ArticleBiology2022
A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- A systematic review of spatial epidemiological modeling approaches applied during the COVID-19 pandemic.BMC public health · 2026Pooled it
- Estimating the potential public health and economic impact of vaccination timing: A modeling study of COVID-19 vaccination in the United Kingdom.Human vaccines & immunotherapeutics · 2026Article
- Inference of latent epidemic regimes and generative simulations reveal how inequality and mobility shape COVID-19 transmission.Scientific reports · 2026Article
- Potential Public Health Impact of Updated COVID-19 Vaccination Strategies in Malaysia: Epidemiological Data Update.Pulmonary therapy · 2026Article
- Modeling the Potential Public Health Impact of Updated COVID-19 Vaccination Strategies in Singapore: Epidemiological Data Update.Pulmonary therapy · 2026Article
- A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.medRxiv : the preprint server for health sciences · 2025Article
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- Bioinformatic analysis of defective viral genomes in SARS-CoV-2 and its impact on population infection characteristics.Frontiers in immunology · 2024Article
- An agent-based model with antibody dynamics information in COVID-19 epidemic simulation.Infectious Disease Modelling · 2023Article
- A Novel Mathematical Model That Predicts the Protection Time of SARS-CoV-2 Antibodies.Viruses · 2023Article
- Statistical analysis supports UTR (untranslated region) deletion theory in SARS-CoV-2.Virulence · 2022Article
- Article
- Application of piecewise fractional differential equation to COVID-19 infection dynamics.Results in physics · 2022Article
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed in this model based upon the contact frequency of individuals within the population, the individual's characteristics, and other factors that can effectively reflect the epidemic's temporal and spatial variation characteristics. The Markov-chain model was then extended to incorporate both the mutation effect of COVID-19 and the decaying effect of antibodies. The developed comprehensive Markov-chain model that integrates the aforementioned factors was finally tested by real data to predict the trend of the COVID-19 epidemic. The result shows that our model can effectively avoid the prediction dilemma that may exist with traditional ordinary differential equations model, such as the susceptible-infectious-recovered (SIR) model. Meanwhile, it can forecast the epidemic distribution and predict the epidemic hotspots geographically at different times. It is also demonstrated in our result that the influence of the population's spatial and geographic distribution in a herd infection event is needed in the model for a better prediction of the epidemic trend. At the same time, our result indicates that no simple derivative relationship exists between the threshold of herd immunity and the virus basic reproduction number
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.