ArticleComputational and mathematical organization theory2021
Computational simulation of the COVID-19 epidemic with the SEIR stochastic model.
Article in Computational and mathematical organization theory, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 29 citations in OpenAlex.
- Mathematical models to characterize the early phase of the COVID-19 pandemic in New Mexico, USA.Mathematical biosciences and engineering : MBE · 2025Article
- Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic.Human vaccines & immunotherapeutics · 2024Article
- The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling.Healthcare (Basel, Switzerland) · 2023Article
- Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach.Spatial statistics · 2023Article
- Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.Healthcare (Basel, Switzerland) · 2023Article
- The impact of multiple non-pharmaceutical interventions for China-bound travel on domestic COVID-19 outbreaks.Frontiers in public health · 2023Article
- COVID-19 multiwaves as multiphase percolation: a general N-sigmoidal equation to model the spread.European physical journal plus · 2023Article
- Modelling the impact of health care providers in transmission dynamics of COVID-19.Results in physics · 2022Article
- COVID-19 waves: variant dynamics and control.Scientific reports · 2022Article
- Editorial of the Special Issue from WorldCIST'20.Computational and mathematical organization theory · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A small number of individuals infected within a community can lead to the rapid spread of the disease throughout that community, leading to an epidemic outbreak. This is even more true for highly contagious diseases such as COVID-19, known to be caused by the new coronavirus SARS-CoV-2. Mathematical models of epidemics allow estimating several impacts on the population and, therefore, are of great use for the definition of public health policies. Some of these measures include the isolation of the infected (also known as quarantine), and the vaccination of the susceptible. In a possible scenario in which a vaccine is available, but with limited access, it is necessary to quantify the levels of vaccination to be applied, taking into account the continued application of preventive measures. This work concerns the simulation of the spread of the COVID-19 disease in a community by applying the Monte Carlo method to a Susceptible-Exposed-Infective-Recovered (SEIR) stochastic epidemic model. To handle the computational effort involved, a simple parallelization approach was adopted and deployed in a small HPC cluster. The developed computational method allows to realistically simulate the spread of COVID-19 in a medium-sized community and to study the effect of preventive measures such as quarantine and vaccination. The results show that an effective combination of vaccination with quarantine can prevent the appearance of major epidemic outbreaks, even if the critical vaccination coverage is not reached.
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