{"database":"biostudies-other","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10"],"submitter":["Lucian Smith"],"journal":["Scientific reports"],"pagination":["21721"],"species":["Severe acute respiratory syndrome coronavirus 2"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/MODEL2012240001"],"repository":["biostudies-other"],"additional_accession":["33303925"],"pubmed_authors":["Lucian Smith","Kausthubh Ramachandran"]},"is_claimable":false,"name":"Law2020 - SIR model of COVID-19 transmission in Malyasia with time-varying parameters","description":"The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.","dates":{"release":"2020-12-24T00:00:00Z","modification":"2025-07-15T09:45:39.977Z","creation":"2025-03-29T22:22:55.917Z"},"accession":"MODEL2012240001","cross_references":{"biomodels___db":["BIOMD0000000982"],"pubmed":["33303925"],"ncit":["C128320","NCIT:C16814","NCIT:C171133","C25746"],"mamo":["MAMO_0000028"],"ido":["0000514","0000511","0000621"],"doid":["DOID:0080600"],"taxonomy":["9606","2697049"]}}