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The purpose of the guideline is to provide information to stakeholders on the necessary requirements for a complete prequalification dossier for insecticide-treated nets (ITNs). Its aim is to establish the baseline for dossier requirements which are necessary to assess ITN products for the purposes
...
of prequalification, describe the data requirements for fulfilling each dossier module, and to provide standardized information for applicants and testing facilities generating data for ITN prequalification dossiers. The document is supported by implementation guidance documents which provide specific information and considerations for how applicants may approach the generation of supporting information and compilation of a complete product dossier.
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Available in English, French an Spanish. The Malaria Threats Map is an interactive data platform which provides a geographic overview of the status of the 4 biological threats to malaria control and elimination
Malaria No More is a non-profit organisation dedicated to eradicating malaria, a preventable and treatable disease, in our lifetime. Through innovative partnerships, advocacy and data-driven solutions, Malaria No More works globally to ensure access
...
to prevention tools, diagnostics and treatment, particularly in vulnerable regions. Malaria No More focuses on high-impact campaigns, technological innovation and policy engagement, collaborating with governments, health organisations and private sector partners to accelerate progress towards malaria eradication and save lives.
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The Malaria Atlas Project (MAP) is a global research initiative that provides high-resolution, evidence-based spatial data on malaria transmission, risk and impact. MAP combines field data, satellit
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e imagery, and advanced geostatistical modelling to deliver open-access maps, datasets, and analytical tools that support malaria control and elimination strategies worldwide. MAP empowers researchers, policymakers, and public health practitioners by providing them with accurate, up-to-date geographic insights to inform resource allocation and intervention planning.
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Internal displacements due to conflict and disasters are a major driver of global human mobility. While the total numbers of internal displacements by cause and geographical location are increasingly well tracked, a significant gap remains in the availability of disaggregated
...
data on key variables – such as age, sex, education, livelihood – for the populations impacted by these events. Data from localised case studies can provide this granularity; however, they are difficult to generalise to other contexts. This lack of disaggregated profiles complicates the work of decision makers tasked with allocating resources efficiently to address the diverse
vulnerabilities and needs of impacted communities
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In conclusion, the current evidence does not support the use of mpox antigen RDTs in the field. More data are expected from independent evaluations conducted by Africa CDC, FIND or other organizations
Surveillance of influenza virus infection at the European level requires close collaboration between virologists, epidemiologists and sentinel GP networks to generate the data necessary to inform a timely public health response.
Operational requirements for implementing WHO recommendations in digital systems. 2nd edition.
To ensure that countries can effectively benefit from digital health investments, “digital adaptation kits” (DAKs) are designed to facilitate the accurate reflection of WHO’s clinical, public health
...
and data use guidelines in the digital systems that countries are adopting. DAKs are operational, software-neutral, standardized documentations that distil clinical, public health and data use guidance into a format that can be transparently incorporated into digital systems.
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The WHO Global Respiratory Syncytial Virus (RSV) Surveillance page describes a World Health Organization initiative under the Global Influenza Programme to monitor RSV infections worldwide. It explains that WHO uses the existing Global Influenza Surveillance and Response System (GISRS) to collect st
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andardized epidemiological and laboratory data on RSV, in order to understand patterns such as seasonality, disease burden, and age groups at highest risk, especially in young children. The surveillance system aims to support countries in tracking RSV activity, improve detection and laboratory capacity, and generate evidence that can guide public health policies, including the use of vaccines and preventive measures. Overall, the text emphasizes building a global platform for RSV surveillance integrated with influenza monitoring to inform better respiratory virus control strategies.
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The Guardian article reports that Google DeepMind has developed an artificial intelligence model capable of predicting weather more accurately than one of the world’s leading traditional forecasting systems. The AI system, trained on large amounts of historical weather
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data, can generate faster and more precise medium-range forecasts, potentially improving predictions of extreme weather events such as storms, heavy rainfall, and heatwaves. Scientists suggest that this advancement could enhance early warning systems and disaster preparedness worldwide, though experts also emphasize the importance of continued evaluation and collaboration with existing meteorological institutions.
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The webpage presents WeatherNext 2, an advanced AI-based weather forecasting model developed by Google DeepMind. It explains how the system uses machine learning to analyze large amounts of atmospheric data and produce fast and accurate weather pred
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ictions. The model is designed to improve forecasting for various weather variables such as temperature, wind, and precipitation, and to support better predictions of extreme weather events. The page also highlights how AI-driven forecasting can complement traditional numerical weather models and help scientists, governments, and organizations make better decisions related to weather and climate.
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The text explains what early warning systems are and why they are important for climate action and disaster risk reduction. It describes how these systems collect and analyze data to detect hazards such as storms, floods, heatwaves, or droughts and
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provide timely alerts to governments and communities. This allows people to prepare, evacuate, or take protective measures before disasters occur. The article also highlights that effective early warning systems can save lives, reduce economic losses, and strengthen resilience to climate change, especially in vulnerable regions.
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The text explains the National Outbreak Reporting System (NORS), a surveillance system used in the United States to collect and analyze data about disease outbreaks. It describes how health departments report outbreaks caused by food, water, person-
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to-person contact, environmental exposure, or unknown sources. The system helps public health authorities monitor trends, identify causes of outbreaks, and improve prevention strategies. By gathering and sharing outbreak information, NORS supports better responses to public health threats and helps reduce the spread of infectious diseases.
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The document “Guidelines for the Investigation and Control of Disease Outbreaks” provides practical guidance for public health professionals on how to detect, investigate, and manage outbreaks of communicable diseases. It describes the key steps of outbreak investigation, including confirming th
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e outbreak, establishing a case definition, collecting epidemiological and laboratory data, identifying the source and mode of transmission, and implementing control measures. The guidelines also explain how to organize outbreak response teams, communicate findings, and document results in outbreak reports. Overall, the document aims to support systematic and effective outbreak investigations in order to control disease spread and protect public health.
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Asia-Pacific Climate Change Adaptation Information Platform (AP-PLAT)
Center for Climate Change Adaptation (CCCA)
National Institute for Environmental Studies (NIES), Japan
(2026)
C2
The Asia-Pacific Climate Change Adaptation Information Platform (AP-PLAT) is an online platform designed to support climate change adaptation in the Asia-Pacific region. It provides scientific data, tools, and knowledge to policymakers, researchers,
...
businesses, and the public in order to improve understanding of climate risks and support evidence-based decision-making. The platform aims to strengthen resilience and sustainability by promoting collaboration between countries and institutions, sharing best practices, and enabling effective adaptation actions. Its core activities include generating and integrating scientific information on climate impacts, developing practical tools for adaptation planning, and building capacity through training and knowledge exchange. Overall, AP-PLAT functions as a central hub that connects science with stakeholders and facilitates informed responses to climate change across the region.
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Front. Public Health 10:876949. doi: 10.3389/fpubh.2022.876949.In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB)
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therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work.
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A crucial element in accelerating progress is the development of improved surveillance systems and tools that provide decision-makers with timely, high-quality data and actionable insights. The investments made to establish genomic platforms for COV
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ID-19 surveillance have catalyzed a genomic revolution—one that can now be leveraged to strengthen the surveillance of endemic diseases such as malaria.
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This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data
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fed into the algorithm. It has been predicted that LMMs will have wide use and application in health care, scientific research, public health and drug development. LMMs are also known as “general-purpose foundation models”, although it is not yet proven whether LMMs can accomplish a wide range of tasks and purposes.
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2nd edition. The document outlines a structured approach to developing a comprehensive sodium reduction strategy, including preparatory steps such as establishing governance mechanisms, engaging stakeholders and investing in data systems. It present
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s a set of evidence-based policies and interventions, with an emphasis on mandatory approaches, including food reformulation, nutrition labelling, public procurement standards, marketing restrictions, fiscal measures and behaviour change communication
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Health Informatics Journal 1–38. AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generali
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zability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.
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