Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Assessing Body Temperature - Temporal Artery

Assessing Body Temperature - Temporal Artery

Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
Step 1: Perform hand hygiene and don a fresh pair of gloves to prevent cross-infection and ensure patient safety.
Step 2: Explain the procedure to the patient to establish trust. Clear communication establishes trust with the patient, ensures they understand what to expect, promotes cooperation, and enhances comfort during the procedure.
Step 3: Assess the patient's...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Quantifying Heat

Quantifying Heat

Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...

Assessing Body Temperature - Axilla

Assessing Body Temperature - Axilla

Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...

You might also read

Related Articles

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

Sort by

Same author

Electrospun Zn-cobimetinib-levofloxacin-PVA/PVP nanofibers for skin-targeted anticancer and antimicrobial applications.

RSC advances·2026

Same author

A landscape analysis of enablers and barriers to digital payments to health workers during large-scale immunisation campaigns in sub-Saharan Africa: a scoping review and in-depth interviews.

Global health action·2026

Same author

Whole genome sequence data on diverse pearl millet accessions across the African continent.

Data in brief·2026

Same author

Sheep feeding preference and methane mitigation potential of fodder tree leaves in the Sahelian silvopastoral zone of Senegal.

Archives of animal nutrition·2026

Same author

Multimodal pulse oximeters to support the integrated management of childhood illnesses: A usability and diagnostic accuracy assessment from a multi-country hybrid type 2 study.

PLOS global public health·2026

Same author

High-Resolution Downscaled CMIP6 Projections dataset of Key Climate Variables for Senegal.

Scientific data·2026

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Machine Learning-Based Prediction of Heatwave-Related Hospitalizations: A Case Study in Matam, Senegal.

Mory Toure^1,2, Ibrahima Sy^3,4,5, Ibrahima Diouf^2,6

¹Agence Nationale de l'Aviation Civile et de la Météorologie (ANACIM), Dakar BP 8184, Senegal.

International Journal of Environmental Research and Public Health

|September 27, 2025

Summary

This summary is machine-generated.

Heatwaves significantly increase hospital admissions in Senegal's Matam region, with a notable delay of 3-5 days. Enhanced climate forecasting and heatwave monitoring are crucial for public health, especially for vulnerable populations.

Keywords:

Senegal climate-health early warning systems heatwave hospital admissions machine learning

Related Experiment Videos

Last Updated: Jan 16, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Environmental Health
Climate Science
Public Health

Background:

Heatwaves pose a growing threat to public health globally.
Understanding the specific impacts of heatwaves on healthcare systems is vital for adaptation.
Senegal's Matam region is particularly vulnerable to extreme heat events.

Purpose of the Study:

To assess the impact of heatwaves on hospital admissions in the Matam region, Senegal.
To identify the temporal relationship between heatwave events and hospitalization rates.
To compare the performance of different machine learning models in predicting heatwave-related hospitalizations.

Main Methods:

Utilized daily maximum temperature (TMAX) and heat index (HI) to identify heatwave events (2017-2022).
Analyzed hospitalization data from Ourossogui Regional Hospital.
Employed and compared Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs) with bootstrapping for robustness.

Main Results:

A significant delayed effect was observed, with hospitalizations peaking 3-5 days post-heatwave.
The Random Forest model demonstrated superior performance, achieving R² values between 0.51 and 0.72.
Heatwave events were strongly correlated with increased hospital admissions.

Conclusions:

Heatwaves have a discernible delayed impact on hospital admissions in the Matam region.
Random Forest is an effective tool for modeling heatwave-related health impacts.
Integrating impact-based climate forecasting into health early warning systems is essential for protecting vulnerable groups.