ArticleComputers in biology and medicine2021
A novel compartmental model to capture the nonlinear trend of COVID-19.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- Susceptible-Infected-Susceptible type COVID-19 spread with collective effects.Scientific reports · 2023Article
- Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model.Annals of data science · 2023Article
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.Computers in biology and medicine · 2021Article
- Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling.Computers in biology and medicine · 2021Article
- Assessing the impact of vaccination in a COVID-19 compartmental model.Informatics in medicine unlocked · 2021Article
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Authors and funding
3 authors.
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Abstract
The COVID-19 pandemic took the world by surprise and surpassed the expectations of epidemiologists, governments, medical experts, and the scientific community as a whole. The majority of epidemiological models failed to capture the non-linear trend of the susceptible compartment and were unable to model this pandemic accurately. This study presents a variant of the well-known SEIRD model to account for social awareness measures, variable death rate, and the presence of asymptomatic infected individuals. The proposed SEAIRDQ model accounts for the transition of individuals between the susceptible and social awareness compartments. We tested our model against the reported cumulative infection and death data for different states in the US and observed over 98.8% accuracy. Results of this study give new insights into the prevailing reproduction number and herd immunity across the US.
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