Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model

Abstract 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 d...

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Autores principales:	Kian Boon Law, Kalaiarasu M. Peariasamy, Balvinder Singh Gill, Sarbhan Singh, Bala Murali Sundram, Kamesh Rajendran, Sarat Chandra Dass, Yi Lin Lee, Pik Pin Goh, Hishamshah Ibrahim, Noor Hisham Abdullah
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7e4b66cf9c174a618a17437128f93686
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spelling	oai:doaj.org-article:7e4b66cf9c174a618a17437128f936862021-12-02T15:11:51ZTracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model10.1038/s41598-020-78739-82045-2322https://doaj.org/article/7e4b66cf9c174a618a17437128f936862020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78739-8https://doaj.org/toc/2045-2322Abstract 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, $$\beta_{t}$$ β t 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.Kian Boon LawKalaiarasu M. PeariasamyBalvinder Singh GillSarbhan SinghBala Murali SundramKamesh RajendranSarat Chandra DassYi Lin LeePik Pin GohHishamshah IbrahimNoor Hisham AbdullahNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kian Boon Law Kalaiarasu M. Peariasamy Balvinder Singh Gill Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Sarat Chandra Dass Yi Lin Lee Pik Pin Goh Hishamshah Ibrahim Noor Hisham Abdullah Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
description	Abstract 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, $$\beta_{t}$$ β t 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.
format	article
author	Kian Boon Law Kalaiarasu M. Peariasamy Balvinder Singh Gill Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Sarat Chandra Dass Yi Lin Lee Pik Pin Goh Hishamshah Ibrahim Noor Hisham Abdullah
author_facet	Kian Boon Law Kalaiarasu M. Peariasamy Balvinder Singh Gill Sarbhan Singh Bala Murali Sundram Kamesh Rajendran Sarat Chandra Dass Yi Lin Lee Pik Pin Goh Hishamshah Ibrahim Noor Hisham Abdullah
author_sort	Kian Boon Law
title	Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
title_short	Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
title_full	Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
title_fullStr	Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
title_full_unstemmed	Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
title_sort	tracking the early depleting transmission dynamics of covid-19 with a time-varying sir model
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7e4b66cf9c174a618a17437128f93686
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Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model

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