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

Relative Risk01:12

Relative Risk

2.2K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.2K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
Random and Systematic Errors01:20

Random and Systematic Errors

15.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.2K
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

13.7K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
13.7K

You might also read

Related Articles

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

Sort by
Same author

How (not) to organise a panel at a global health conference.

The Lancet. Global health·2026
Same author

Integration of Continuous Glucose Monitoring With HbA<sub>1c</sub> to Improve the Detection of Prediabetes in Asian Individuals: Model Development Study.

JMIR diabetes·2026
Same author

Factors associated with onward SARS-CoV-2 transmission among household and dormitory contacts of cases in Brunei Darussalam, August 2021 to February 2022: A retrospective cohort study.

IJID regions·2026
Same author

Comparison of contact tracing methods: A modelling study.

Infectious Disease Modelling·2025
Same author

Optimal design of financial rebates to encourage dental visits.

Journal of dentistry·2025
Same author

A case report of <i>Herbaspirillum</i> infection in rural Australia.

ASM case reports·2025
Same journal

Development and international multicenter evaluation of a second-generation immunochromatography test for the serological diagnosis of melioidosis.

PLoS neglected tropical diseases·2026
Same journal

Schistosomiasis mansoni and alcohol abuse comorbidity: Prevalence and risk factors among adults in Makenene, Cameroon.

PLoS neglected tropical diseases·2026
Same journal

Chagas Disease in northern Minas Gerais: Clinical and epidemiological features of patients at a pioneering specialized outpatient service.

PLoS neglected tropical diseases·2026
Same journal

Forest-river interfaces shape lobomycosis risk in the Amazon Basin.

PLoS neglected tropical diseases·2026
Same journal

Completion of rabies post-exposure prophylaxis in Ouagadougou, Burkina Faso, 2021-2023: A cross-sectional analysis of routine data.

PLoS neglected tropical diseases·2026
Same journal

Cost analysis of implementing community-based mass drug administration for schistosomiasis control among adult individuals using community drug distributors in Ukerewe district council, North-Western Tanzania.

PLoS neglected tropical diseases·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

14.4K

Mapping dengue risk in Singapore using Random Forest.

Janet Ong1, Xu Liu1, Jayanthi Rajarethinam1

  • 1Environmental Health Institute, National Environment Agency, Singapore.

Plos Neglected Tropical Diseases
|June 19, 2018
PubMed
Summary
This summary is machine-generated.

This study developed an accurate dengue risk map for Singapore using Random Forest. The map effectively identifies high-risk areas, guiding vector control efforts and optimizing resource allocation for dengue prevention.

More Related Videos

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
09:16

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

17.4K
Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

7.5K

Related Experiment Videos

Last Updated: Feb 8, 2026

Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

14.4K
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
09:16

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

17.4K
Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

7.5K

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Machine Learning

Background:

  • Singapore faces endemic dengue, with 2013 marking its largest outbreak.
  • Effective vector control is crucial for dengue prevention due to limited resources.
  • Stratifying spatial dengue transmission risk is vital for targeted resource deployment.

Purpose of the Study:

  • To stratify the spatial risk of dengue transmission in Singapore.
  • To create a dengue risk map for effective resource allocation in vector control.
  • To evaluate the accuracy of predictive modeling for dengue surveillance.

Main Methods:

  • Random Forest algorithm employed to predict dengue transmission risk in 1km² grids.
  • Integration of dengue, population, entomological, and environmental data for risk prediction.
  • Categorization and mapping of predicted risk ranks into four color-coded risk groups.

Main Results:

  • Random Forest models demonstrated high accuracy in predicting dengue risk.
  • Over 80% of observed risk ranks fell within the 80% prediction interval.
  • Predicted risk levels showed excellent agreement with case density (Kappa > 0.80).
  • Approximately 90% of dengue clusters occurred in high-risk areas identified by the model.

Conclusions:

  • Random Forest is a powerful tool for stratifying spatial dengue transmission risk.
  • The generated dengue risk map is accurate and serves as an effective surveillance tool.
  • The risk map can guide vector control operations and optimize resource deployment for dengue prevention.