Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Geospatial techniques for monitoring and mitigating climate change and its effects on human health.

This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing.

The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. The causation of OO and T2D is complex and highly multifactorial rather than a mere energy intake (food) and expenditure (exercise) imbalance. But previous research into food and physical activity (PA) neighbourhood environments has mainly focused on associating body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, crude and inconsistent assumptions and conclusions that are far from the spirit of 'precision and accuracy public health'. Different people and population groups respond differently to the same food and PA environments, due to a myriad of unique individual and population group factors (genetic/epigenetic, metabolic, dietary and lifestyle habits, health literacy profiles, screen viewing times, stress levels, sleep patterns, environmental air and noise pollution levels, etc.) and their complex interplays with each other and with local food and PA settings. Furthermore, the same food store or fast food outlet can often sell or serve both healthy and non-healthy options/portions, so a simple binary classification into 'good' or 'bad' store/outlet should be avoided. Moreover, appropriate physical exercise, whilst essential for good health and disease prevention, is not very effective for weight maintenance or loss (especially when solely relied upon), and cannot offset the effects of a bad diet. The research we should be doing in the third decade of the twenty-first century should use a systems thinking approach, helped by recent advances in sensors, big data and related technologies, to investigate and consider all these factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention.

Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics.

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.

An overview of GeoAI applications in health and healthcare.

The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed.

Geospatial blockchain: promises, challenges, and scenarios in health and healthcare.

A PubMed query run in June 2018 using the keyword 'blockchain' retrieved 40 indexed papers, a reflection of the growing interest in blockchain among the medical and healthcare research and practice communities. Blockchain's foundations of decentralisation, cryptographic security and immutability make it a strong contender in reshaping the healthcare landscape worldwide. Blockchain solutions are currently being explored for: (1) securing patient and provider identities; (2) managing pharmaceutical and medical device supply chains; (3) clinical research and data monetisation; (4) medical fraud detection; (5) public health surveillance; (6) enabling truly public and open geo-tagged data; (7) powering many Internet of Things-connected autonomous devices, wearables, drones and vehicles, via the distributed peer-to-peer apps they run, to deliver the full vision of smart healthy cities and regions; and (8) blockchain-enabled augmented reality in crisis mapping and recovery scenarios, including mechanisms for validating, crediting and rewarding crowdsourced geo-tagged data, among other emerging use cases. Geospatially-enabled blockchain solutions exist today that use a crypto-spatial coordinate system to add an immutable spatial context that regular blockchains lack. These geospatial blockchains do not just record an entry's specific time, but also require and validate its associated proof of location, allowing accurate spatiotemporal mapping of physical world events. Blockchain and distributed ledger technology face similar challenges as any other technology threatening to disintermediate legacy processes and commercial interests, namely the challenges of blockchain interoperability, security and privacy, as well as the need to find suitable and sustainable business models of implementation. Nevertheless, we expect blockchain technologies to get increasingly powerful and robust, as they become coupled with artificial intelligence (AI) in various real-word healthcare solutions involving AI-mediated data exchange on blockchains.

From urban planning and emergency training to Pokémon Go: applications of virtual reality GIS (VRGIS) and augmented reality GIS (ARGIS) in personal, public and environmental health.

The latest generation of virtual and mixed reality hardware has rekindled interest in virtual reality GIS (VRGIS) and augmented reality GIS (ARGIS) applications in health, and opened up new and exciting opportunities and possibilities for using these technologies in the personal and public health arenas. From smart urban planning and emergency training to Pokémon Go, this article offers a snapshot of some of the most remarkable VRGIS and ARGIS solutions for tackling public and environmental health problems, and bringing about safer and healthier living options to individuals and communities. The article also covers the main technical foundations and issues underpinning these solutions.

On the road to personalised and precision geomedicine: medical geology and a renewed call for interdisciplinarity.

Our health depends on where we currently live, as well as on where we have lived in the past and for how long in each place. An individual's place history is particularly relevant in conditions with long latency between exposures and clinical manifestations, as is the case in many types of cancer and chronic conditions. A patient's geographic history should routinely be considered by physicians when diagnosing and treating individual patients. It can provide useful contextual environmental information (and the corresponding health risks) about the patient, and should thus form an essential part of every electronic patient/health record. Medical geology investigations, in their attempt to document the complex relationships between the environment and human health, typically involve a multitude of disciplines and expertise. Arguably, the spatial component is the one factor that ties in all these disciplines together in medical geology studies. In a general sense, epidemiology, statistical genetics, geoscience, geomedical engineering and public and environmental health informatics tend to study data in terms of populations, whereas medicine (including personalised and precision geomedicine, and lifestyle medicine), genetics, genomics, toxicology and biomedical/health informatics more likely work on individuals or some individual mechanism describing disease. This article introduces with examples the core concepts of medical geology and geomedicine. The ultimate goals of prediction, prevention and personalised treatment in the case of geology-dependent disease can only be realised through an intensive multiple-disciplinary approach, where the various relevant disciplines collaborate together and complement each other in additive (multidisciplinary), interactive (interdisciplinary) and holistic (transdisciplinary and cross-disciplinary) manners.

'Social, innovative and smart cities are happy and resilient': insights from the WHO EURO 2014 International Healthy Cities Conference.

This paper provides a brief overview of, and elaborates on, some of the presentations, discussions and conclusions from Day 4 of the 'WHO EURO 2014 International Healthy Cities Conference: Health and the City - Urban Living in the 21st Century', held in Athens, Greece on 25 October 2014. The Internet of Things (IoT) is made of sensors and other components that connect our version of the world made of atoms, i.e., humans/our bodies, our devices, vehicles, roads, buildings, plants, animals, etc., with a mirror digital version made of bits. This enables cities and regions to be self-aware and dynamically reconfigurable in real- or near-real-time, based on changes that are continuously monitored and captured by sensors, similar to the way the internal biological systems of a living being operate and respond to their environment (homeostasis). Data collected by various IoT sensors and processed via appropriate analytics can also help predict the immediate future with reasonable accuracy, which enables better planned responses and mitigation actions. Cities and regions can thus become more adaptable and resilient in face of adversity. Furthermore, IoT can link atoms (humans) to other atoms (humans) (again via bits), resulting in the formation of 'smart(er) communities' that are socially connected in new ways and potentially happier. Cities, but also less urbanised regions and the countryside, could all benefit from, and harness the power of, IoT to improve the health, well-being and overall quality of life of the local populations, actively engage citizens in a smarter governance of their region, empower them to better care for one another, promote stronger social inclusion, and ensure a greener, sustainable and more enjoyable environment for all. Technology can also help reverse the 'brain drain' from the countryside and smaller towns to larger metropolises by making the former more attractive and connected, with better services akin to those found in larger cities. The article also discusses some ways of measuring and benchmarking the performance of smart cities and their impact on well-being. However, it should be emphasised that technology is not a panacea and that other factors are equally important in creating happier and healthier cities and regions.

On the Internet of Things, smart cities and the WHO Healthy Cities.

This article gives a brief overview of the Internet of Things (IoT) for cities, offering examples of IoT-powered 21st century smart cities, including the experience of the Spanish city of Barcelona in implementing its own IoT-driven services to improve the quality of life of its people through measures that promote an eco-friendly, sustainable environment. The potential benefits as well as the challenges associated with IoT for cities are discussed. Much of the 'big data' that are continuously generated by IoT sensors, devices, systems and services are geo-tagged or geo-located. The importance of having robust, intelligent geospatial analytics systems in place to process and make sense of such data in real time cannot therefore be overestimated. The authors argue that IoT-powered smart cities stand better chances of becoming healthier cities. The World Health Organization (WHO) Healthy Cities Network and associated national networks have hundreds of member cities around the world that could benefit from, and harness the power of, IoT to improve the health and well-being of their local populations.

ChatGPT in Clinical Toxicology.

ChatGPT has recently been shown to pass the United States Medical Licensing Examination (USMLE). We tested ChatGPT (Feb 13, 2023 release) using a typical clinical toxicology case of acute organophosphate poisoning. ChatGPT fared well in answering all of our queries regarding it.

Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments.

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (ß = 0.504, CR = 5.064, p < 0.05) and self-concept (ß = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.

Real-time locating systems (RTLS) in healthcare: a condensed primer.

Real-time locating systems (RTLS, also known as real-time location systems) have become an important component of many existing ubiquitous location aware systems. While GPS (global positioning system) has been quite successful as an outdoor real-time locating solution, it fails to repeat this success indoors. A number of RTLS technologies have been used to solve indoor tracking problems. The ability to accurately track the location of assets and individuals indoors has many applications in healthcare. This paper provides a condensed primer of RTLS in healthcare, briefly covering the many options and technologies that are involved, as well as the various possible applications of RTLS in healthcare facilities and their potential benefits, including capital expenditure reduction and workflow and patient throughput improvements. The key to a successful RTLS deployment lies in picking the right RTLS option(s) and solution(s) for the application(s) or problem(s) at hand. Where this application-technology match has not been carefully thought of, any technology will be doomed to failure or to achieving less than optimal results.

Accuracy of geographically targeted internet advertisements on Google AdWords for recruitment in a randomized trial.

BACKGROUND: Google AdWords are increasingly used to recruit people into research studies and clinical services. They offer the potential to recruit from targeted control areas in cluster randomized controlled trials (RCTs), but little is known about the feasibility of accurately targeting ads by location and comparing with control areas. OBJECTIVE: To examine the accuracy and contamination of control areas by a location-targeted online intervention using Google AdWords in a pilot cluster RCT. METHODS: Based on previous use of online cognitive behavioral therapy for depression and population size, we purposively selected 16 of the 121 British postcode areas and randomized them to three intervention and one (do-nothing) control arms. Two intervention arms included use of location-targeted AdWords, and we compared these with the do-nothing control arm. We did not raise the visibility of our research website to normal Web searches. Users who clicked on the ad were directed to our project website, which collected the computer Internet protocol (IP) address, date, and time. Visitors were asked for their postcode area and to complete the Patient Health Questionnaire (depression). They were then offered links to several online depression resources. Google Analytics largely uses IP methods to estimate location, but AdWords uses additional information. We compared locations assessed by (1) Analytics, and (2) as self-identified by users. RESULTS: Ads were shown 300,523 times with 4207 click-throughs. There were few site visits except through AdWord click-throughs. Both methods of location assessment agreed there was little contamination of control areas. According to Analytics, 69.75% (2617/3752) of participants were in intervention areas, only 0% (8/3752) in control areas, but 30.04% (1127/3752) in other areas. However, according to user-stated postcodes, only 20.7% (463/2237) were in intervention areas, 1% (22/2236) in control areas, but 78.31% (1751/2236) in other areas. Both location assessments suggested most leakage from the intervention arms was to nearby postcode areas. Analytics data differed from postcodes reported by participants. Analysis of a subset of 200/2236 records over 10 days comparing IP-estimated location with stated postcode suggested that Google AdWords targeted correctly in just half the cases. Analytics agreed with our assessment that, overall, one-third were wrongly targeted by AdWords. There appeared little evidence that people who bothered to give their postcode did not answer truthfully. CONCLUSIONS: Although there is likely to be substantial leakage from the targeted areas, if intervention and control areas are a sufficient distance apart, it is feasible to conduct a cluster RCT using online ads to target British postcode areas without significant contamination. TRIAL REGISTRATION: Clinicaltrials.gov NCT01469689; http://clinicaltrials.gov/ct2/show/NCT01469689 (Archived by WebCite at http://www.webcitation.org/681iro5OU).

Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data.

This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or 'patch' implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.

Integrating Social and Family Support as a Measure of Health Outcomes: Validity Implications from the Integrated Model of Health Literacy.

(1) Background: Health literacy (HL) is one of the key determinants of health and healthcare outcomes. The objectives of this study are to measure and validate Sørensen et al.'s integrated model of health literacy (IMHL) in a developing country's youth population, as well as to assess the impact of family affluence and social and family support on healthcare domains. (2) Methods: A cross-sectional survey was carried out of undergraduate university students in 19 public and private sector universities in Pakistan during June-August 2022. A nine-factor measurement model was tested using confirmatory factor analysis (CFA), and structural equation modeling (SEM) based on the 56 valid items obtained from three different validated scales, such as the family affluence scale (FAS-II), the multidimensional scale of perceived social support (MSPSS), and the European Health Literacy Questionnaire (the HLS-EU-Q). (3) Results: The data were collected from 1590 participants with a mean age of 21.16 (±2.027) years. The model fit indices indicate that the model partially fitted the data: χ2 = 4.435, df = 1448, p = 0.000, RMSEA = 0.048, TLI = 0.906, CFI = 0.912, IFI = 0.912, GFI = 0.872, NFI = 0.889, RFI = 0.882, PGFI = 0.791. The structural equation model showed acceptable goodness of fit indices, indicating a significant direct influence of social and family support on healthcare and disease prevention. (4) Conclusions: Social and family support are the most influential factors, with regard to HL dimensions, in improving healthcare, disease prevention, and health promotion in low-income settings and among non-English-speaking communities.

Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data.

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper.

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples.

'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.

COVID-19 Misinformation Online and Health Literacy: A Brief Overview.

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and 'viral' nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.