Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Data Preparation and Visualization for Monitoring NCDs MortalityRamon Martinez
This is the slide deck of my talk at the Alteryx webinar Tableau Zen Masters - Preparing Data for the Conference, Oct 13, 2015.
It describes how we prepare data for analysis and visualization, particularly for assessing the trends of premature mortality from noncommunicable diseases.
Real Time Location Systems (RTLS) use sensors and wireless technologies to track the location of assets, people, and equipment in real time. RTLS has several applications in healthcare, such as monitoring patients, tracking medical equipment and supplies, improving patient flow, and monitoring workflows. The document discusses how RTLS works using location sensors and middleware to transmit location data. It also outlines different precision levels for locating tags, from presence in a room to precise coordinates. RTLS data can optimize workflows by identifying delays, bottlenecks, and inefficiencies. Regulations around RTLS are still developing to ensure privacy protections are in place as these systems collect large amounts of personal data.
A SAVVY Approach to Improving Mortality Information SystemsMEASURE Evaluation
The document discusses using the SAVVY Resource Kit to improve mortality information systems in developing countries. It describes how the kit has been used in Mozambique to collect representative cause-specific mortality data through a post-census mortality survey. It also discusses how the kit is being used in Brazil to increase mortality data coverage, especially in poor rural areas, and in Kenya to harmonize mortality data collection across demographic surveillance sites. Additionally, it mentions how the kit was adapted to conduct a post-DHS child mortality survey in Uganda to collect child-specific mortality information at a national level.
apidays LIVE Australia 2020 - Adaptable Digital Healthcare is built on well a...apidays
apidays LIVE Australia 2020 - Building Business Ecosystems
Adaptable Digital Healthcare is built on well architected APIs
Tim Eckersley, Enterprise Architect at NSW Health Pathology
The dog who caught the car: There’s more PEPFAR data than ever before, now what?MEASURE Evaluation
This document summarizes a presentation about using PEPFAR (President's Emergency Plan for AIDS Relief) data more effectively. It describes over a dozen years of USAID investments in PEPFAR data infrastructure, totaling nearly $1.9 billion. This includes indicators to support reporting systems, the DATIM data management system, and improved site capacity. With more data available than ever, the presentation discusses how it could be used to more effectively target HIV/AIDS services, identify priority sites, and better understand the epidemic context.
ASCVD EHR Integration Pilot Using FHIRDino Damalas
Copy of Dr. Gluckman's presentation during HIMSS 16 that discusses the value of the ASCVD Risk Estimator and pilots being conducted by the American College of Cardiology to integrate the tool with EHRs using FHIR.
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...MEASURE Evaluation
This document proposes using DHIS 2 Tracker to track mother-infant pairs across PMTCT services in order to reduce loss to follow up. Currently, high rates of loss to follow up occur when women do not complete preventive treatment or infants do not receive necessary testing. The DHIS 2 Tracker could track progress through the PMTCT cascade regardless of service location, track referrals, and link mother and infant data to improve infant monitoring. It would generate appointments, mark their completion, and issue alerts for missed appointments. This would allow programs to better monitor the PMTCT cascade and improve outcomes.
Data Preparation and Visualization for Monitoring NCDs MortalityRamon Martinez
This is the slide deck of my talk at the Alteryx webinar Tableau Zen Masters - Preparing Data for the Conference, Oct 13, 2015.
It describes how we prepare data for analysis and visualization, particularly for assessing the trends of premature mortality from noncommunicable diseases.
Real Time Location Systems (RTLS) use sensors and wireless technologies to track the location of assets, people, and equipment in real time. RTLS has several applications in healthcare, such as monitoring patients, tracking medical equipment and supplies, improving patient flow, and monitoring workflows. The document discusses how RTLS works using location sensors and middleware to transmit location data. It also outlines different precision levels for locating tags, from presence in a room to precise coordinates. RTLS data can optimize workflows by identifying delays, bottlenecks, and inefficiencies. Regulations around RTLS are still developing to ensure privacy protections are in place as these systems collect large amounts of personal data.
A SAVVY Approach to Improving Mortality Information SystemsMEASURE Evaluation
The document discusses using the SAVVY Resource Kit to improve mortality information systems in developing countries. It describes how the kit has been used in Mozambique to collect representative cause-specific mortality data through a post-census mortality survey. It also discusses how the kit is being used in Brazil to increase mortality data coverage, especially in poor rural areas, and in Kenya to harmonize mortality data collection across demographic surveillance sites. Additionally, it mentions how the kit was adapted to conduct a post-DHS child mortality survey in Uganda to collect child-specific mortality information at a national level.
apidays LIVE Australia 2020 - Adaptable Digital Healthcare is built on well a...apidays
apidays LIVE Australia 2020 - Building Business Ecosystems
Adaptable Digital Healthcare is built on well architected APIs
Tim Eckersley, Enterprise Architect at NSW Health Pathology
The dog who caught the car: There’s more PEPFAR data than ever before, now what?MEASURE Evaluation
This document summarizes a presentation about using PEPFAR (President's Emergency Plan for AIDS Relief) data more effectively. It describes over a dozen years of USAID investments in PEPFAR data infrastructure, totaling nearly $1.9 billion. This includes indicators to support reporting systems, the DATIM data management system, and improved site capacity. With more data available than ever, the presentation discusses how it could be used to more effectively target HIV/AIDS services, identify priority sites, and better understand the epidemic context.
ASCVD EHR Integration Pilot Using FHIRDino Damalas
Copy of Dr. Gluckman's presentation during HIMSS 16 that discusses the value of the ASCVD Risk Estimator and pilots being conducted by the American College of Cardiology to integrate the tool with EHRs using FHIR.
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...MEASURE Evaluation
This document proposes using DHIS 2 Tracker to track mother-infant pairs across PMTCT services in order to reduce loss to follow up. Currently, high rates of loss to follow up occur when women do not complete preventive treatment or infants do not receive necessary testing. The DHIS 2 Tracker could track progress through the PMTCT cascade regardless of service location, track referrals, and link mother and infant data to improve infant monitoring. It would generate appointments, mark their completion, and issue alerts for missed appointments. This would allow programs to better monitor the PMTCT cascade and improve outcomes.
Talk about data visualization as tool to add new value to health data, presented in the Panel: Old School Data Set, Rebooted, Repurposed and Creating Killer New Value Health Datapalooza, June 2, 2015
My talk in the technical meeting "Global Burden of Diseases and Scientific Computation in Health". 25-26 September 2015. FIOCRUZ, Rio de Janeiro, Brazil
The document summarizes ChathamHealthLink, a health information exchange program in Chatham County, Georgia. It was formed by the Chatham County Safety Net Planning Council in 2004 to improve access to and quality of healthcare for uninsured county residents. The program allows different healthcare providers using separate electronic medical record systems to securely share patient information through a central database. This reduces duplication of services, improves care coordination, and allows providers and the Council to track health outcomes and service trends across the safety net system. The goal is for ChathamHealthLink to eventually connect all area providers, hospitals, and behavioral health organizations using interoperable electronic records.
Decision Support System Enabled Data Warehouses for Improving the Analytic Ca...MEASURE Evaluation
“Decision Support Systems for Improving the Analytic Capacity of HIS in Developing Countries”
Mike Edwards (MEASURE Evaluation), Presenter. Co-author: Theo Lippeveld (MEASURE Evaluation)
Presentation given
Analysis of the Pressure Placed on Medical Systems during the COVID-19 PandemicTyler Wishnoff
See how big data is being used to analyze and track the rise of COVID-19, and learn how query acceleration through solutions like Kyligence can help the global community respond faster to the pandemic. Learn more here: https://kyligence.io/
This document discusses the use of visual analytics in healthcare. It provides three case studies of using visual analytics: 1) supporting chronic headache patients by providing interactive visualizations of daily activities and their impact on conditions, 2) maintaining sepsis data dashboards through automated processes to allow robust visualizations of sepsis processes and outcomes, and 3) creating an interactive analytic injury dashboard to empower stakeholders to synthesize information to strengthen child injury surveillance, prevention and research. Visual analytics transforms raw data into meaningful information by making data accessible and helping address information overload.
The document describes a visualization system for medical data that was designed to intuitively display examination results and enhance readability of data. It includes two parts: regional summary and individual visualization. The individual visualization part allows users to view: 1) an individual's health trend over years via line charts, 2) their overall health status using a "fingerprint" model, and 3) individual health summaries and disease connections via bar charts. The system was developed using Python, Django, D3.js, jQuery, and MySQL and utilizes geriatric medical and cause of death datasets.
sitNL 2015 Connecting the internet of things to predictive analysis (Flexso)Twan van den Broek
Find out how HANA Cloud Platform with all his features can help healthcare. For a Hackaton in february 2015, we have built a solution for helping epilepsy patients. We used the power of HCP by capturing heart rate data (IOT) and let the HANA predictive libraries (PAL) warn us for possible seizures (attacks).
This document discusses productivity tools in healthcare IT systems and their relationship to patient care. It begins by outlining the concept of using electronic medical record (EMR) and laboratory information management system (LIMS) data to develop more objective measures of clinical management. The present scenario section notes that EMR implementation can initially lower but later increase physician productivity. It also stresses the need to continually adapt processes. Several challenges of EMR are presented, including difficulties with longitudinal patient tracking across multiple providers and issues with system usability and financial impacts. The solution involves using healthcare IT systems to integrate and analyze longitudinal patient data from various sources to facilitate more objective clinical decision-making and monitoring of metrics like productivity and efficiency.
Can drug safety get good data while running highly automated processes today?MyMeds&Me
This document discusses challenges with drug safety data collection and proposes digital solutions to address them. It notes that health care data collection lacks standardization, resulting in significant data gaps. Digital reporting tools can help capture more complete, standardized safety reports directly from patients and providers. The Reportum platform is highlighted as a multi-lingual, multi-platform tool that facilitates real-time safety data collection and coding to regulatory standards, improving data quality for pharmacovigilance. Artificial intelligence is also discussed as a way to further structure data and enhance efficiency.
Big Data to Artificial Intelligence in Healthcarejetweedy
Big data in healthcare is studied because electronic health data sets are large, complex and growing. They contain 90% unstructured data that will increase 25 times over the next decade. Examples of artificial intelligence in healthcare include IBM Watson which provides evidence-based treatment options to oncologists, Medical Sieve which assists with clinical decision making in radiology and cardiology, and an app from AiCure supported by NIH that uses a smartphone's camera to confirm patients are adhering to their prescriptions. Deep Genomics also aims to identify patterns in genetic data to inform doctors about the effects of genetic variations at a cellular level. Overall, big data and AI can help make the right healthcare decisions for patients.
This document provides an overview and status update of the RPMS EHR (Resource and Patient Management System Electronic Health Record) system implemented across Indian Health Service facilities. It discusses the goals of adopting an EHR, the components and functionality of the RPMS EHR, implementation milestones achieved to date, lessons learned from early adopter sites, and how the EHR can help improve patient care, documentation, and metrics. Over 75 facilities are currently using the RPMS EHR, with a goal of all IHS federal sites implementing it by the end of 2008.
This document contains a resume for Kyle L. Miller which includes his contact information, certifications, work experience, education, and skills. Miller has over 10 years of experience as an EpicCare Ambulatory Consultant and Application Coordinator where he has implemented and supported EMR systems, created documentation templates, built registries and reports, and provided go-live support. He is proficient in various Epic applications and holds numerous Epic certifications that are current as of 2015. Miller also has a Bachelor's degree in French and a Master's degree in Liberal Studies.
The document describes three projects:
1. Sehaty, a secure online patient portal integrated with Cerner that allows patients to access their health information and schedule appointments from anywhere. The developer led a team of 15 over 1 year to build it.
2. A patient referral system connecting 40 hospitals to enhance efficiency by integrating internal systems and verifying patient data. It allows for paperless communication via SMS and fax.
3. An iPhone app for hospital staff providing timely information and connectivity from anywhere through features like viewing tasks, contacts, appointments and a date converter.
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
This document discusses medical data and its importance. It defines key terms like data, information and knowledge. It explains how medical data is collected and used by various stakeholders in healthcare. It also outlines the peculiarities of medical data and challenges with traditional record keeping. Finally, it discusses important data sources, users, and agencies involved in medical data in India.
Health Informatics Mobile Health, Telemedicine, and the Consumerjetweedy
Health informatics involves the use of information technology and systems to deliver healthcare. Mobile health or mHealth uses mobile devices to improve health outcomes through platforms like mobile apps and sensors. Telemedicine uses technology to provide remote healthcare services and overcome geographical barriers. Consumers are increasingly using mobile apps, fitness trackers, and online resources for health information. However, challenges include issues with costs, privacy, user-friendliness, and low health literacy.
The document discusses Healthbank, the world's first citizen-owned health data transaction platform established in Geneva, Switzerland in 2013. Healthbank connects data from all parts of the healthcare system and rewards participants for sharing their data. The summary also discusses how patients can register with Healthbank when recruited for a weight loss consumer healthcare product. It notes the added value of apps, wearables, and medical devices that collect health data and the potential insights and business value this data can provide for organizations.
The document discusses how AI and machine learning can help address challenges in healthcare by analyzing complex medical data. It provides examples of how AI can help with tasks like analyzing medical images to assist radiologists, predicting drug response from scans, and using electronic health records to better understand diseases and patient heterogeneity. The document also acknowledges challenges like the need for large labeled datasets and ensuring interpretability and avoidance of bias.
This document contains forms and instructions for conducting a point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals. The forms collect data at the hospital, ward, patient, and national/regional level. Hospital data includes bed numbers, staffing levels, infection control activities and organizational culture. Ward data includes bed numbers, hand hygiene infrastructure. Patient data collects infection details, antimicrobial use, and patient characteristics for those with infections or receiving antibiotics. National data provides healthcare system context. The forms standardize data collection to allow prevalence comparisons across settings.
The document discusses the global spread of the mcr-1 gene, which confers plasmid-mediated colistin resistance in Enterobacteriaceae. This poses a substantial public health risk as it limits treatment options for multidrug-resistant infections. Options for response include improved detection of mcr-1 via laboratory methods like PCR and whole genome sequencing, enhanced surveillance programs, infection control measures in healthcare settings, antimicrobial stewardship, and reducing colistin use in animals to prevent further spread. A One Health approach combining human and veterinary medicine is needed to monitor mcr-1 in food and the environment.
Talk about data visualization as tool to add new value to health data, presented in the Panel: Old School Data Set, Rebooted, Repurposed and Creating Killer New Value Health Datapalooza, June 2, 2015
My talk in the technical meeting "Global Burden of Diseases and Scientific Computation in Health". 25-26 September 2015. FIOCRUZ, Rio de Janeiro, Brazil
The document summarizes ChathamHealthLink, a health information exchange program in Chatham County, Georgia. It was formed by the Chatham County Safety Net Planning Council in 2004 to improve access to and quality of healthcare for uninsured county residents. The program allows different healthcare providers using separate electronic medical record systems to securely share patient information through a central database. This reduces duplication of services, improves care coordination, and allows providers and the Council to track health outcomes and service trends across the safety net system. The goal is for ChathamHealthLink to eventually connect all area providers, hospitals, and behavioral health organizations using interoperable electronic records.
Decision Support System Enabled Data Warehouses for Improving the Analytic Ca...MEASURE Evaluation
“Decision Support Systems for Improving the Analytic Capacity of HIS in Developing Countries”
Mike Edwards (MEASURE Evaluation), Presenter. Co-author: Theo Lippeveld (MEASURE Evaluation)
Presentation given
Analysis of the Pressure Placed on Medical Systems during the COVID-19 PandemicTyler Wishnoff
See how big data is being used to analyze and track the rise of COVID-19, and learn how query acceleration through solutions like Kyligence can help the global community respond faster to the pandemic. Learn more here: https://kyligence.io/
This document discusses the use of visual analytics in healthcare. It provides three case studies of using visual analytics: 1) supporting chronic headache patients by providing interactive visualizations of daily activities and their impact on conditions, 2) maintaining sepsis data dashboards through automated processes to allow robust visualizations of sepsis processes and outcomes, and 3) creating an interactive analytic injury dashboard to empower stakeholders to synthesize information to strengthen child injury surveillance, prevention and research. Visual analytics transforms raw data into meaningful information by making data accessible and helping address information overload.
The document describes a visualization system for medical data that was designed to intuitively display examination results and enhance readability of data. It includes two parts: regional summary and individual visualization. The individual visualization part allows users to view: 1) an individual's health trend over years via line charts, 2) their overall health status using a "fingerprint" model, and 3) individual health summaries and disease connections via bar charts. The system was developed using Python, Django, D3.js, jQuery, and MySQL and utilizes geriatric medical and cause of death datasets.
sitNL 2015 Connecting the internet of things to predictive analysis (Flexso)Twan van den Broek
Find out how HANA Cloud Platform with all his features can help healthcare. For a Hackaton in february 2015, we have built a solution for helping epilepsy patients. We used the power of HCP by capturing heart rate data (IOT) and let the HANA predictive libraries (PAL) warn us for possible seizures (attacks).
This document discusses productivity tools in healthcare IT systems and their relationship to patient care. It begins by outlining the concept of using electronic medical record (EMR) and laboratory information management system (LIMS) data to develop more objective measures of clinical management. The present scenario section notes that EMR implementation can initially lower but later increase physician productivity. It also stresses the need to continually adapt processes. Several challenges of EMR are presented, including difficulties with longitudinal patient tracking across multiple providers and issues with system usability and financial impacts. The solution involves using healthcare IT systems to integrate and analyze longitudinal patient data from various sources to facilitate more objective clinical decision-making and monitoring of metrics like productivity and efficiency.
Can drug safety get good data while running highly automated processes today?MyMeds&Me
This document discusses challenges with drug safety data collection and proposes digital solutions to address them. It notes that health care data collection lacks standardization, resulting in significant data gaps. Digital reporting tools can help capture more complete, standardized safety reports directly from patients and providers. The Reportum platform is highlighted as a multi-lingual, multi-platform tool that facilitates real-time safety data collection and coding to regulatory standards, improving data quality for pharmacovigilance. Artificial intelligence is also discussed as a way to further structure data and enhance efficiency.
Big Data to Artificial Intelligence in Healthcarejetweedy
Big data in healthcare is studied because electronic health data sets are large, complex and growing. They contain 90% unstructured data that will increase 25 times over the next decade. Examples of artificial intelligence in healthcare include IBM Watson which provides evidence-based treatment options to oncologists, Medical Sieve which assists with clinical decision making in radiology and cardiology, and an app from AiCure supported by NIH that uses a smartphone's camera to confirm patients are adhering to their prescriptions. Deep Genomics also aims to identify patterns in genetic data to inform doctors about the effects of genetic variations at a cellular level. Overall, big data and AI can help make the right healthcare decisions for patients.
This document provides an overview and status update of the RPMS EHR (Resource and Patient Management System Electronic Health Record) system implemented across Indian Health Service facilities. It discusses the goals of adopting an EHR, the components and functionality of the RPMS EHR, implementation milestones achieved to date, lessons learned from early adopter sites, and how the EHR can help improve patient care, documentation, and metrics. Over 75 facilities are currently using the RPMS EHR, with a goal of all IHS federal sites implementing it by the end of 2008.
This document contains a resume for Kyle L. Miller which includes his contact information, certifications, work experience, education, and skills. Miller has over 10 years of experience as an EpicCare Ambulatory Consultant and Application Coordinator where he has implemented and supported EMR systems, created documentation templates, built registries and reports, and provided go-live support. He is proficient in various Epic applications and holds numerous Epic certifications that are current as of 2015. Miller also has a Bachelor's degree in French and a Master's degree in Liberal Studies.
The document describes three projects:
1. Sehaty, a secure online patient portal integrated with Cerner that allows patients to access their health information and schedule appointments from anywhere. The developer led a team of 15 over 1 year to build it.
2. A patient referral system connecting 40 hospitals to enhance efficiency by integrating internal systems and verifying patient data. It allows for paperless communication via SMS and fax.
3. An iPhone app for hospital staff providing timely information and connectivity from anywhere through features like viewing tasks, contacts, appointments and a date converter.
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
This document discusses medical data and its importance. It defines key terms like data, information and knowledge. It explains how medical data is collected and used by various stakeholders in healthcare. It also outlines the peculiarities of medical data and challenges with traditional record keeping. Finally, it discusses important data sources, users, and agencies involved in medical data in India.
Health Informatics Mobile Health, Telemedicine, and the Consumerjetweedy
Health informatics involves the use of information technology and systems to deliver healthcare. Mobile health or mHealth uses mobile devices to improve health outcomes through platforms like mobile apps and sensors. Telemedicine uses technology to provide remote healthcare services and overcome geographical barriers. Consumers are increasingly using mobile apps, fitness trackers, and online resources for health information. However, challenges include issues with costs, privacy, user-friendliness, and low health literacy.
The document discusses Healthbank, the world's first citizen-owned health data transaction platform established in Geneva, Switzerland in 2013. Healthbank connects data from all parts of the healthcare system and rewards participants for sharing their data. The summary also discusses how patients can register with Healthbank when recruited for a weight loss consumer healthcare product. It notes the added value of apps, wearables, and medical devices that collect health data and the potential insights and business value this data can provide for organizations.
The document discusses how AI and machine learning can help address challenges in healthcare by analyzing complex medical data. It provides examples of how AI can help with tasks like analyzing medical images to assist radiologists, predicting drug response from scans, and using electronic health records to better understand diseases and patient heterogeneity. The document also acknowledges challenges like the need for large labeled datasets and ensuring interpretability and avoidance of bias.
This document contains forms and instructions for conducting a point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals. The forms collect data at the hospital, ward, patient, and national/regional level. Hospital data includes bed numbers, staffing levels, infection control activities and organizational culture. Ward data includes bed numbers, hand hygiene infrastructure. Patient data collects infection details, antimicrobial use, and patient characteristics for those with infections or receiving antibiotics. National data provides healthcare system context. The forms standardize data collection to allow prevalence comparisons across settings.
The document discusses the global spread of the mcr-1 gene, which confers plasmid-mediated colistin resistance in Enterobacteriaceae. This poses a substantial public health risk as it limits treatment options for multidrug-resistant infections. Options for response include improved detection of mcr-1 via laboratory methods like PCR and whole genome sequencing, enhanced surveillance programs, infection control measures in healthcare settings, antimicrobial stewardship, and reducing colistin use in animals to prevent further spread. A One Health approach combining human and veterinary medicine is needed to monitor mcr-1 in food and the environment.
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
This document summarizes discussions from the 15th National Microbiology Focal Points meeting regarding whole genome sequencing (WGS) for public health surveillance in Europe. It outlines ECDC's strategy and roadmap to integrate WGS and molecular typing into EU-level surveillance from 2012-2019. Key priorities for 2016-2018 include using WGS for cross-border foodborne outbreak investigations and continuous surveillance of Listeria monocytogenes and Neisseria meningitidis. Barriers like technical capacity and cost will need to be addressed before expanding WGS-based surveillance to other priority pathogens.
Carbapenem-resistant Acinetobacter baumannii poses a significant threat in healthcare settings across Europe. It can cause serious infections that are difficult to treat due to limited antibiotic options. The number of countries reporting spread and endemicity of carbapenem-resistant A. baumannii has increased in recent years. Increased detection and control efforts are needed to prevent it from becoming endemic in more European regions and healthcare facilities.
APPLICATION OF COMPUTERS IN EPIDEMIOLOGY AND PUBLIC HEALTH - ANJALI MAM.pptxAnjali Singh
This lecture describes the uses of Computers in Epidemiology and Health. The topic has been made considering the basics for the undergraduate, and third-year students.
This document outlines the use of computers in epidemiology and public health. It discusses:
1. Health management information systems which collect and analyze demographic and health data.
2. Developing databases for epidemiological research using tools like Excel, Epi Info and Access.
3. Using statistical software like Epi Info, SPSS and STATA to analyze epidemiological data.
4. Disease surveillance systems using computers to rapidly transmit and monitor health data.
5. Challenges implementing computer systems including structural issues, procedural problems, and constraints in India like fragmented data and lack of infrastructure.
IQChart is a patient management database that collects clinical data from HIV/AIDS patients to generate accurate monthly and quarterly reports for monitoring and evaluation. It was developed by AIDS Relief and ICAP to computerize paper-based patient registers and improve data analysis and clinical decision making. The tool is freely available, open source software that is used in over 90 treatment facilities in Rwanda to track over 54,000 patients. Future plans include integrating geographic information system mapping capabilities to help identify underserved areas and monitor program outcomes.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
GLI TB Diagnostics Connectivity Guide 2016SystemOne
This document provides an overview of diagnostics connectivity solutions for tuberculosis (TB) programs. It discusses how connectivity solutions can enable remote monitoring of diagnostic devices, automatically send test results to clinicians and health information systems, facilitate inventory management, and enhance disease surveillance and program monitoring. The document also covers the necessary software, hardware, internet connectivity, data security, personnel needs, and budgeting considerations for implementing diagnostics connectivity solutions. Overall, the document presents connectivity solutions as a way for TB programs to improve patient care and management while strengthening laboratory systems.
PanCareSurPass @SIOP Europe/CCI Europe Meeting 2021, Riccardo HauptKylieOBrien10
Research Manager Dr. Riccardo Haupt presented the PanCareSurPass project at the ‘PanCare and ELTEC – Late effects’ session of the SIOP Europe/CCI Europe Meeting 2021 on 28th April 2021.
PanCareSurPass has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 89999. The material presented and views expressed here are the responsibilities of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out.
This document discusses disease registries and the benefits of centralized data. It explains that disease registries collect uniform clinical and research data from multiple sources to study outcomes for populations with specific diseases or exposures. Centralizing registry data provides several advantages, including easier data entry and analysis across locations, more robust research on risk factors and disease patterns, and quicker decision making for health managers and researchers. The document advocates for web-based registry software to facilitate anytime access to real-time centralized data without geographical boundaries, allowing greater data sharing and collaborative research efforts.
"Hello, I'm Claude. I'm an AI assistant created by Anthropic to be helpful, harmless, and honest. How can I assist you today?"
Patient: "I'm not feeling well. Can you check my temperature?"
Topic:
Effective Visualizations that will aid in minimizing the spread of infectious diseases
Group members:
Lamar Munoz, Michael Brockenbrough, Neisha Sadhnani
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
This document discusses developing and strengthening monitoring and evaluation (M&E) systems for national tuberculosis (TB) programs. It identifies key elements of an effective M&E system and outlines five steps to strengthen implementation: 1) assessing current M&E practices, 2) developing an M&E plan, 3) establishing an M&E unit, 4) implementing the M&E plan, and 5) managing quality control. The document provides guidance on conducting a situation analysis, developing indicators and data collection methods, building M&E capacity, and ensuring quality monitoring and use of data.
Improved_Smartcare-ART System Presentation_V6.pptxBetsegaw1
The document provides an overview and outline of a presentation on training for an updated EMR-ART V6.0 software system. Key points include:
- The rationale for improving the existing EMR-ART software to address user complaints, incorporate new HIV/AIDS program features and indicators, and establish data exchange with other systems.
- An overview of the new features in EMR-ART V6.0 including dashboards, patient management, treatment and follow up, viral load tracking, reporting, and data quality assurance.
- A demonstration of the system's capabilities like differentiated service delivery models, appointment scheduling, tracing lost patients, and generating reports for indicators.
The presentation aims to describe the updated
Quality tools (2), Ola Elgaddar, 30 09 - 2013Ola Elgaddar
I) Management and quality tools include planning tools like Hoshin Planning and Gantt charts, as well as team tools like brainstorming and affinity diagrams.
II) Data collection tools mentioned include indicators and check sheets. Indicators are quantitative measures that can help provide data on quality when analyzed. Check sheets are structured forms for collecting and observing data repeatedly.
III) Data analysis tools include control charts to study process changes over time, histograms to show data distributions, and scatter diagrams to show correlations between paired data. Tools for root cause analysis include flow charts, cause-effect diagrams, and Pareto charts.
This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
Sepsis Prediction Using Machine LearningIRJET Journal
This document summarizes a research paper that used machine learning algorithms to predict sepsis in ICU patients using vital sign and laboratory data. The researchers:
1) Collected data from 36,000 patients including vital signs, lab values, and demographics as features for an MLP classifier model.
2) The top important features for prediction were temperature, oxygen saturation, respiratory rate, and heart rate.
3) The MLP classifier model achieved a log loss of 0.15 and was able to accurately predict sepsis risk from patient data on admission to the ICU.
Early prediction of sepsis using machine learning approaches can help clinicians initiate early treatment and reduce mortality and healthcare costs.
Despite massive investment in both people and technology, health systems are still struggling to maximize the value of their greatest asset: their data. Delivering high-quality, valuable insight from data and pushing those insights to the frontline healthcare professionals remains challenging and expensive. According to a recent survey conducted by HealthLeaders Media, health systems are hiring more analytics staff than almost any other role in health care. We know there’s an alternative to the massive hiring of analytics staff, a better way to dramatically increase the efficiency of your existing resources and provide an ROI that grows over time. The better way is the Rapid Response Analytics Solution.
Rapid Response Analytics Solution (RRA Solution) consists of two elements: curated, modular data called DOS™ Marts and Population Builder, a powerful self-service tool that lets any type of user, from physician executive to frontline nurses and population health teams explore their data and quickly build and share populations without needing to know how to write SQL and data science code. RRA Solution increases an analytics team’s productivity by up to 10x and reduces its time to develop analytics by as much as 90 percent. Analysts can spend more time focusing on key strategic analysis and less time on repetitive tasks that can lead to inconsistent results and a backlog of requests.
Learning Objectives:
- Discover how RRA Solution allows you to take components and customize them to quickly tailor and deliver meaningful insights.
- Learn about DOS™ Marts and Population Builder and how they drive consistency and efficiency, without needing to know SQL and data science coding.
- Understand how to use RRA Solution to increase the value of your analytics team and get them operating at the top of their function.
View this webinar and learn how RRA Solution can help you achieve a 10x increase in productivity and reduce your time to develop new analytics reports by more than 90 percent.
The Integrated Disease Surveillance Project (IDSP) aims to establish a decentralized disease surveillance system in India to improve disease control. It integrates existing surveillance programs, coordinates surveillance activities, and establishes quality data collection, analysis, and feedback using information technology. The IDSP covers diseases like malaria, acute diarrheal diseases, tuberculosis, and measles. It is implemented in phases across states and union territories of India and involves strengthening laboratories, training health professionals, and creating an IT network to link surveillance sites. The goal is to provide data to enable efficient public health decision making and interventions for priority diseases.
This document provides guidance on collecting and reporting on anthropometric indicators and annual monitoring indicators for Title II maternal and child health and nutrition programs. It discusses the key impact indicators of reduced stunting and underweight in children, as well as recommended annual monitoring indicators of growth promotion program participation and weight gain. The document provides information on anthropometric data collection, equipment, measurements, analysis, and comparison to standards. It aims to support consistent monitoring and evaluation of child nutrition activities.
Public health surveillance involves the ongoing collection and analysis of health data to support public health programs and policies. It is used to monitor disease outbreaks and other health issues. India has implemented an Integrated Health Information Platform (IHIP) to create a single system for collecting and analyzing real-time surveillance data from across the country. IHIP aims to improve disease monitoring and response by integrating data on over 33 health conditions from various programs into one electronic platform. It allows identification of outbreaks and resource allocation through features like automated epidemic curve analysis and geospatial mapping of disease clusters. While IHIP has integrated some vertical programs, full integration remains a work in progress. Limitations also include challenges in implementation, private sector involvement, and
Monitoring and evaluation toolkit - Conférence de la 2e édition du Cours international « Atelier Paludisme » - TUSEO Luciano - World Health Organization / Roll Back Malaria - maloms@iris.mg
Similar to Atlas for infectious diseases. Bruno Cianco (ECDC) (20)
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Dag Harmsen presented on the evolvement and challenges of cgMLST for the harmonization of bacterial genome sequencing and analysis. Key points include:
- cgMLST (core genome multilocus sequence typing) involves identifying and comparing alleles across a fixed set of core genome genes and has been applied to outbreak investigation and global pathogen nomenclature.
- Tools for cgMLST analysis have been developed and improved to work on read, draft, and complete genome levels and allow scalable, additive analysis of single genes to whole genomes.
- Standardizing a hierarchical cgMLST-based approach and developing common nomenclature poses challenges but is important for microbial genotypic surveillance across laboratories and countries.
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
This document summarizes discussions from several sessions of a meeting on antimicrobial resistance and healthcare-associated infections. Key points include:
- Most countries submit antimicrobial consumption data close to the deadline, and there are specific rules for who can access and publish the data.
- It is important but challenging to compare hospital antimicrobial consumption data between countries due to differences in how data is collected. Both defined daily doses and packages are needed for comparison.
- A pilot hospital-based antimicrobial consumption survey was proposed to collect additional data starting in late 2015, but the protocol requires further review and clarification before implementation.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Validation studies are essential to accurately assess the sensitivity, specificity, and predictive values of point prevalence surveys (PPS) of healthcare-associated infections (HAI). Previous validation studies of PPS have shown varied results, underscoring the need for formal evaluations. Without validation, true HAI prevalence is unknown and differences between locations cannot be properly investigated. International organizations can help support national validation efforts to improve HAI surveillance.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
More from European Centre for Disease Prevention and Control (ECDC) (20)
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
One health condition that is becoming more common day by day is diabetes.
According to research conducted by the National Family Health Survey of India, diabetic cases show a projection which might increase to 10.4% by 2030.
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
Medical Quiz ( Online Quiz for API Meet 2024 ).pdf
Atlas for infectious diseases. Bruno Cianco (ECDC)
1. ECDC’s Surveillance Atlas of Infectious Diseases
Dr Bruno C. Ciancio, on behalf of the surveillance Atlas team
2. The Surveillance Atlas of Infectious Diseases
http://www.ecdc.europa.eu/en/data-tools/atlas/Pages/atlas.aspx
3. Surveillance Atlas team
• Frantiska Hruba: overall coordination, definition of content, statistical methods;
• Ivo Van Walle: overall project management, algorithms for data analysis, interface with ICT;
• Jerome Milliere: project management of ICT development;
• Catalin Albu: data process and workbench;
• Silviu Lucian Ionescu: business analysis, GIS solutions;
• Gaetan Guyodo: coordination of data management and storage;
• Phillip Zucs: Definition of epidemiological indicators, input on concepts, design and functionalities of tool;
• Surveillance disease experts: definition of content, revision of data and outputs;
• Data managers: preparation of data;
• GIS team: mapping and overall GIS support;
• Heads of DP programme: revision of disease content and dashboard tool;
• Denis Coulombier and Bruno Ciancio: conception of the idea and high level coordination;
4. From Data to Atlas
TESSy
Eurostat
WHO CISID
Workbench Repository Atlas
Raw data Clean data Atlas data
5. Atlas Disease content structure
• Populations/subpopulations:
– All cases, Confirmed cases, Age less than 1, VTEC HUS cases
• Indicators:
– Number of cases, NR, CFR, % pulmonary TB cases, Serotype-specific NR
• Distributions:
– Age groups, Gender, Serotype, Vaccination status
• Time resolution:
– Year, Quarter, Month, Week
• Geographical resolution:
– Country (NUTS 0 or GAUL 0), NUTS 1-3 or GAUL 1-2
6. Atlas Disease project flow chart
Disease to be
included in Atlas
Specifications of disease indicators, variables and
cleaning rules for Atlas
Tracing and correction of issues
Disease Experts
& EPM
Workbench – Preparation of clean datasets.
Data managers
Calculation and Migration process – Internal Atlas
BGMSS
BGMSS
Approval by Disease
Experts
Preparation of the
Disease text
Review and validation
Define the issue If No If Yes Calculation and Migration process – Nominated AtlasBGMSSDisease Experts
Approval by
Member States/Network members
Review and validation
If No If Yes
Disease Experts
& Member states/
Network members
Calculation and Migration process – Public Atlas
BGMSS &DE
Disease indicators displayed in
Public Atlas
Internal ECDC approval for
publication @ ECDC portal
If YesIf No
Disease experts & EPM
& HoDP, HoU, CS
7. Plan for including diseases in the Atlas in
2015
11/2014 12/2014 01/2015 02/2015 03/2015 04/2015 05/2015 06/2015 07/2015 08/2015 09/2015 10/2015 11/2015 12/2015
Influenza
FLU FLU FLU FLU FLU FLU FLU FLU FLU FLU FLU FLU FLU FLU
VPD
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
Measles
Rubella
IBD 2013 IBD 2014
VPD
2013
VPD
2013
VPD
2013
VPD
2014
VPD
2014
EVD
WNF WNF WNF WNF WNF WNF WNF
Other
EVD
Other
EVD
Other
EVD
Other
EVD 14
FWD
SALM
VTEC
SALM
VTEC Q3
SALM
VTEC Q4
SALM
VTEC Q1
SALM
VTEC Q2
SALM
VTEC Q3
FWD FWD FWD FWD 14
LEGI LEGI 14
Tuberculosis
TB 2013 TB 2014
HSH
GONO
AMR?
GONO
AMR?
HIV AIDS HIV AIDS HIV AIDS
14
HEP B C HEP B C HEP B&C
14
STI STI STI 14
ARHAI
To be defined for implementation in 2016
RED first time data cleaning and indicators calculation
GREEN routine data cleaning and indicators calculation