ArticleInfectious Disease Modelling2023
An agent-based model with antibody dynamics information in COVID-19 epidemic simulation.
Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SIMPLICITY is an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.Communications biology · 2026Article
- Early-warning prediction of visceral leishmaniasis mortality using a multivariate STL-deep learning hybrid approach on 20 years of monthly time series.Frontiers in public health · 2026Article
- Dynamics of an epidemic controlled by isolation and quarantine: A probability-based deterministic model.Infectious Disease Modelling · 2025Article
- A mathematical model simulating the adaptive immune response in various vaccines and vaccination strategies.Scientific reports · 2024Article
- More or less deadly? A mathematical model that predicts SARS-CoV-2 evolutionary direction.Computers in biology and medicine · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.
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