Identification of COVID-19 spread mechanisms based on first-wave data, simulation models, and evolutionary algorithms.

ABSTRACT:

Background

The COVID-19 epidemic has shown that efficient prediction models are required, and the well-known SI, SIR, and SEIR models are not always capable of capturing the real dynamics. Modified models with novel structures could help identify unknown mechanisms of COVID-19 spread.

Objective

Our objective is to provide additional insights into the COVID-19 spread mechanisms based on different models' parameterization which was performed using evolutionary algorithms and the first-wave data.

Methods

Data from the Our World in Data COVID-19 database was analysed, and several models-SI, SIR, SEIR, SEIUR, and Bass diffusion-and their variations were considered for the first wave of the COVID-19 pandemic. The models' parameters were tuned with differential evolution opt

SUBMITTER: Stanovov V 

PROVIDER: S-EPMC9797101 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature