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

Prevalence and Incidence01:08

Prevalence and Incidence

1.6K
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
1.6K
Predator-Prey Interactions02:39

Predator-Prey Interactions

21.1K
Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
21.1K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.0K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.0K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.0K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Data Collection I01:30

Data Collection I

7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K

You might also read

Related Articles

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

Sort by
Same author

Time to collect informed consent to participate to randomized control trials in Intensive Care Unit patients: a single-center retrospective study over 7 years.

Minerva anestesiologica·2026
Same author

Modelling impacts of paediatric amoxicillin shortage management on pneumococcal resistance and invasive disease in Europe.

Nature communications·2026
Same author

Age-dependent clinical and molecular rhinovirus epidemiology, 2018 to 2023.

The Journal of infectious diseases·2026
Same author

Genome wide association study of vaginal microbiota genetic diversity in French women.

Open research Europe·2026
Same author

Resource landscape shapes the composition and stability of the human vaginal microbiota.

PLoS biology·2026
Same author

A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.

bioRxiv : the preprint server for biology·2025
Same journal

Spatio-temporal modeling of zoonotic cutaneous leishmaniasis (ZCL) in the Algerian steppe: Epidemiological insights and climatic associations.

Epidemics·2026
Same journal

Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review.

Epidemics·2026
Same journal

Identifying memory mechanisms in Bayesian models of behavioural change during epidemics.

Epidemics·2026
Same journal

Mapping the landscape of individual-based models for respiratory pathogen transmission in the pandemic and post-pandemic era (2020-2024): A systematic review.

Epidemics·2026
Same journal

A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.

Epidemics·2026
Same journal

Modelling serological cross-reactivity to disentangle the dynamics of West Nile and Usutu viruses in an emerging area.

Epidemics·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.6K

Detecting within-host interactions from genotype combination prevalence data.

Samuel Alizon1, Carmen Lía Murall1, Emma Saulnier1

  • 1MIVEGEC, CNRS, IRD, Université de Montpellier, France.

Epidemics
|July 2, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to analyze parasite genetic diversity and detect within-host interactions from epidemiological data. The approach shows promise for understanding complex infections like Human Papillomaviruses (HPVs).

Keywords:
ABCCompetitionInferenceMOIMultiple infectionsSuperspreaders

More Related Videos

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

11.6K
Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
07:21

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells

Published on: September 17, 2021

4.0K

Related Experiment Videos

Last Updated: Jan 22, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.6K
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

11.6K
Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
07:21

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells

Published on: September 17, 2021

4.0K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Parasitology

Background:

  • Parasite genetic diversity offers insights into disease transmission dynamics.
  • Current mathematical and statistical models often overlook specific genotype combinations in infections.

Purpose of the Study:

  • To introduce and validate a novel method for detecting within-host parasite interactions.
  • To assess the method's robustness against host heterogeneity in epidemiological data analysis.

Main Methods:

  • Combines explicit epidemiological modeling of coinfections with regression-Approximate Bayesian Computing (ABC).
  • Utilizes a susceptible-infected-susceptible (SIS) model for simulations.
  • Applies the method to analyze multiple infection prevalence data.

Main Results:

  • Within-host parasite interactions can be detected from epidemiological data if interactions are sufficiently strong.
  • Detection remains robust despite some host behavioral heterogeneity.
  • The method is suitable for large datasets, including those for Human Papillomaviruses (HPVs).

Conclusions:

  • The developed regression-ABC method effectively detects within-host parasite interactions.
  • This approach enhances the analysis of complex infection dynamics and genetic diversity.
  • It holds significant potential for studying prevalent infections like HPV.