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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

264
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:
264
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

151
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
151
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

280
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
280

You might also read

Related Articles

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

Sort by
Same author

A new Twitter based credit rating model methodology.

Annals of operations research·2026
Same author

JointLIME: An interpretation method for machine learning survival models with endogenous time-varying covariates in credit scoring.

Risk analysis : an official publication of the Society for Risk Analysis·2024
Same author

Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey.

The journal of real estate finance and economics·2024
Same author

On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19.

European journal of operational research·2022
Same author

"Birds of a Feather" Fail Together: Exploring the Nature of Dependency in SME Defaults.

Risk analysis : an official publication of the Society for Risk Analysis·2017

Related Experiment Video

Updated: Oct 27, 2025

High-throughput Identification of Bacteria Repellent Polymers for Medical Devices
10:43

High-throughput Identification of Bacteria Repellent Polymers for Medical Devices

Published on: November 5, 2016

9.2K

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach.

Antonia Gieschen1, Jake Ansell1, Raffaella Calabrese1

  • 1University of Edinburgh Business School, 29 Buccleuch Place, Edinburgh, EH8 9JS, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|July 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spatiotemporal clustering method to analyze antimicrobial prescribing patterns in Scotland. The approach helps identify similar prescriber groups and unusual prescribing behaviors, aiding in responsible antimicrobial use.

Keywords:
Cluster analysishealth policyspatial riskspatiotemporal analysis

More Related Videos

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.7K
Spatial Quantification of Drugs in Pulmonary Tuberculosis Lesions by Laser Capture Microdissection Liquid Chromatography Mass Spectrometry LCM-LC/MS
09:58

Spatial Quantification of Drugs in Pulmonary Tuberculosis Lesions by Laser Capture Microdissection Liquid Chromatography Mass Spectrometry LCM-LC/MS

Published on: April 18, 2018

9.3K

Related Experiment Videos

Last Updated: Oct 27, 2025

High-throughput Identification of Bacteria Repellent Polymers for Medical Devices
10:43

High-throughput Identification of Bacteria Repellent Polymers for Medical Devices

Published on: November 5, 2016

9.2K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.7K
Spatial Quantification of Drugs in Pulmonary Tuberculosis Lesions by Laser Capture Microdissection Liquid Chromatography Mass Spectrometry LCM-LC/MS
09:58

Spatial Quantification of Drugs in Pulmonary Tuberculosis Lesions by Laser Capture Microdissection Liquid Chromatography Mass Spectrometry LCM-LC/MS

Published on: April 18, 2018

9.3K

Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • The British government recognized the need to reduce antimicrobial prescriptions in 2016.
  • Antimicrobial prescription patterns are influenced by spatiotemporal factors like bacterial outbreaks and population density.
  • Analyzing these patterns is crucial for combating antimicrobial resistance.

Purpose of the Study:

  • To develop a spatiotemporal clustering approach for modeling antimicrobial prescription behavior in Scotland.
  • To adapt density-based spatial clustering of applications with noise (DBSCAN) for diverse population densities.
  • To identify peer groups of general practitioners (GPs) with similar prescribing habits and detect outliers.

Main Methods:

  • Utilized a modified DBSCAN algorithm incorporating both spatial and temporal data.
  • Employed dynamic time warping for temporal analysis to account for seasonality.
  • Introduced a novel spatial weighting method using Kernel density estimation to handle varying local densities.

Main Results:

  • Successfully applied the spatiotemporal clustering approach to antibiotic prescription data in Scotland.
  • Demonstrated the ability to group GPs with similar prescribing behaviors.
  • Showcased the identification of regions with extreme prescribing patterns.

Conclusions:

  • The proposed method effectively models antimicrobial prescribing behavior in diverse geographical settings.
  • This approach facilitates comparative analysis of prescribing habits among peer groups.
  • It aids in pinpointing areas requiring targeted interventions to optimize antimicrobial stewardship.