Epidemiology Data Collection Methods

The World Health Organization Hub in #Berlin: driving innovation to make the world safer from health threats WHO is developing new tools and innovative partnerships to boost countries’ defenses against future #pandemics, including real-time #threat #detection and #genomic analysis of #viruses In today’s interconnected world, health threats spread faster than ever. A new virus can cross continents in hours. An outbreak in one country can escalate into a global crisis in days. This reality requires constant innovation to protect lives and prevent the next pandemic Building on lessons learned from the #COVID-19 pandemic, the WHO Hub for Pandemic and Epidemic Intelligence in Berlin leverages innovative tools and collaborations for more effective disease surveillance worldwide. Just over three years after its inauguration, the Hub now supports over 150 countries in detecting health threats more effectively and rapidly The Hub’s latest annual report highlights the growing impact of this work and provides key insights into progress made in 2024 As no country can tackle the next pandemic alone, WHO is supporting countries to implement Collaborative Surveillance, a new collaborative approach to disease surveillance that promotes data and information sharing so that outbreaks can be detected and controlled faster The early warning system hosted at the Hub, called Epidemic Intelligence from Open Sources, scans online sources in real time and uses #AI technology to identify #publichealth threats more efficiently “The Hub is ensuring that the most robust tools and analytics are available to enhance early threat detection and rapid response and support decision-makers around the world,” said Tedros Adhanom Ghebreyesus, WHO DG. “I have urged all WHO Member States to work closely with the Hub, not only to strengthen their own national and regional health security, but also to contribute to global preparedness and response.” Pathogen genomics, which analyses the genetic material of viruses and other pathogens, has become a powerful tool to track and predict outbreaks. The Hub’s International Pathogen Surveillance Network connects over 235 organizations and countries to expand genomic surveillance more equitably around the world, including through a US$ 4 million fund for low- and middle-income countries To help decision-makers better understand an emerging #health #emergency and plan an effective response, the Hub is developing a cutting-edge platform that will visualize disease transmission and simulate the impact of different countermeasures. Once launched, the pandemic simulator will provide actionable insights to policy-makers and support them in responding to a health #crisis WHO remains at the forefront of developing tools, building partnerships and strengthening public health intelligence and surveillance capacities worldwide

➡️ New Pre-Print Paper (Arxiv): 📄 Non-traditional data in pandemic preparedness and response: identifying and addressing first and last-mile challenges ✍️ by Mattia Mazzoli, Irma Varela-Lasheras, Sónia Namorado, Constantino Pereira Caetano, Andreia Leite, Lisa Hermans, Niel Hens, Polen Türkmen, Kyriaki Kalimeri, Leo Ferres, Ciro Cattuto, Daniela Paolotti, and Stefaan Verhulst 🔗 Read it here: https://lnkd.in/eXXe2tGX 🤔 The COVID-19 pandemic served as a global stress test for how we integrate non-traditional data (NTD)—from mobility traces and social media activity to wearable data—into public health decision-making. ➡️ As part of the ESCAPE project, and drawing on an expert workshop (Brussels, 2024) and a survey of European modelers, our paper assesses both the promise and persistent limitations of NTD in pandemic preparedness and response. We distinguish between: 🧭 “First-mile” challenges — accessing, harmonizing, and standardizing data. 🚦 “Last-mile” challenges — translating insights into timely, actionable decisions. Some Key findings include: - 66% of datasets faced access issues; - Data-sharing reluctance for NTD was twice that of traditional data (30% vs. 15%); - Only 10% of respondents could use all the data they needed. 🤔 Barriers extend beyond the technical—encompassing institutional inertia and weak data-to-policy translation. To move forward, we propose a roadmap that emphasizes (among other things): ✅ Legal and technical frameworks for data access; ✅ Fusion Centers and Decision Accelerator Labs to bridge analysis and action; ✅ Networks of Scientific Ambassadors to connect scientists and policymakers; ✅ A shift toward a culture of data solidarity and sustained institutional readiness ✅ Advancing data stewardship. ➡️ Grounded in the lessons of COVID-19, this work aims to guide the responsible use of non-traditional data not only for pandemics, but also for emerging challenges like climate shocks and humanitarian crises. 💻 Learn more about ESCAPE: https://lnkd.in/eTU5-7DD ISI Foundation, The Data Tank #DataForHealth #Pandemic #NonTraditionalData #DataStewardship #DataSolidarity #PublicHealth #AIForGood #EvidenceBasedPolicy #DataGovernance

This new paper by Sergio Consoli et al explores how generative AI can transform unstructured outbreak data into structured, searchable knowledge. The team developed an epidemiological knowledge graph (eKG) using WHO Disease Outbreak News (DONs), applying an ensemble of large language models to extract details such as disease name, country, date, and number of cases or deaths. The researchers used open-source models including Mistral, Zephyr, and Meta-Llama to extract information from over 3,000 outbreak reports. They structured this data into a FAIR-compliant knowledge graph, linking it with biomedical and geographic ontologies. The resulting resource—comprising nearly 3,000 outbreak events—is now publicly accessible via SPARQL endpoints and visualization tools. This matters because many official outbreak reports remain locked in prose, making them difficult to analyze at scale. With eKG, public health professionals can conduct detailed, structured queries across decades of global outbreak data. This significantly improves our ability to track, analyze, and respond to emerging health threats. The big takeaway: AI can unlock the full value of legacy outbreak data by transforming it into structured, interoperable formats that support real-time analysis and response. This approach opens new possibilities for integrating informal sources like news and social media into formal disease surveillance systems, advancing global preparedness and early warning capabilities. https://lnkd.in/ePc54yvQ #GlobalHealth #PathogenSurveillance #HealthInnovation #PublicHealth

📊 Most medical data lives in unstructured clinical notes, and too often, it not used. Every day, physicians write progress notes, imaging reports, and pathology summaries filled with signals: treatment responses, adverse events, even early signs of success or failure. But buried in free text, this information is unusable for traditional analysis. Clinical trials remain the gold standard, but they’re expensive, slow, and often unfeasible. So how do we unlock the evidence hidden in messy medical records? That’s what TRIALSCOPE set out to do. 🔹 By combining biomedical language models, probabilistic approaches, inferences, TRIALSCOPE automatically structured EMR data from over 1 million cancer patients. 🔹 It reproduced results of published lung cancer trials, generalized to pancreatic cancer, and simulated studies. 🔹 Compared with manual curation, it achieved >20× faster processing and 10× lower cost. -> Clinical text isn’t noise. With the right tools, it’s raw data waiting to become real world evidence.

In epidemiology, numbers don’t just describe reality—they guide life-saving decisions. A simple confidence interval from a flu outbreak (30% infection rate; 95% CI: 25.5%–34.5%) goes beyond statistics. It quantifies uncertainty, strengthens inference, and informs action. For public health, this determines the scale of interventions—vaccination strategies, resource allocation, and outbreak containment. For curative medicine, it shapes clinical preparedness—bed capacity, drug supply chains, and workforce planning. At a policy level, confidence intervals are the bridge between data and decision-making. They ensure that health systems respond not just to point estimates, but to the range of plausible realities—a critical factor in building resilient, evidence-driven systems. In an era of pandemics, antimicrobial resistance, and constrained resources, mastering such analytical tools is no longer optional—it is foundational. Data doesn’t just inform policy. Properly interpreted, it protects populations. #Epidemiology #PublicHealth #HealthSystems #EvidenceBasedPolicy #Biostatistics #GlobalHealth

Our new paper was published in Nature Medicine this week (https://lnkd.in/gWE9NcWj), in which we investigated a major febrile illness outbreak in the Panzi Health Zone, Democratic Republic of the Congo, with no known cause. I got involved in the project while teaching a bioinformatics course at the National Biomedical Research Institute (INRB) in Kinshasa. When the outbreak first emerged, there was concern that a novel pathogen might be circulating. Through coordinated clinical investigation, molecular diagnostics and metagenomic analysis, we demonstrated that the surge in cases was primarily driven by Plasmodium falciparum malaria, often occurring alongside respiratory viral infections and, in some cases, invasive bacterial disease. My contribution focused on bioinformatics and high-performance computational analyses. We leveraged Australia’s national supercomputing infrastructure at the Pawsey Supercomputing Research Centre, using the Setonix system to process and analyse large-scale sequencing datasets. High-resolution metagenomic analysis was essential to disentangle co-infections and rule out the presence of a novel agent. What makes this study particularly important is its broader lesson: in endemic settings, overlapping infections, malnutrition and constrained diagnostic capacity can create outbreak signals that resemble something entirely new. Robust computational pathogen surveillance that leverages local laboratory systems is critical for rapid, evidence-based public health responses. An absolutely stellar team came together for this international effort. This work underscores how bioinformatics and national HPC infrastructure can directly support global outbreak investigation and public health decision-making, even when we are continents apart.

Exactly six years ago, on 16 March 2020, the UK's Chief Medical Officer, Chris Whitty, announced the high-risk conditions for COVID-19 which required special precautions to reduce risk of hospitalisation and death. The following day, a patient saw me in clinic asking the cause of his higher risk. 79 people had died from COVID-19 in the UK by then. Using national electronic health records, our team began a 72-hour analysis. We finalised our report and sent it to the CMO's office on 22 March 2020, predicting more than 70,000 deaths over the next year if the infection rate was allowed to rise to 10% of the population. The PM announced lockdown on that evening of 23 March 2020. https://lnkd.in/eS_BCbGR As a group of researchers led by Kamlesh Khunti, we have continued to regularly meet to discuss COVID and Long COVID throughout this period. It is apt that six years later, to the day, we published our recommendations, led by Daniel Ayoubkhani, for future research into post-infectious conditions such as Long COVID. "Reliable insights into the epidemiology of Long Covid were essential for informing the public health response during the COVID-19 pandemic, and they continue to be needed to inform public service provision and spending decisions as society and the economy recover from the pandemic." https://lnkd.in/exBwDgx4

Whose job is it to make sense of a new health threat? Twelve months ago, Rachael Pung and I published some considerations for where infectious disease modelling* could be most effective, based on perspectives from the UK and Singapore COVID responses. (*in the broadest sense, from situational awareness and scenario analysis to parameter estimation and characterisation of dynamics) Here’s a summary of what we thought was particularly important to consider when deciding who should be responsible for what: 1. Operational vs research needs: Real-time operational analysis can often work better within public health teams, while deeper, more complex research may fit better in academic groups. 2. Speed and frequency of tasks: High-cadence outbreak metrics are often better handled in-house to reduce bottlenecks in delivering and communicating results, and freeing up capacity to improve methods and provide advisory oversight from academics. 3. Funding stability: Routine modelling work needs sustained roles that academia’s short-term grants often can’t provide, making agencies the logical home for ongoing essential analytics. 4. Scalability of expertise: Skilled analysis of disease dynamics can’t be conjured on demand, so both sectors need mechanisms to pull in and support adjacent expertise when crises hit (e.g. like the Royal Society RAMP Scheme). 5. Value of diverse perspectives: Complex questions can benefit from multiple methodological viewpoints, while simpler analyses with consensus methodology may be better in-house to avoid unnecessary duplication. 6. Data sensitivity and governance: Work requiring identifiable or high-resolution data usually is better placed inside agencies unless strong governance frameworks are in place. 7. Iterating for future crises: The right balance will shift over time, and effective outbreak response depends on government and academia-linked teams working together as priorities change.

#Alhamdulillah #newpaperalert Pleased to share our new research mapping the exact regions in Pakistan that are hardest hit by income shocks and hunger—and the results provide a blueprint for justice. Key Highlight: Pandemics do not affect everyone equally. Using COVID-19 as a case study, our new spatial analysis reveals that income shocks and food insecurity in Pakistan formed stark geographic patterns. The worst-hit region for income loss was Balochistan, while Sindh became the hotspot for hunger. Why is this Important? Blanket national policies often miss the mark. Our study provides a precise map of vulnerability. It proves that crises like pandemics deepen existing inequalities, hitting the least developed areas and most vulnerable populations the hardest. The Driving Factors: We identified what really drove the crisis: --Lack of Asset Ownership --High Illiteracy Rates --Precarious Employment Future Prospects: This isn't just about the past; it's a warning and a guide for the future. Our model provides governments and NGOs with a tool to target interventions with pinpoint accuracy, ensuring aid reaches the right places before a crisis becomes a catastrophe. This is how we build resilience. Share to spread the word on data-driven policy. Link to full text: https://lnkd.in/eYct9-fh #FoodSecurity #DataForGood #SpatialAnalysis #Inequality #GlobalHealth #Pakistan #COVID19 #Policy #Resilience

How AI and LLMs Could Change the Game in Pandemic Preparedness What if the next big health breakthrough came from something as unexpected as wastewater? METAGENE-1, a state-of-the-art 7-billion-parameter AI Large Language Model (LLM), is proving it can. By analyzing billions of DNA and RNA fragments from sewage, this LLM detects early signals of emerging pathogens and gives insights that are priceless, not just for public health, but also for the business world looking toward resilience. 🔹 Research Focus In the paper (https://lnkd.in/dME-zehS) the authors Ollie Liu, Jason Wiemels, Shangshang Wang, Willie Neiswanger (University of Southern California), Sami Jaghouar, Johannes Hagemann (Prime Intellect) and Jeff Kaufman (SecurBio) introduce METAGENE-1. It can processes more than 1.5 trillion base pairs of metagenomic data to track unusual genetic markers present in wastewater samples, helping to track health risks before they escalate. This makes decisions quicker and wiser for both communities and organizations. 🔹 Key Applications - Pandemic Monitoring: Pinpoint changes in microbial activity that warn of new variants or emerging threats. - Anomaly Detection: Pinpoint rare genetic shifts within the complex DNA and RNA sequences. - Business Continuity: Protect operations by responding early to health threats and reduce disruption caused. 🔹 Technological Features - Smooth Tokenization: The proposed LLM uses the BPE method to prepare highly diverse, fragmented genomic data. - Agnostic in Design: Tailor-made to find minute variations in pathogen behavior, even through unclean data in wastewater form. 📌 Key Takeaway - METAGENE-1 can accelerate responses toward outbreaks, thus helping with community health. - Business Benefits: Early detection will protect employees, and costly operation shutdowns may be avoided. - Collaborative Potential: As an open source model, this invites innovation across sectors to push the boundaries of biosurveillance. AI in the form of METAGENE-1 presents the fact that some of the innovation comes from completely unexpected places. This model now changes the whole way of considering preparedness-in preventing pandemics or business resilience-by turning wastewater into actionable data. 👉 How do you think AI and LLMs can enhance pandemic preparedness in public health? How can AI-powered genomic models help in future pandemic monitoring? 👈 #ArtificialIntelligence #AI #GenerativeAI #Healthcare #PublicHealth #ClinicalResearch #HealthTech #Genomics #Pandemic Subscribe to my Newletter: https://lnkd.in/dQzKZJ79