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

472
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:
472
Infection01:20

Infection

11.6K
When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...
11.6K
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

444
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...
444
Gene Flow02:39

Gene Flow

37.3K
Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
37.3K
Viral Recombination00:57

Viral Recombination

24.9K
Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
24.9K

You might also read

Related Articles

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

Sort by
Same author

Incubation Period of Pertussis During a School-based Outbreak, South Korea, 2024.

The Journal of infectious diseases·2026
Same author

Simulating population compliance with pandemic interventions using large language models.

medRxiv : the preprint server for health sciences·2026
Same author

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

medRxiv : the preprint server for health sciences·2025
Same author

Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospitals using genomic, microbiological and patient mobility data.

Nature communications·2025
Same author

Adaptive mobility responses during Hurricanes Helene and Milton in 2024.

medRxiv : the preprint server for health sciences·2025
Same journal

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.

medRxiv : the preprint server for health sciences·2026
Same journal

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease.

medRxiv : the preprint server for health sciences·2026
Same journal

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity.

medRxiv : the preprint server for health sciences·2026
Same journal

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

medRxiv : the preprint server for health sciences·2026
Same journal

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026
Same journal

A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: Jan 10, 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

Inferring pathogen superspreading potential using early spatial spread patterns.

Qing Yao1, Renquan Zhang2, Tom Britton3

  • 1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA.

Medrxiv : the Preprint Server for Health Sciences
|November 24, 2025
PubMed
Summary
This summary is machine-generated.

Individual variation in pathogen transmission, known as superspreading, significantly impacts disease spread. This study introduces a novel method using spatial data to quantify superspreading potential, revealing its dynamics during the COVID-19 pandemic.

More Related Videos

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.5K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K

Related Experiment Videos

Last Updated: Jan 10, 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
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.5K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Science

Background:

  • Superspreading, driven by individual transmission variation, is crucial for pathogen emergence and control.
  • Existing methods for estimating superspreading often rely on contact tracing or genomic data, which can be limited by availability and sampling bias.

Purpose of the Study:

  • To investigate the impact of superspreading on the early spatial spread of novel pathogens.
  • To develop and validate a method for inferring superspreading potential from population-level spatial spread data.

Main Methods:

  • Utilized a branching process model incorporating inter-county mobility in the US to simulate spatial spread.
  • Modeled transmission heterogeneity using a negative binomial distribution with a dispersion parameter (r) for superspreading.
  • Employed a graph neural network for inferring the dispersion parameter (r) from early spatial epidemic patterns.

Main Results:

  • Simulations indicated that increased superspreading can slow early spatial invasion and increase epidemic growth variability.
  • The graph neural network reliably inferred superspreading potential (r) from spatial spread data, robust to variations in reproduction number (R0) and underreporting.
  • Analysis of early COVID-19 data in the US showed strong superspreading before lockdown (r=0.50) and weaker superspreading after (r=1.3).

Conclusions:

  • This study presents a novel approach to quantify pathogen superspreading potential using observable population-level spatial spread.
  • The findings highlight the dynamic nature of superspreading and its potential to be inferred from early epidemic data.
  • The developed method offers a valuable tool for understanding and managing infectious disease outbreaks.