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

330
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:
330
Causality in Epidemiology01:21

Causality in Epidemiology

1.2K
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...
1.2K
Censoring Survival Data01:09

Censoring Survival Data

360
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
360
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.3K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.3K
Contingency Table01:29

Contingency Table

3.3K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
3.3K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

403
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
403

You might also read

Related Articles

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

Sort by
Same author

Dynamical analysis of a stochastic dual-strain infectious model with hospital beds and logarithmic Ornstein-Uhlenbeck process.

Journal of mathematical biology·2026
Same author

Rapid assessment of local disease control measures against the Marburg virus outbreak in Ethiopia in late 2025.

Infectious Disease Modelling·2026
Same author

Epidemiological and economic effect of preference-driven HIV pre-exposure prophylaxis implementation for men who have sex with men and transgender women across 16 countries and territories in the Asia-Pacific region: a modelling study.

The lancet. HIV·2026
Same author

Heat effects on influenza-like illness and heterogeneity factors across regions: based on 333 cities in China.

BMC public health·2026
Same author

Sensitivity of Convergent Cross Mapping to temporal discontinuities: A case study of seasonal influenza in Hong Kong.

Infectious Disease Modelling·2026
Same author

Role of diagnostic testing in reducing unnecessary antibiotic use for upper respiratory tract infections in Chinese primary healthcare: a mixed-methods study.

BMC primary care·2026

Related Experiment Video

Updated: Nov 18, 2025

Author Spotlight: Studying Host-Virus Interactions with Pseudotyped Viruses
05:49

Author Spotlight: Studying Host-Virus Interactions with Pseudotyped Viruses

Published on: November 21, 2023

2.1K

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.

Shi Zhao1,2, Mingwang Shen3, Salihu S Musa4,5

  • 1JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. zhaoshi.cmsa@gmail.com.

BMC Medical Research Methodology
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

A new zero-truncated framework improves estimates of infectious disease transmission heterogeneity (k), crucial for understanding superspreading events and managing pandemics like COVID-19.

Keywords:
COVID-19Contact tracingHeterogeneity in infectiousnessStatistical inferenceSuperspreadingTransmission

More Related Videos

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research
06:08

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research

Published on: September 8, 2023

1.5K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K

Related Experiment Videos

Last Updated: Nov 18, 2025

Author Spotlight: Studying Host-Virus Interactions with Pseudotyped Viruses
05:49

Author Spotlight: Studying Host-Virus Interactions with Pseudotyped Viruses

Published on: November 21, 2023

2.1K
Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research
06:08

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research

Published on: September 8, 2023

1.5K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K

Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Statistical Inference

Background:

  • High heterogeneity in individual infectiousness leads to superspreading events.
  • Transmission heterogeneity is often quantified using the negative binomial (NB) distribution's dispersion parameter (k).
  • Existing frameworks may neglect under-ascertainment of sporadic cases, biasing estimates of k.

Purpose of the Study:

  • To develop and evaluate a zero-truncated likelihood-based framework for estimating transmission heterogeneity (k).
  • To address bias introduced by under-ascertainment of cases with zero secondary transmissions.
  • To apply the framework to COVID-19 contact tracing data.

Main Methods:

  • Employed a zero-truncated likelihood-based approach to estimate the dispersion parameter (k).
  • Utilized stochastic simulations to assess estimation performance compared to a non-truncated baseline.
  • Applied the analytical framework to three COVID-19 contact tracing datasets.

Main Results:

  • The zero-truncated framework effectively reduces bias caused by under-ascertainment of index cases with zero secondary cases.
  • The estimated dispersion parameter (k) for COVID-19 was 0.32 (95% CI: 0.15, 0.64), potentially lower than previous estimates.
  • Simulation codes for the inference framework are provided.

Conclusions:

  • The zero-truncated framework is recommended for more accurate estimation of transmission heterogeneity.
  • Findings underscore the importance of targeted case management strategies for potential superspreaders to mitigate pandemics.
  • Accurate estimation of k is vital for effective public health interventions.