tetano
Editor, Senior Moderator
Paediatr Respir Rev
. 2020 Jun 20;S1526-0542(20)30099-3.
doi: 10.1016/j.prrv.2020.06.014. Online ahead of print.
Modelling insights into the COVID-19 pandemic
Michael T Meehan[SUP] 1 [/SUP], Diana P Rojas[SUP] 2 [/SUP], Adeshina I Adekunle[SUP] 1 [/SUP], Oyelola A Adegboye[SUP] 1 [/SUP], Jamie M Caldwell[SUP] 3 [/SUP], Evelyn Turek[SUP] 4 [/SUP], Bridget M Williams[SUP] 4 [/SUP], Ben J Marais[SUP] 5 [/SUP], James M Trauer[SUP] 4 [/SUP], Emma S McBryde[SUP] 6 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R[SUB]0[/SUB] (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.
Keywords: COVID-19; Emerging infectious diseases; Mathematical modelling; Pandemic; Review.
. 2020 Jun 20;S1526-0542(20)30099-3.
doi: 10.1016/j.prrv.2020.06.014. Online ahead of print.
Modelling insights into the COVID-19 pandemic
Michael T Meehan[SUP] 1 [/SUP], Diana P Rojas[SUP] 2 [/SUP], Adeshina I Adekunle[SUP] 1 [/SUP], Oyelola A Adegboye[SUP] 1 [/SUP], Jamie M Caldwell[SUP] 3 [/SUP], Evelyn Turek[SUP] 4 [/SUP], Bridget M Williams[SUP] 4 [/SUP], Ben J Marais[SUP] 5 [/SUP], James M Trauer[SUP] 4 [/SUP], Emma S McBryde[SUP] 6 [/SUP]
Affiliations
- PMID: 32680824
- DOI: 10.1016/j.prrv.2020.06.014
Abstract
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R[SUB]0[/SUB] (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.
Keywords: COVID-19; Emerging infectious diseases; Mathematical modelling; Pandemic; Review.