# Public health Published articles for Public health. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Predicting Public Health Impacts of Electricity Usage DevFeed: [Predicting Public Health Impacts of Electricity Usage]() Original publisher: [Read original article]() Author: Yejia Liu, Zhifeng Wu, Pengfei Li, Shaolei Ren Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Advanced AI workflows](), [Sustainable Computing](), [Critical Infrastructure]() Tags: [ai](), [air-quality](), [charging](), [cs-ai](), [cs-lg](), [electricity](), [emissions](), [framework](), [pipeline](), [public-health]() ### AI overview HealthPredictor links electricity use to estimated public health impacts through three stages: predicting the generation fuel mix, modeling pollutant emissions and atmospheric dispersion, and translating pollutant changes into monetized health damages. Across multiple U.S. regions, the authors report that optimizing for health impacts produces substantially lower public-health prediction errors than fuel-mix-driven baselines. A case study on electric-vehicle charging schedules illustrates potential health gains and guidance for demand-side energy management. The supplied text is an abstract and does not provide detailed methods, quantitative results, or limitations. ### Source excerpt arXiv:2511.22031v2 Announce Type: replace Abstract: The electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community. Although regulatory measures have reduced air pollutants, fossil fuels remain a significant component of the energy supply, highlighting the need for more advanced demand-side approaches to reduce the public health impacts. To enable health-informed demand-side management, we introduce HealthPredictor, a domain-specific AI model that provides an end-to-end pipeline linking electricity use to public health outcomes. The model comprises three components: a fuel mix predictor that estimates the contribution of different generation sources, an air quality converter that models pollutant emissions and atmospheric dispersion, and a health impact assessor that translates resulting pollutant changes into monetized health damages. Across multiple regions in the United States, our health-driven optimization framework yields substantially lower prediction errors in terms of public health impacts than fuel mix-driven baselines. A case study on electric vehicle charging schedules illustrates the public health gains enabled by our method and the actionable guidance it can offer for health-informed energy management. Overall, this work shows how AI models can be explicitly designed to enable health-informed energy management for advancing public health and broader societal well-being. Our datasets and code are released at: https://github.com/Ren-Research/Health-Impact-Predictor. ## From Prediction to Action: Population Health World Model for Digital Medicine DevFeed: [From Prediction to Action: Population Health World Model for Digital Medicine]() Original publisher: [Read original article]() Author: Xike Xie, Yu Li, Weizhong Yang, Amalie Dyda, Shaohua Kevin Zhou Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [World models](), [Machine learning]() Tags: [actions](), [cs-ai](), [data](), [electronic-health-records](), [equity](), [feedback](), [governance](), [health](), [intelligence](), [learning](), [medical](), [medicine](), [mobile-devices](), [model](), [modeling](), [monitoring](), [prediction](), [public-health](), [sensing](), [social](), [world-model]() ### AI overview The paper argues that digital medicine should move beyond outcome-specific prediction toward population health world models (PHWMs) that represent how interventions alter health trajectories and population dynamics amid heterogeneous populations, scarce resources, and feedback in care. It identifies gaps in dynamic health-state modeling, population-level intervention intelligence, and validation, equity, and governance. The proposed minimum capabilities are representation learning under action-dependent observation, multi-scale intervention intelligence, and validation with explicit decision boundaries. It also highlights social, contextual, and public-health sensing data as important inputs. These are proposed requirements; the supplied abstract does not report evidence of a deployed or validated PHWM. ### Source excerpt arXiv:2610.24662v2 Announce Type: replace Abstract: Digital medicine now captures data that traces individual health trajectories and reveals population health dynamics through electronic health records, claims, remote monitoring, wearable devices, mobile devices, social sensing data, medical images, etc. But much of its modeling still centers on task-specific, outcome-specific, and prediction-specific estimation. The next frontier is not simply better prediction, but action in an evolving health world shaped by multiple decision-makers, heterogeneous populations, resource constraints, and feedback in care delivery. This is why digital medicine now needs world models: not only to estimate what will happen, but to represent how actions change what can happen next -- whom to prioritize, how to allocate scarce resources, how to adapt care across subgroups, and how interventions reshape future health trajectories and population dynamics. We call such a model population health world model (PHWM): a more foundational representation of an evolving health world in which health states, individual trajectories, population health dynamics, interventions, constraints, and feedback are intrinsically coupled. Building a PHWM endows three unmet structural gaps in current digital medicine: dynamic health-state modeling, population-level intervention intelligence, and validation, equity, and governance for action-oriented use. We argue that addressing these gaps requires three minimal capabilities: representation learning under action-dependent observation, multi-scale intervention intelligence, and validation with explicit decision boundaries, while highlighting the importance of social, contextual, and public-health sensing data for action-oriented digital medicine. ## From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations DevFeed: [From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations]() Original publisher: [Read original article]() Author: Abhishek A. Sabnis, Mihai Mitrea, Lya Lugon, Karine Sartelet, Marc Bocquet, Xiaoyuan Cheng, Shupeng Zhu, Sibo Cheng Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [Machine learning](), [Deep learning](), [Model Development](), [data augmentation]() Tags: [air](), [air-quality](), [cs-ai](), [cs-lg](), [data-augmentation](), [deep-learning](), [diffusion](), [generative](), [monitoring](), [paris](), [public-health](), [real-world](), [simulation](), [spatial]() ### AI overview The study reconstructs citywide NO2, O3, PM2.5 and PM10 fields from sparse station readings using models trained on full-field simulations. It benchmarks a diffusion-based generative method against deterministic deep learning models, evaluating them on Paris observations from 9 to 28 monitoring stations. The abstract reports high structural similarity on simulated validation data and realistic spatial patterns in real observations by power-spectrum analysis, despite noise and spatial variability. Data augmentation supports transfer to real-world observations without retraining and generalization beyond the training period. The excerpt does not provide quantitative comparisons between model types or further deployment results. ### Source excerpt arXiv:2607.25687v2 Announce Type: replace-cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making. However, the complex interactions among pollutants, hard-to-predict weather patterns, and limited monitoring station coverage make this a complex task. We apply deep learning techniques to provide fast and accurate reconstructions from sparse observations of four key pollutants: NO2, O3, PM2.5 and PM10. Models are trained on full-field simulation data and evaluated on real-world observations collected from 9 to 28 monitoring stations in the city of Paris. We introduce a diffusion-based generative framework for multi-pollutant reconstruction and benchmark its performance against deterministic deep learning models. Despite noisy observations and strong spatial variability, the models achieve high structural similarity on simulated validation data and produce realistic spatial patterns on real-world observations, as indicated by power-spectrum analysis. We introduce data augmentation methods that enable transfer to real-world observations without retraining, allowing the models to generalise beyond the training period. These findings highlight the potential of ML models for reliable real-world deployment in air pollution reconstruction tasks. ## Hybrid SEIR Temporal Fusion Transformer Framework with SHAP Analysis for Interpretable Pandemic Forecasting DevFeed: [Hybrid SEIR Temporal Fusion Transformer Framework with SHAP Analysis for Interpretable Pandemic Forecasting]() Original publisher: [Read original article]() Author: Ashish Anton Abraham, Gopi Krishnan R, Aiswarya Anil Nair, Ajo Babu George, Kunal Agarwal Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Predictive AI](), [Time Series](), [Data analysis](), [Neural Network](), [Data pipelines](), [Data visualization]() Tags: [covid-19](), [cs-lg](), [dashboards](), [data](), [forecasting](), [geospatial](), [hybrid](), [physics-soc-ph](), [public-health](), [shap](), [temporal](), [tft](), [transformer]() ### AI overview The framework combines an SEIR model, which estimates latent disease dynamics and supplies mechanistic priors, with a Temporal Fusion Transformer that learns temporal patterns and feature interactions from region-wise COVID-19 data. Inputs include epidemiological indicators, interventions, mobility and demographics. SHAP explanations quantify feature contributions globally and for individual forecasts. The abstract reports more stable and accurate forecasts than a calibrated standalone SEIR baseline across epidemic phases, especially when interventions shift. Structured forecasts and explanations are designed for interactive time-series, policy-impact and geospatial dashboards. These are claims from the abstract; it does not provide quantitative evaluation details. ### Source excerpt arXiv:2610.31673v1 Announce Type: cross Abstract: Forecasting pandemic spread across heterogeneous regions demands models that are both accurate and interpretable for public health action. This paper proposes a hybrid forecasting framework that integrates an SEIR epidemiological model enhanced with a Temporal Fusion Transformer (TFT) to produce interpretable multi-horizon infection forecasts. Using region-wise COVID-19 data from the Our World in Data corpus, we present a unified pipeline that integrates epidemiological indicators (susceptible, exposed, infectious, recovered), intervention signals (masking, vaccination, policies), mobility covariates, and demographic context. The SEIR component estimates latent disease dynamics and injects mechanistic priors, while the TFT learns non-linear temporal dependencies and cross-feature interactions from historical observations and known future inputs. To ensure transparency, SHAP (SHapley Additive exPlanations) is applied at both global and local levels, quantifying feature contributions and revealing region-specific drivers of surges and slowdowns. Compared with a calibrated standalone SEIR baseline, the hybrid SEIR-TFT approach provides more stable and accurate forecasts across epidemic phases, particularly under shifting intervention regimes. The framework is further operationalized through structured prediction and explanation outputs for interactive dashboards, enabling time-series, policy-impact, and geospatial analysis. By unifying mechanistic modeling, deep temporal learning, and explainable AI, this study offers a practical decision-support system for policy-sensitive pandemic forecasting. ## Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints DevFeed: [Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints]() Original publisher: [Read original article]() Author: Vasundhara Acharya, B\\"ulent Yener Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Post-training optimization]() Tags: [bayesian](), [bootstrap](), [capacity](), [cs-lg](), [distillation](), [effect](), [jaccard](), [machine-learning](), [models](), [policies](), [policy](), [public-health](), [robust](), [rules](), [stable](), [state](), [student](), [study](), [uncertainty](), [validation]() ### AI overview The study tests whether simpler models can reproduce budgeted targeting decisions from flexible causal-effect models when no historical decision policy is logged. Causal Forest and Bayesian Causal Forest teachers estimate treatment effects; decision tree and random forest students approximate them. At a 70% capacity cap, students came within 0.011 absolute utility of their teachers across PIMA obesity and glucose and NHANES smoking-history experiments, with allocation Jaccard similarities of 0.738-0.937. K-means subgroup and matched-size causal-tree policies were also competitive, and the highest point estimate varied by application. Paired comparisons were nonsignificant after Holm adjustment; agreement near the budget boundary was less stable for some teacher-student pairs. The observational estimates rely on causal-identification assumptions and need longitudinal or prospective validation. ### Source excerpt arXiv:2610.25627v1 Announce Type: new Abstract: Personalized public-health targeting must capture heterogeneous intervention benefits while remaining interpretable under resource constraints. Flexible causal-effect models can be hard to operationalize, while coarse subgroup policies may lose individualized effect information. We study budget-constrained hypothetical state interventions in observational data without a logged historical decision policy. For obesity, elevated glucose, and lifetime smoking-history states, causal-structure-informed pretreatment covariates are prespecified, with capacity capped at 70% of those observed in the adverse state. We evaluate causal distillation where Causal Forest and Bayesian Causal Forest teachers estimate conditional average treatment effects (CATEs), which decision tree and random forest students approximate. We compare individualized policies with unsupervised K-means subgroup policies and supervised causal-tree policies with leaf counts matched to K-means cluster counts. Observational policies are assessed on held-out data using a doubly robust estimator, bootstrap uncertainty, and decision-focused teacher-student fidelity measures. Across PIMA obesity and glucose and NHANES smoking-history experiments, each distilled student was within 0.011 absolute utility of its teacher, with allocation Jaccard similarities of 0.738-0.937. K-means and causal-tree policies were also competitive, with the highest point-estimate policy varying by application. Prespecified paired comparisons were nonsignificant after Holm adjustment, while near-budget-boundary agreement was less stable than global fidelity for some teacher-student pairs. Findings support causal distillation for translating flexible effect models into simpler budgeted targeting rules, although observational estimates depend on causal-identification assumptions and require longitudinal or prospective validation. ## GrabMart data for faster disease outbreak detection DevFeed: [GrabMart data for faster disease outbreak detection]() Original publisher: [Read original article]() Author: Muqi Li Published: 2026-10-13T00:23:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Data analysis](), [Data Quality](), [Detection engineering]() Tags: [ai](), [analytics](), [bias](), [data](), [engineering](), [governance](), [health](), [outbreak](), [process](), [public-health](), [validation]() ### AI overview The article outlines an early-stage proposal to use aggregated GrabMart purchases of symptom-related products as an additional signal for public health surveillance. It describes anomaly detection against seasonal baselines, statistical validation, and privacy and governance controls. The concept is not deployed, and the signal cannot diagnose disease or replace clinical surveillance. ### Source excerpt Abstract Retail purchases can supplement clinical surveillance because changes in demand for some over-the-counter (OTC) products may appear before confirmed case reports. This article describes an early-stage concept for analysing aggregated GrabMart transaction data to identify unusual changes in product demand. The concept is not a deployed system, and it would require validation by public health experts, privacy review, and regulatory approval before any implementation. Introduction Public health agencies use sources such as clinical case reports, laboratory results, and hospital records to monitor disease activity. Retail data may offer an additional signal when demand changes across groups of symptom-related products. It cannot establish a diagnosis or replace clinical evidence. When using indirect signals, we should assess whether they remain relevant to the outcome we want to measure. This includes considering how changes in user behaviour could affect their interpretation. A retail-based signal can experience similar drift, selection bias, and false alarms. Any useful design must therefore combine statistical validation with careful privacy and governance controls. Background Syndromic surveillance tracks patterns that may indicate emerging illness before diagnoses are confirmed. Retail surveillance applies this idea to changes in demand for products associated with symptoms, such as fever remedies or cough medicines. The proposed method groups eligible products into broad symptom classes and measures changes in aggregate demand over time. An anomaly score can indicate that observed demand differs from an expected seasonal pattern. The score is a statistical signal, not an estimate of how many people have a disease. The design uses the following terms: Proposed solution The proposed pipeline would process only transactions that meet documented eligibility rules. It would map eligible SKUs to symptom classes, aggregate counts across approved time periods and ## Google Earth AI's Population Dynamics Foundation Model supports public health research DevFeed: [Google Earth AI's Population Dynamics Foundation Model supports public health research]() Original publisher: [Read original article]() Published: 2026-10-06T15:05:11Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Foundation Models](), [geospatial](), [Earth AI](), [lunar foundation model]() Tags: [ai](), [earth](), [earth-ai](), [foundation-models](), [geospatial](), [global](), [health](), [health-bioscience](), [public-health]() ### AI overview Google Research presents five partner-led case studies evaluating its Population Dynamics Foundation Model (PDFM), which turns privacy-preserving population and environmental signals into location embeddings for epidemiological workflows. Across studies involving measles vaccination, cardiovascular disease, dengue, postpartum depression and cholera, the embeddings supplemented existing data and models, with reported benefits varying by task and forecast horizon. The article also notes limitations including static snapshots and ongoing work on dynamic embeddings and geographic transfer. ### Source excerpt Earth AI ## MIT students gain a humanist lens on technical innovation in Tulsa, Oklahoma DevFeed: [MIT students gain a humanist lens on technical innovation in Tulsa, Oklahoma]() Original publisher: [Read original article]() Author: PKG Center Published: 2026-09-25T20:15:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Data Science](), [data](), [AI Development](), [decision-making]() Tags: [ai](), [alexandra-leviness](), [alison-badgett](), [art-culture-and-technology](), [artificial-intelligence](), [black-tech-street](), [cherokee-nation](), [cities](), [classes-and-programs](), [code-tulsa](), [commonwealth-fusion-systems-cfs](), [communities](), [computer-science-and-technology](), [data](), [data-science](), [data-sovereignty](), [education-teaching-academics](), [ethics](), [governance](), [health-sciences-and-technology](), [humanities](), [indigenous-mit-students](), [k-12-education](), [mit](), [mit-pkg-center](), [muscogee-creek-nation](), [pkg-center-for-social-impact](), [pregnancy-risk-assessment-monitoring-system-prams](), [public-health](), [school-of-humanities-arts-and-social-sciences](), [stem-education](), [students](), [technical](), [technology-and-society](), [tulsa-advanced-sciences-camp-tasc](), [urban-coders-guild](), [vippy-yee](), [volunteering-outreach-public-service]() ### AI overview An MIT Code.Tulsa internship program gave students experience applying AI, data science, and technical skills alongside community-based learning. Students supported the Muscogee Nation's first Pregnancy Risk Assessment Monitoring System survey while considering tribal history, data sovereignty, governance, and the people affected by technical work. ### Source excerpt The PKG Center for Social Impact expands Code.Tulsa experiential learning program. ## Bailey Flanigan uses computational and mathematical tools to study democratic participation DevFeed: [Bailey Flanigan uses computational and mathematical tools to study democratic participation]() Original publisher: [Read original article]() Author: Michaela Jarvis | MIT Laboratory for Information and Decision Systems Published: 2026-07-17T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Computer science](), [Electrical engineering and computer science (EECS)](), [Mathematics](), [MIT Schwarzman College of Computing]() Tags: [ai-and-democracy](), [ai-in-politics](), [algorithms](), [artificial-intelligence](), [bailey-flanigan](), [computer-science](), [computer-science-and-technology](), [defending-democracy](), [democracy](), [democratic-decision-making](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [government](), [labor-and-jobs](), [laboratory-for-information-and-decision-systems-lids](), [math](), [mit-eecs-faculty](), [mit-faculty-profile](), [mit-lids](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [political-legitimacy](), [political-science](), [politics](), [profile](), [public-health](), [research](), [school-of-engineering](), [school-of-humanities-arts-and-social-sciences](), [science](), [technology-and-society](), [wisconsin-innocence-project]() ### AI overview An MIT faculty member, Bailey Flanigan studies how computational and mathematical tools can create new avenues for meaningful democratic participation. The article describes her interdisciplinary path across computer science, political science, medicine, public health, economics, and related fields. ### Source excerpt Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive. ## New method aims to keep kids safe from illegal AI-generated content DevFeed: [New method aims to keep kids safe from illegal AI-generated content]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-13T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ai safety](), [AI Research](), [Open Source]() Tags: [ai](), [ai-deepfakes](), [ai-safety](), [algorithms](), [artificial-intelligence](), [ashia-wilson](), [computer-science-and-technology](), [csam](), [ethics](), [generative](), [generative-ai](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [marzyeh-ghassemi](), [mit-schwarzman-college-of-computing](), [models](), [online-child-safety](), [open-source](), [privacy](), [public-health](), [research](), [safety](), [school-of-engineering](), [vinith-suriyakumar]() ### AI overview MIT researchers and Thorn developed an auditing technique that assesses whether a generative AI model has been specialized to produce child sexual abuse material without generating illegal outputs. In testing, the procedure identified specialized model variants with 100 percent accuracy. ### Source excerpt Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. ## Strengthening societal resilience with Rosalind Biodefense DevFeed: [Strengthening societal resilience with Rosalind Biodefense]() Original publisher: [Read original article]() Published: 2026-05-29T03:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Resilience](), [Frontier AI](), [future of AI](), [Critical Infrastructure]() Tags: [ai](), [biodefense](), [developers](), [frontier-ai](), [government](), [gpt-rosalind](), [health](), [launches](), [openai](), [pandemic-preparedness](), [partners](), [product](), [public-health](), [resilience](), [rosalind](), [trusted-access]() ### AI overview OpenAI is launching Rosalind Biodefense to help vetted teams use GPT-Rosalind for practical biological defense and pandemic preparedness, while expanding trusted access for select U.S. government and allied public-health partners. The program sponsors model access and launch support for qualified developers, including academic, nonprofit, government-affiliated, and mission-driven teams; potential work includes early detection, epidemiological modeling, preparedness, diagnostics, outbreak response, and medical countermeasures. OpenAI says access is paired with safeguards such as capability assessments, evaluations, monitoring, expert red teaming, and security controls. Named efforts include Lawrence Livermore National Laboratory's AI, simulation, and laboratory work on countermeasures; Johns Hopkins Applied Physics Laboratory's planned protein-engineering screening platform; and access for CEPI in support of its vaccine-focused 100 Days Mission. The announcement describes an initial step, with access pathways and safeguards expected to evolve. ### Source excerpt OpenAI launches Rosalind Biodefense, expanding trusted access to GPT-Rosalind for vetted developers and U.S. government partners advancing biodefense, public health, and pandemic preparedness through frontier AI. ## A Science of Cybersecurity Public Health DevFeed: [A Science of Cybersecurity Public Health]() Original publisher: [Read original article]() Author: Adam Published: 2022-06-02T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Cybersecurity](), [Security & Privacy]() Tags: [blog-post](), [cybersecurity](), [public-health](), [science]() ### AI overview The article discusses developing public health as a way of thinking about cybersecurity, in collaboration with the CyberGreen Institute. ### Source excerpt I've been working with the CyberGreen Institute to develop public health as a way of thinking about cybersecurity. ## Vitamin D Research and Potential Implications for COVID-19 DevFeed: [Vitamin D Research and Potential Implications for COVID-19]() Original publisher: [Read original article]() Published: 2020-05-30T15:52:44Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [data](), [Internet]() Tags: [biology](), [covid](), [covid-19](), [health](), [opinion](), [public-health](), [research]() ### AI overview This commentary reviews polarized research and expert views on vitamin D, sunlight, and COVID-19. It presents the author's view that avoiding vitamin D deficiency may be important during the pandemic while acknowledging that evidence remains incomplete and contested. ### Source excerpt Welcome to this post, in which we'll be taking a trip through the wild and bumpy ride that has been Vitamin D & sunlight research over the past few decades, and what it might mean for COVID-19. As with my previous post, before any non-specialist attempts to write something on COVID-19, we should wonder: are any actual experts already writing on this, and if not, why not. Enough badly informed stuff has already been published by amateurs that before posting we should be certain we are actually being helpful. ## Phone-Based Contact Tracing, Privacy, and Proximity Detection: Thoughts and Links DevFeed: [Phone-Based Contact Tracing, Privacy, and Proximity Detection: Thoughts and Links]() Original publisher: [Read original article]() Published: 2020-04-11T11:46:27Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [App](), [Protocol (disambiguation)](), [Localization (l10n)](), [Internet]() Tags: [app](), [decentralized](), [privacy](), [protocol](), [public-health](), [tracing]() ### AI overview The article considers whether phone apps and internet-connected devices could support automated COVID-19 contact tracing while preserving privacy. It discusses the limits of GPS, GNSS, Wi-Fi, and phone-location data for measuring close proximity, then reviews decentralized proposals such as DP-3T, CEN/TCN, and the Apple/Google protocol. ### Source excerpt As many countries are now managing to drive down their COVID-19 epidemic, thoughts are turning to how to manage the outbreak beyond the "lockdown". The restrictions we live under now do appear to work, but for multiple reasons, they'll be hard to maintain. When the epidemic was small, it was possible to trace every new infection and isolate potential new cases. This did buy time, but for most countries, "the cat was out the bag already". ## Public disclosure of C. auris outbreaks supports public health and security DevFeed: [Public disclosure of C. auris outbreaks supports public health and security]() Original publisher: [Read original article]() Author: Adam Published: 2019-04-08T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Security & Privacy](), [Cybersecurity]() Tags: [cybersecurity](), [medicine](), [public-health]() ### AI overview The article argues that hospitals and governments should publicly disclose outbreaks of resistant infections such as C. auris. It connects transparency in public health with cybersecurity, warning that hiding harmful outcomes prevents society from making informed choices and addressing risks. ### Source excerpt Secrecy isn't the best policy when it comes to public health.