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The nature of light has been a subject of inquiry since antiquity. In the seventeenth century, Isaac Newton performed experiments with lenses and prisms and was able to demonstrate that white light consists of the individual colors of the rainbow combined together. Newton explained his optics findings in terms of a "corpuscular" view of light, in which light was composed of streams of extremely tiny particles traveling at high speeds according to Newton's laws of motion.

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Related Experiment Video

Updated: Jan 20, 2026

Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells

Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells

Published on: May 19, 2023

A novel sub-epidemic modeling framework for short-term forecasting epidemic waves.

Gerardo Chowell^1,2, Amna Tariq³, James M Hyman⁴

¹Department of Population Heath Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA. gchowell@gsu.edu.

|August 24, 2019

Summary

A new sub-epidemic modeling framework accurately forecasts complex epidemic trajectories, outperforming simpler models in predicting outbreaks like SARS and Ebola. This approach captures oscillations often missed, improving public health intervention assessments.

Keywords:

Democratic Republic of Congo Ebola Epidemic wave Forecast Mathematical framework Mean interval score Plague Reporting delay SARS Sub-epidemic Uncertainty Unobserved heterogeneity

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Area of Science:

Epidemiology
Mathematical Modeling
Public Health

Background:

Phenomenological growth models are used for epidemic forecasting.
Existing models often fail to capture complex, multi-peak outbreak dynamics.

Purpose of the Study:

Develop a novel sub-epidemic modeling framework to support diverse epidemic trajectories.
Improve forecasting of epidemic outbreaks with complex dynamics, including oscillations.

Main Methods:

Developed a sub-epidemic modeling framework decomposing coarse-scale incidence into finer-scale sub-epidemics.
Evaluated the model using datasets from SARS, plague, and Ebola outbreaks.
Utilized performance metrics including mean interval score and prediction interval coverage.

Main Results:

The sub-epidemic wave model demonstrated superior short-term forecasting compared to simpler models.
Successfully captured the two-peak pattern of the SARS outbreak in Singapore.
Forecasted the second surge in SARS cases, which simpler models could not.

Conclusions:

Overlapping sub-epidemics effectively model complex epidemic dynamics and oscillations.
Accurate modeling of oscillations is crucial for interpreting incidence data and intervention impact.
Sub-epidemic models offer insights into factors shaping epidemic patterns for various infectious diseases.