ArticlePloS one2022
Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model.
Article in PloS one, 2022. 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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10 citing papers in PubMed, 32 citations in OpenAlex.
- Bayesian changepoint detection for epidemic models.Scientific reports · 2025Article
- An agent-based model to assess the impact of shared staff and occupancy rate on infectious disease burden in nursing homes.BMC infectious diseases · 2025Article
- Utility of compartmental models to test the competing hypotheses of pathogen evolution and human intervention.Frontiers in public health · 2025Article
- Estimating the Relative Risks of Spatial Clusters Using a Predictor-Corrector Method.Mathematics (Basel, Switzerland) · 2025Article
- Dynamics of HIV transmission among men who have sex with men in Taiwan: a mathematical modeling study.BMC public health · 2024Article
- Accurate Estimation of Individual Transmission Rates Through Contact Analytics Using UWB Based Indoor Location Data.International Conference on Smart Applications, Communications and Networking : SmartNets. International Conference on Smart Applications, Communications and Networking · 2024Article
- Article
- STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information.Biomedical signal processing and control · 2023Article
- Update on COVID-19 and Effectiveness of a Vaccination Campaign in a Global Context.International journal of environmental research and public health · 2022Review
- Measuring the impact of social-distancing, testing, and undetected asymptomatic cases on the diffusion of COVID-19.PloS one · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection.
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