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The stochastic -SEIHRD model: Adding randomness to the COVID-19 spread.

Álvaro Leitao1,2, Carlos Vázquez1,2

  • 1CITIC, Universidade da Coruña, Spain.

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Summary
This summary is machine-generated.

This study enhances a COVID-19 deterministic model by introducing stochastic elements, creating a more realistic simulation. The new stochastic model allows for uncertainty analysis, including confidence intervals and worst-case scenarios for COVID-19 dynamics.

Keywords:
60G9965C0565C30CIR processCOVID-19Compartmental modelsMonte Carlo simulationRODEsStochastic modelling

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Stochastic Modeling

Background:

  • Deterministic models are widely used for studying infectious disease dynamics, including COVID-19.
  • However, deterministic models do not fully capture the inherent randomness and uncertainties present in real-world disease transmission.
  • There is a need for more flexible and realistic models to better understand and predict disease spread.

Purpose of the Study:

  • To extend a previously developed deterministic COVID-19 model into a stochastic framework.
  • To incorporate randomness into key model coefficients, reflecting real-world uncertainties.
  • To enable the simulation of COVID-19 dynamics using stochastic differential equations.

Main Methods:

  • Modification of a deterministic COVID-19 model by introducing stochasticity in selected coefficients.
  • Assumption that these coefficients follow a defined stochastic dynamics.
  • Simulation of the resulting stochastic process by solving the system of stochastic differential equations.

Main Results:

  • The developed stochastic model provides a more complete and flexible representation of COVID-19 dynamics compared to its deterministic counterpart.
  • The model successfully incorporates additional uncertainties inherent in realistic epidemiological scenarios.
  • It allows for the computation of confidence intervals for key variables and the analysis of worst-case scenarios.

Conclusions:

  • The stochastic extension of the COVID-19 model offers enhanced realism by accounting for inherent uncertainties.
  • This approach facilitates more robust predictions and risk assessments, including the quantification of potential disease spread variability.
  • The model serves as a valuable tool for understanding complex epidemiological dynamics and informing public health strategies.