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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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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.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Mathematical model to predict COVID-19 mortality rate.

Melika Yajada1, Mohammad Karimi Moridani1, Saba Rasouli1

  • 1Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.

Infectious Disease Modelling
|November 21, 2022
PubMed
Summary

This study developed a Smoothing Spline model to predict COVID-19 cases and mortality rates in Iran, the US, and South Korea. The model accurately assessed and compared disease progression, aiding public health strategies.

Keywords:
Covid-19Curve fittingMathematical modelingMortalityPrediction

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • COVID-19 (Coronavirus Disease 2019) became a global pandemic, causing significant mortality.
  • Accurate prediction of disease incidence and mortality is crucial for effective public health interventions.

Purpose of the Study:

  • To predict and compare quarterly COVID-19 cases and mortality rates for 2020-2021 in Iran, the United States, and South Korea.
  • To evaluate the performance of various mathematical models for mortality rate prediction.

Main Methods:

  • Utilized World Health Organization (WHO) approved COVID-19 mortality data from March 2020 to March 2022.
  • Applied six mathematical modeling methods: Fourier, Interpolant, Gaussian, Polynomial, Sum of Sine, and Smoothing Spline.
  • Evaluated model performance using Root Mean Square Error (RMSE) and Final Prediction Error.

Main Results:

  • The Smoothing Spline model demonstrated the lowest error rate, accurately predicting COVID-19 incidence and mortality.
  • Achieved an RMSE of 3.76498 × 10-5 for mortality rate prediction across the three countries.
  • R-Square and Adj R-sq values of 1 indicated full compliance of the prediction model.

Conclusions:

  • The Smoothing Spline model provides an effective tool for assessing and comparing COVID-19 incidence and mortality.
  • The model offers insights into seasonal disease progression, aiding governments in implementing preventative measures.
  • Improved disease assessment can lead to better public health guidance, reducing infection and mortality rates.