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Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Population Growth

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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

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Related Experiment Video

Updated: Jan 17, 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

A Curve-Fitting Approach for Generating Long-Term Projections of COVID-19 Mortality.

George Kafatos¹, George Seegan², Bagmeet Behera³

¹Center for Observational Research, Amgen Ltd, Uxbridge, UK.

Disaster Medicine and Public Health Preparedness

|September 16, 2025

Summary

This study introduces a curve-fitting method for long-term COVID-19 mortality forecasting. The approach provides scalable, data-driven pandemic projections with potential for future public health applications.

Keywords:

COVID-19 model projections

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Measurement of Lifespan in Drosophila melanogaster

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Last Updated: Jan 17, 2026

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

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Measurement of Lifespan in Drosophila melanogaster

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Published on: January 7, 2013

Area of Science:

Epidemiology
Mathematical Modeling
Public Health

Background:

Early COVID-19 pandemic lacked understanding of long-term impact.
Existing models offered limited short- to mid-term projections.
Need for accessible, long-term pandemic forecasting tools.

Purpose of the Study:

Develop a curve-fitting approach for long-term COVID-19 mortality projections.
Evaluate its effectiveness as a scalable, data-driven forecasting tool.
Provide a foundation for future pandemic modeling.

Main Methods:

Described a dynamic curve-fitting approach for long-term projections.
Retrospectively applied the model using January-June 2020 mortality data.
Generated 11-month projections (June 2020-April 2021).

Main Results:

The best-fit scenarios showed a 7.7% to 28.2% difference between observed and projected total deaths.
Demonstrated the model's capability for retrospective forecasting.
Indicated the potential accuracy of the projection method.

Conclusions:

The developed approach offers relatively easy implementation for long-term projections.
The method can be enhanced with parameters like vaccine impact or virus variants.
This curve-fitting technique could be a valuable tool for future pandemics.