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

492
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:
492
Pareto Chart00:52

Pareto Chart

7.6K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.6K
Interpreting Run Charts01:25

Interpreting Run Charts

3.0K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
3.0K
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

466
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...
466

You might also read

Related Articles

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

Sort by
Same author

Infectious disease outbreak controllability: biological, social and public health factors.

Proceedings. Biological sciences·2026
Same author

Introducing a framework for within-host dynamics and mutations modelling of H5N1 influenza infection in humans.

Journal of the Royal Society, Interface·2025
Same author

Modelling timelines to elimination of sleeping sickness in the Democratic Republic of Congo, accounting for possible cryptic human and animal transmission.

Parasites & vectors·2024
Same author

The Hidden Hand of Asymptomatic Infection Hinders Control of Neglected Tropical Diseases: A Modeling Analysis.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2024
Same author

Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC.

PLoS computational biology·2024
Same author

Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis.

Journal of the Royal Society, Interface·2023
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.8K

Identifying COVID-19 peaks using early warning signals.

Joshua Looker1, Kat S Rock2, Louise Dyson2,3

  • 1EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, United Kingdom.

Plos Computational Biology
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Early warning signals (EWS) can predict critical transitions in infectious disease dynamics, like COVID-19 outbreaks. This study shows EWS analysis of case and hospitalization data improves epidemic forecasting for better public health responses.

More Related Videos

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.3K
High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.7K

Related Experiment Videos

Last Updated: Jan 17, 2026

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.8K
Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.3K
High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.7K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • The COVID-19 pandemic highlighted the need for effective infectious disease modeling.
  • Early Warning Signals (EWS) theory offers a framework to predict critical transitions in complex systems, including disease dynamics.
  • Predicting epidemic peaks and troughs is crucial for timely public health interventions.

Purpose of the Study:

  • To analyze the theoretical and data-driven suitability of EWS for predicting epidemic transitions in infectious disease data.
  • To evaluate the performance of various temporal and spatial EWS statistics using COVID-19 case data.
  • To investigate the utility of EWS analysis on hospitalization data for anticipating case surges.

Main Methods:

  • Derivation of analytical statistics for infectious disease models.
  • Stochastic simulations to assess EWS applicability across different modeling scenarios.
  • Application of temporal and spatial EWS statistics to United Kingdom COVID-19 case data.
  • Analysis of hospitalization data for predicting corresponding case data transitions.

Main Results:

  • EWS analysis demonstrates applicability in predicting epidemic transitions within infectious disease models.
  • Temporal and spatial EWS statistics effectively anticipated transitions in UK COVID-19 case data.
  • Hospitalization data analysis using EWS showed potential for forecasting case data trends.

Conclusions:

  • EWS analysis is a valuable tool for predicting critical transitions in infectious disease dynamics.
  • Integrating EWS into modeling can enhance the accuracy of epidemic forecasting.
  • EWS analysis of real-world infection and hospitalization data can significantly improve pandemic preparedness and response strategies.