📖 Chinese libraries have book sterilization machines. UV-C light kills bacteria and flips pages automatically. Hygiene you can see builds trust. Sources: China Daily, SCMP #omgfacts# #Libraries# #PublicHealth#

❓ Does the flu shot really protect the elderly? Research shows it hasn’t reduced hospitalizations—raising real questions about effectiveness for our most vulnerable. See the data for yourself: #publichealth#

Defining Ultraprocessed Foods: In our latest post we answer 5 frequently asked questions about UPFs, our new definition developed for policy use, and what it means for researchers, advocates, consumers, and policymakers. #ultraprocessedfoods# #publichealth#

When people think about energy and health, they don't always consider the impact of air pollution. By reducing reliance on more polluting energy sources, nuclear has helped prevent around two million deaths linked to air pollution, with the potential to avert an additional seven million by 2050. Electricity from low-carbon nuclear power doesn't just support climate goals – it can also contribute to cleaner air and healthier communities. #nuclear# #energy# #PublicHealth# #ClimateGoals# #sustainability#

We are pleased to launch the Zero Leprosy Best Practices Toolkit! A comprehensive resource to strengthen prevention, treatment, & community engagement worldwide. Available in 5 languages (Bahasa, FR, HI, PT, ES). 👇 #ZeroLeprosy# #PublicHealth# #document#

Advancing towards our goal of zero disease, zero disability, and zero stigma: Our Q4 2025 Update is Here! Together, we are rooted in partnership and driven by change. Read the full newsletter here: #ZeroLeprosy# #GlobalHealth# #PublicHealth# #EndStigma#

What if you could build an epidemic outbreak prediction model in minutes rather than weeks? Title: Planetary prediction engine: Automating global models via Earth AI URL: Google's Planetary Prediction Engine (PPE) automatically builds geospatial prediction models from natural language queries. For problems like public health, food security, and infectious disease outbreaks, it compresses weeks of manual data curation and feature engineering into minutes — no specialized engineering team required. Key Points 🌍 A three-stage automated pipeline — from query to model Stage 1 translates natural language into geographic constraints and retrieves covariates from Data Commons and Google Earth Engine. Stage 2 fuses structured covariates with embeddings from Population Dynamics Foundation Models and AlphaEarth. Stage 3 searches across regularized linear models, gradient-boosted trees, and MLPs. Each stage embeds a "Feature Gate" (leakage prevention) and an "Overfitting Guard Protocol" (self-correcting risk assessment). 🔬 Multimodal fusion outperforms either modality alone — across every experiment Combining structured statistical covariates with latent foundation model embeddings captures information neither source provides individually. This synergy was confirmed across all experiments, establishing a new architectural pattern for geospatial prediction. 📊 Validated across three high-stakes domains ・US CDC health indicators (21 types): R² 76.8% vs 60.0% baseline ・Nigeria food security downscaling: R² 66.1% vs 31.5% baseline (2× improvement) ・DRC Ebola outbreak prediction: Recall@10 83.3% (+10.3 percentage points vs Bayesian baseline) The real impact is democratization: humanitarian organizations and researchers without dedicated engineering teams can now get evidence-based predictions within minutes when it matters most. #EarthAI# #PublicHealth#