Author: Vedant Chandra
Title: Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation Document date: 2020_4_1
ID: itviia7v_19
Snippet: Additionally, whilst parameter fits are poorly constrained in populations where the infection has not already peaked, it would be interesting to explore epidemic forecasting on those datasets. The Gillespie algorithm can be optimized to work faster with larger numbers of patients. Our parameterization of the SIR model can also be modified to include vital statistics like births and deaths. ABC generalizes well to these higher-dimensional paramete.....
Document: Additionally, whilst parameter fits are poorly constrained in populations where the infection has not already peaked, it would be interesting to explore epidemic forecasting on those datasets. The Gillespie algorithm can be optimized to work faster with larger numbers of patients. Our parameterization of the SIR model can also be modified to include vital statistics like births and deaths. ABC generalizes well to these higher-dimensional parameter spaces. Specific to SARS-CoV-2, age-structured models would also be a valuable development, as would models that include vaccinations and acquired immunity. . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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