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

689
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:
689

You might also read

Related Articles

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

Sort by
Same author

Integrated surveillance resolves Darién paradox of Oropouche virus emergence in Panama's migration corridor.

Research square·2026
Same author

Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.

Nature communications·2026
Same author

Estimating the in vivo prophylactic effect of mosnodenvir, a novel dengue antiviral, on DENV-2 infection.

Journal of the Royal Society, Interface·2026
Same author

Estimation of the Ebola outbreak size in the Democratic Republic of the Congo.

The Lancet. Infectious diseases·2026
Same author

Modelling the risk of West Nile virus infection in seven European countries from published serological and case notification data, 2008 to 2022.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026
Same author

The cost-effectiveness of Wolbachia-based biocontrol interventions for dengue: A scoping review of the available evidence.

PLoS neglected tropical diseases·2026
Same journal

Flubendazole 2.0: Designing a large field trial in dogs for a challenging setting.

PLoS neglected tropical diseases·2026
Same journal

Temporal and spatial patterns of Leprosy in Uganda, 2020-2024: A nationwide surveillance analysis.

PLoS neglected tropical diseases·2026
Same journal

Prevalence and risk factors of asymptomatic Plasmodium spp. infection in the military population of the Colombian National Army.

PLoS neglected tropical diseases·2026
Same journal

Mobility and non-household environments: Understanding dengue transmission patterns in urban contexts.

PLoS neglected tropical diseases·2026
Same journal

Hantavirus stability and inactivation.

PLoS neglected tropical diseases·2026
Same journal

Disability as a neglected outcome of neglected tropical diseases: A systematic review.

PLoS neglected tropical diseases·2026
See all related articles

Related Experiment Video

Updated: Mar 18, 2026

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

2.2K

Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.

Natsuko Imai1, Ilaria Dorigatti1, Simon Cauchemez2

  • 1MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.

Plos Neglected Tropical Diseases
|July 12, 2016
PubMed
Summary
This summary is machine-generated.

Estimating dengue transmission intensity is crucial for disease control. This study developed a method using incidence data to estimate dengue

More Related Videos

A Simple Flow Cytometry Based Assay to Determine In Vitro Antibody Dependent Enhancement of Dengue Virus Using Zika Virus Convalescent Serum
07:06

A Simple Flow Cytometry Based Assay to Determine In Vitro Antibody Dependent Enhancement of Dengue Virus Using Zika Virus Convalescent Serum

Published on: April 10, 2018

9.2K
Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction
08:36

Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction

Published on: November 1, 2018

32.7K

Related Experiment Videos

Last Updated: Mar 18, 2026

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

2.2K
A Simple Flow Cytometry Based Assay to Determine In Vitro Antibody Dependent Enhancement of Dengue Virus Using Zika Virus Convalescent Serum
07:06

A Simple Flow Cytometry Based Assay to Determine In Vitro Antibody Dependent Enhancement of Dengue Virus Using Zika Virus Convalescent Serum

Published on: April 10, 2018

9.2K
Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction
08:36

Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction

Published on: November 1, 2018

32.7K

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Public Health

Background:

  • Dengue is a widespread mosquito-borne viral infection with ambiguous transmission intensity estimates.
  • Accurate dengue burden and intervention impact assessments require robust transmission intensity data.

Purpose of the Study:

  • To estimate dengue transmission intensity, force of infection (λ), and basic reproduction numbers (R0).
  • To develop a method for estimating transmission intensity using age-stratified incidence data.
  • To assess dengue under-reporting and the contribution of different infection orders to disease burden.

Main Methods:

  • Fitting catalytic models to age-stratified incidence data from literature.
  • Comparing estimates from incidence and seroprevalence data.
  • Estimating the probability of detecting secondary dengue infections.

Main Results:

  • Basic reproduction numbers (R0) generally ranged from one to five.
  • Force of infection estimates from incidence data aligned with seroprevalence data.
  • Dengue reporting rates were often low (<25%) and varied geographically.

Conclusions:

  • Dengue transmission is heterogeneous across space and time.
  • Incidence models offer a viable method for estimating dengue transmission intensity where seroprevalence data are unavailable.
  • This method aids in assessing disease burden and intervention effectiveness.