Jove
Visualize
Contact Us

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

875
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:
875
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

437
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
437
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.6K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.6K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
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...
1.4K
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

691
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
691
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

440
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...
440

You might also read

Related Articles

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

Sort by
Same author

Knowledge, attitudes and practices regarding bovine tuberculosis in cattle and humans in Malawi.

PloS one·2026
Same author

Derivation of a multivariate longitudinal causal effects model.

Journal of applied statistics·2025
Same author

Prevalence of Brucellosis in cattle, sheep, and goats in rural pastoral settings in Northern Tanzania.

One health (Amsterdam, Netherlands)·2025
Same author

Challenges of Controlling Foot-and-Mouth Disease in Pastoral Settings in Africa.

Transboundary and emerging diseases·2025
Same author

Meta-analysis of the prevalence of tuberculosis in cattle and zoonotic tuberculosis in humans in sub-Saharan Africa.

One health outlook·2025
Same author

Farm costs and benefits of antimicrobial use reduction on broiler farms in Dar es Salaam, Tanzania.

Frontiers in antibiotics·2025
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles
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 Experiment Video

Updated: Jan 9, 2026

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

14.1K

JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists.

Alfred Ngwira1,2,3, Samuel O M Manda4, Esron D Karimuribo1,3

  • 1Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, Morogoro, Tanzania.

Plos One
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces JSTMapp, a user-friendly application for disease risk mapping using Bayesian methods. It aids public health officials in designing effective disease control programs by visualizing spatial and temporal risk factors.

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

13.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K

Related Experiment Videos

Last Updated: Jan 9, 2026

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

14.1K
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

13.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K

Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Public Health

Background:

  • Disease mapping models are crucial for public health policy and disease control programs.
  • Current models often use frequentist or Bayesian methods via software like SaTScan, WinBUGS, and INLA.
  • There is a need for user-friendly applications for joint disease spatiotemporal modeling and mapping.

Purpose of the Study:

  • To develop a user-friendly joint disease spatiotemporal modelling and mapping application (JSTMapp) for epidemiologists and health statistics analysts.
  • To base the application on Bayesian methods for enhanced disease risk assessment.
  • To facilitate the estimation, mapping, and visualization of disease-specific and general spatial and temporal risk factors.

Main Methods:

  • Developed JSTMapp using the R package Shiny.
  • Integrated proven joint spatial modeling technology from the Bayesian statistical software INLA.
  • Utilized cattle bovine tuberculosis (BTB) and human extrapulmonary tuberculosis (EPTB) data in Africa for illustration.

Main Results:

  • The JSTMapp application enables comprehensive disease risk assessment, including mapping and visualization of spatial and temporal risk factors.
  • The application can evaluate spatial, temporal, and spatiotemporal correlations.
  • Exploratory analyses, such as mapping the standardized disease incidence ratio, are supported.
  • Improved performance was observed when JSTMapp was launched from GitHub R compared to the Shiny server.

Conclusions:

  • JSTMapp provides a valuable tool for epidemiologists and health statistics analysts for disease spatiotemporal modeling and mapping.
  • The application enhances the ability to understand and manage disease risks through advanced visualization and analysis.
  • Further optimization of online server performance is suggested for broader accessibility and usability.