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Related Concept Videos

Derivatives: Problem Solving01:26

Derivatives: Problem Solving

Temperature-Dependent Growth of Brook TroutThe growth of brook trout is closely influenced by water temperature. Experimental data demonstrate how trout weight changes over a 24-day period in response to varying water temperatures. At lower temperatures, such as 15.5 degrees Celsius, brook trout show significant weight gain. However, as the temperature increases, the amount of weight gained steadily decreases. At the highest temperature measured, 24.4 degrees Celsius, trout experience a net...
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Regression Analysis01:11

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Actuarial Approach01:20

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Updated: Jul 14, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

A predictive model relating daily fluctuations in summer temperatures and mortality rates.

Anne Fouillet1, Grégoire Rey, Eric Jougla

  • 1INSERM, U754, Villejuif, France. fouillet@vjf.inserm.fr <fouillet@vjf.inserm.fr>

BMC Public Health
|June 21, 2007
PubMed
Summary

This study developed a model to predict summer mortality fluctuations in France based on temperature. The model accurately forecasts daily mortality, improving heat wave alert systems.

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Last Updated: Jul 14, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

Area of Science:

  • Environmental Epidemiology
  • Biometeorology
  • Public Health

Background:

  • Climate change necessitates effective heat-related risk alert systems.
  • Understanding the temperature-mortality relationship is crucial for public health.
  • France experienced significant heat-related mortality risks over 29 years.

Purpose of the Study:

  • To describe the temperature-mortality relationship in France (1975-2003).
  • To define and validate temperature factors for predicting summer mortality fluctuations.
  • To enhance heat wave alert systems.

Main Methods:

  • Analysis of daily mortality rates (aged >55) and meteorological data (1975-2003).
  • Poisson regression modeling with autoregressive structure and lag effects (5 days).
  • Validation using a distinct period (summer 2003).

Main Results:

  • Temperature indicators explained 76% of mortality over-dispersion.
  • Mortality fluctuations were primarily driven by daily minimum and maximum temperature interactions.
  • Model accurately predicted mortality during usual and extreme heat events (correlation 0.88).

Conclusions:

  • A strong national-scale correlation exists between summer temperatures and daily mortality in France.
  • The developed model accurately predicts daily mortality, even during heat waves.
  • Findings support improved heat wave alert systems for public health protection.