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Updated: Dec 3, 2025

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A discrete-time-evolution model to forecast progress of Covid-19 outbreak.

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This study introduces a simple, two-parameter discrete-time model to forecast Covid-19 spread using daily data. The model accurately predicts total infected individuals for 14 days, aiding public health planning.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Forecasting infectious disease propagation, such as Covid-19, is crucial for effective public health interventions.
  • Existing models may require complex data inputs or numerous parameters, limiting their widespread implementation.
  • Understanding the transmission dynamics, including asymptomatic and symptomatic phases, is key to accurate prediction.

Purpose of the Study:

  • To develop and present a user-friendly discrete-time-evolution model for forecasting Covid-19 propagation.
  • To create a tool that utilizes easily accessible, daily updated pandemic data.
  • To provide a 14-day forecast of total infected individuals with minimal adjustable parameters.

Main Methods:

  • A discrete-time-evolution model with a one-day interval was developed.
  • The model incorporates disease aspects like asymptomatic/symptomatic phases and a two-week evolution period.
  • The model was validated using publicly available daily Covid-19 data from Brazil, the UK, and South Korea.

Main Results:

  • The model demonstrates low error rates in predicting the evolution of Covid-19 in Brazil, the UK, and South Korea.
  • It successfully forecasts the total number of infected people for the subsequent 14 days.
  • The model directly outputs daily total infected case numbers, facilitating health sector implementation.

Conclusions:

  • The presented discrete-time model offers a practical and accurate tool for forecasting Covid-19 propagation.
  • Its simplicity and reliance on readily available data make it suitable for health sector use.
  • The model's low error rates suggest its utility in estimating disease spread and informing public health strategies.