Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

234
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
234
Manipulation and Analysis01:21

Manipulation and Analysis

265
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
265
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

853
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
853
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

454
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
454
Introduction to GIS01:28

Introduction to GIS

456
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
456
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.3K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Association between household food insecurity and quality of life: a longitudinal study in Northeast Brazil, 2014-2019.

BMC public health·2026
Same author

Reviewed article: Bando DH, Neto FC, Queiroz AP. Stroke mortality negatively associated with health, education, and safety: an ecological study, Minas Gerais, 2014-2022. Epidemiol Serv Saude. 2025:34;e20240820.

Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil·2025
Same author

Acoustic-prosodic measures discriminate the emotions of Brazilian portuguese speakers.

CoDAS·2025
Same author

Food insecurity and water insecurity measurement in Brazil: Sustainable Development Goals monitoring through experiential scales.

Cadernos de saude publica·2025
Same author

Social network for guardians of transgender children and adolescents.

Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil·2025
Same author

Costs of obesity attributable to the consumption of sugar-sweetened beverages in Brazil.

Scientific reports·2024

Related Experiment Video

Updated: Jan 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

A new combination rule for Spatial Decision Support Systems for epidemiology.

Luciana Moura Mendes de Lima1, Laísa Ribeiro de Sá2, Ana Flávia Uzeda Dos Santos Macambira3

  • 1Graduate Program in Decision Models and Health, Department of Statistics, Federal University of Paraíba, João Pessoa, Paraíba, Brazil. lumouramendes@gmail.com.

International Journal of Health Geographics
|November 11, 2019
PubMed
Summary

This study introduces a new approach using Spatial Decision Support Systems (SDSS) and Multiple Criteria Decision Making (MCDM) to map disease priorities. The method helps public health managers prioritize actions for fighting diseases effectively.

Keywords:
BrazilEpidemiologyMultiple Criteria Decision MakingSpace–time analysisSpatial Decision Support SystemsSpatial analysis

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.7K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.0K

Related Experiment Videos

Last Updated: Jan 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.7K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.0K

Area of Science:

  • Public Health
  • Geographic Information Systems (GIS)
  • Epidemiology

Background:

  • Health decision-making is complex, involving multiple factors, data, and spatial considerations.
  • Existing methodologies often lack a specific focus on epidemiological purposes.
  • Spatial Decision Support Systems (SDSS) are a limited but relevant group of tools for spatial health analysis.

Purpose of the Study:

  • To develop and present a novel approach for health decision-making using integrated SDSS and Multiple Criteria Decision Making (MCDM).
  • To create a georeferenced map indicating priority levels for disease control efforts.
  • To aid public health managers in planning and directing health actions.

Main Methods:

  • Utilizes a set of SDSS, each analyzing specific problem aspects with spatial and non-spatial data.
  • Employs fuzzy rule-based systems to group results from individual SDSS analyses.
  • Combines initial evaluations using weighted linear combination (WLC) to generate a final decision map.

Main Results:

  • Demonstrates the approach with real tuberculosis epidemiological data from a Brazilian municipality.
  • Produces a final map categorizing areas into four priority levels for disease control: non-priority, non-priority tendency, priority tendency, and priority.
  • The georeferenced map visually represents disease priority levels.

Conclusions:

  • The developed approach offers a valuable tool for public health managers.
  • It supports the planning, direction, and prioritization of health actions and public services.
  • The method enhances the strategic organization of disease control initiatives.