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Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Immune Surveillance by NK Cells and Phagocytes01:25

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Immune surveillance is an integral part of the innate immune system, involving the continuous monitoring of peripheral tissues to detect and respond to pathogens, infected cells, or cancerous cells. This surveillance is conducted primarily by natural killer (NK) cells and phagocytes, which employ distinct but complementary mechanisms to identify and eliminate threats.
Natural Killer Cells: The Fast Responders
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Related Experiment Video

Updated: Feb 11, 2026

Building a Better Mosquito: Identifying the Genes Enabling Malaria and Dengue Fever Resistance in A. gambiae and A. aegypti Mosquitoes
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Exploring Semantic Data Federation to Enable Malaria Surveillance Queries.

Jon Haël Brenas1, Mohammad Sadnan Al Manir2, Kate Zinszer3

  • 1The University of Tennessee Health Science Center (UTHSC)- Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, Memphis, TN, USA.

Studies in Health Technology and Informatics
|April 22, 2018
PubMed
Summary

Malaria surveillance experts can now query global data without coding skills. Semantic Automated Discovery and Integration (SADI) Web services federate diverse data sources, simplifying complex information access for effective interventions.

Keywords:
Distributed DataInteroperabilityMalaria AnalyticsMalaria SurveillanceWeb Services

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Related Experiment Videos

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Preventing the Spread of Malaria and Dengue Fever Using Genetically Modified Mosquitoes
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Area of Science:

  • * Infectious disease epidemiology
  • * Bioinformatics
  • * Health informatics

Background:

  • * Malaria remains a significant global health challenge, particularly in tropical regions.
  • * Effective interventions require integrating distributed data, posing challenges for non-technical experts.
  • * Existing data access methods demand advanced coding skills, limiting surveillance capabilities.

Purpose of the Study:

  • * To present a novel approach for federating and querying global malaria data.
  • * To empower surveillance experts by removing the need for advanced programming skills.
  • * To demonstrate the utility of Semantic Automated Discovery and Integration (SADI) Web services in malaria surveillance.

Main Methods:

  • * Deployment of over 10 Semantic Automated Discovery and Integration (SADI) Web services.
  • * Development of a system for federating disparate malaria data repositories.
  • * Implementation of a query system designed for non-technical users.

Main Results:

  • * Successful creation of a federated data system for malaria surveillance.
  • * Demonstrated ability to answer complex queries integrating data from multiple sources.
  • * Enhanced accessibility of global malaria data for surveillance experts.

Conclusions:

  • * SADI Web services provide a viable solution for complex, distributed data querying in public health.
  • * The developed system significantly lowers the technical barrier for malaria surveillance experts.
  • * This approach facilitates more efficient and informed decision-making for malaria control efforts.