Related Experiment Video
Updated: Aug 16, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time
Amila Madiligama1, Zachary Vandervort2, Arshad Khan2
1Department of Physics, Pennsylvania State University, DuBois, Pennsylvania15801, United States.
Abstract:
Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection-time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant "half-life" relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one "half-life"), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions.
Related Concept Videos
Steps in Outbreak Investigation
Hazard Rate
Residuals and Least-Squares Property
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...
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Parametric Survival Analysis: Weibull and Exponential Methods
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...
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

