators into public health decision making. The article concludes that search data can enrich influenza-like illness surveillance in Central Asia if embedded in transparent analytic workflows, governed by robust ethical safeguards and continuously validated against clinical data.

Digital epidemiology has emerged as a critical complement to traditional surveillance by leveraging digitally generated data to monitor population health behaviors and perceptions in near real time. Despite rapid methodological advances, the field remains dominated by tool-centric approaches, with limited integration of behavioral theory capable of explaining how information exposure translates into health-related decision-making. This article advances a theory-building, analytically grounded framework for digital epidemiological enquiry and empirically illustrates its application using a COVID-19 vaccine discourse case study from the United Kingdom and the United States. Drawing on the Health Belief Model (HBM), infodemiology, and information diffusion theory, the framework links perceived susceptibility, severity, benefits, barriers, and cues to action with AI-enabled sentiment and topic analytics. Using a large corpus of vaccine-related Twitter data, natural language processing and topic modeling were employed to operationalize behavioral constructs in digital discourse. Findings demonstrate that sentiment and thematic patterns can approximate key behavioral dimensions, while also revealing important limitations related to structural context, trust, and collective narratives that are only partially visible in digital traces. The study contributes a theoretically informed, empirically grounded approach for advancing digital epidemiology as an explanatory and policy-relevant discipline.

Changes in public search behavior following the WHO reclassification of tanning beds as a group 1 human carcinogen: A digital epidemiology study.

ABSTRACT This study aims to explore the theoretical evolution of modern epidemiology within the context of global health research, emphasizing its transition from traditional biomedical paradigms toward more integrative, interdisciplinary, and justice-oriented frameworks. Employing a qualitative descriptive approach through a comprehensive literature review, the research synthesizes findings from peer-reviewed articles, official reports, and theoretical sources published between 2014 and 2025. Data collection involved systematic document analysis and thematic categorization, while the analysis followed an inductive approach to identify emerging concepts and paradigmatic shifts in epidemiological theory and practice. The results reveal five major trends: (1) the persistence yet transformation of biomedical dominance; (2) methodological innovation through big data, AI, and digital epidemiology; (3) the rise of precision epidemiology integrating personalized and population-level insights; (4) the growing centrality of social justice and equity frameworks; and (5) specialized advances in wastewater-based and occupational epidemiology. These findings demonstrate how modern epidemiology is evolving into a multidimensional science that unites biological, technological, and social determinants of health. The study concludes that this evolution not only strengthens theoretical foundations but also enhances the ethical and practical capacity of global health research to address complex and inequitable health challenges. Future directions emphasize developing hybrid theoretical frameworks that balance computational innovation with social responsibility to promote global health equity. Keywords: modern epidemiology, global health, qualitative research, precision epidemiology, social determinants of health.

Abstract Brain cancer affects approximately 5-7 people per 100,000 people. With over 35% of patients using Google to search their medical conditions, Google Trends (GT) can help evaluate public search interest in medical conditions like brain cancer. The objective was to determine the strength of Google data surrounding brain cancer in comparison to data from the National Cancer Institute (NCI). GT state-by-state analysis for “brain cancer” was conducted from 2017-2021 for the United States and compared to NCI brain cancer diagnosis data by state from 2017-2021. States with higher brain cancer diagnoses had higher search volumes for brain cancer, and states with lower brain cancer diagnoses had lower search volumes for brain cancer (p=0.0473). Hawaii and District of Columbia have the fewest brain cancer diagnoses while Vermont and New Hampshire have the most brain cancer diagnoses. New York and Kentucky had the highest search volumes for brain cancer while Utah, Hawaii, and Nevada had the lowest search volumes for brain cancer. Understanding brain cancer diagnosis and brain cancer search trends can optimize public health initiatives and education efforts surrounding brain cancer.

The February 2023 Kahramanmaraş earthquakes in Türkiye caused widespread devastation, significantly disrupting health services, including reproductive health, which is often neglected in disaster response. Assessing reproductive health needs post-disaster is logistically challenging. This study leverages digital epidemiology to investigate the earthquake’s impact on online information-seeking for birth control methods across eleven affected provinces. We analyzed weekly birth control-related search probability metrics from the Google Trends Research API (January 2022–December 2023), employing multilingual knowledge graph queries for enhanced coverage in diverse populations. The 12-week post-earthquake period was compared to the immediate pre-earthquake and 2022 baseline periods using Wilcoxon signed-rank tests, supplemented by time-series decomposition and anomaly detection. A significant, immediate decline in contraceptive searches occurred post-earthquake across most analyzed provinces compared to both reference periods. Recovery patterns varied markedly by earthquake impact severity; heavily affected provinces (e.g., Hatay, Kahramanmaraş) showed prolonged reductions, while less affected regions (e.g., Elazığ, Diyarbakır) stabilized faster. Regional factors like high baseline fertility (Şanlıurfa) were observed with sustained search interest. In contrast, low-population areas (Kilis) yielded minimal data, highlighting methodological limitations for Google Trends in low-search contexts. The initial sharp decline and recovery observed in online searches underscore the persistent underlying importance of reproductive health post-disaster, necessitating the timely restoration and integration of contraceptive services within response frameworks. Despite limitations, this novel digital surveillance approach provides valuable real-time insights into public health needs during crises, emphasizing the need to prioritize equitable contraceptive access, potentially through digital tools, in disaster settings.

Digital epidemiology: leveraging social media for insight into epilepsy and mental health