Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review

Christian Morgenstern; Thomas Rawson; Isobel Routledge; Mara D. Kont; Natsuko Imai-Eaton; Janetta Skarp; Patrick Doohan; Kelly McCain; Robert Johnson; H Juliette T Unwin; Tristan Naidoo; Dominic Dee; Kanchan Parchani; Bethan Cracknell Daniels; Anna Vicco; Kieran Drake; Paula Christen; Richard Sheppard; Sequoia Leuba; Joseph T. Hicks; Ruth McCabe; Rebecca K. Nash; Cosmo Santoni; Gina Cuomo-Dannenburg; Sabine L. van Elsland; Sangeeta Bhatia; Anne Cori

doi:10.1016/j.lanmic.2025.101144

Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review

Christian Morgenstern^*
, Thomas Rawson
, Isobel Routledge
, Mara D. Kont
, Natsuko Imai-Eaton
, Janetta Skarp
, Patrick Doohan
, Kelly McCain
, Robert Johnson
, H Juliette T Unwin
, Tristan Naidoo
, Dominic Dee
, Kanchan Parchani
, Bethan Cracknell Daniels
, Anna Vicco
, Kieran Drake
, Paula Christen
, Richard Sheppard
, Sequoia Leuba
, Joseph T. Hicks

Ruth McCabe, Rebecca K. Nash, Cosmo Santoni, Gina Cuomo-Dannenburg, Sabine L. van Elsland, Sangeeta Bhatia, Anne Cori

^*Corresponding author for this work

Research output: Contribution to journal › Review article (Academic Journal) › peer-review

7 Citations (Scopus)

Abstract

We conducted a systematic review (PROSPERO CRD42023393345) of severe acute respiratory syndrome (SARS) transmission models and parameters characterising its transmission, evolution, natural history, severity, risk factors and seroprevalence. Information was extracted using a custom database and quality assessment tool.

We extracted 519 parameters, 243 risk factors, and 112 models from 289 papers. Our analyses show SARS is characterised by high lethality (case fatality ratio 10.9%), transmissibility (R0 range 1.1-4.59), and is prone to superspreading (20% top infectors causing up to 91% of infections). Infection risk was highest among healthcare workers and close contacts of infected individuals. Severe disease and death were associated with age and existing comorbidities. SARS's natural history is poorly characterised, except for the incubation period and mean onset-to-hospitalisation.

Our associated R package, epireview, contains this database, which can continue to be updated to maintain a living review of SARS epidemiology and models, thus providing a key resource for informing response to future coronavirus outbreaks.

Original language	English
Article number	101144
Number of pages	12
Journal	The Lancet Microbe
Volume	6
Issue number	9
Early online date	23 Jul 2025
DOIs	https://doi.org/10.1016/j.lanmic.2025.101144
Publication status	E-pub ahead of print - 23 Jul 2025

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© 2025 The Author(s)

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Morgenstern, C., Rawson, T., Routledge, I., Kont, M. D., Imai-Eaton, N., Skarp, J., Doohan, P., McCain, K., Johnson, R., Unwin, H. J. T., Naidoo, T., Dee, D., Parchani, K., Cracknell Daniels, B., Vicco, A., Drake, K., Christen, P., Sheppard, R., Leuba, S., ... Cori, A. (2025). Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review. The Lancet Microbe, 6(9), Article 101144. Advance online publication. https://doi.org/10.1016/j.lanmic.2025.101144

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title = "Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review",

abstract = "We conducted a systematic review (PROSPERO CRD42023393345) of severe acute respiratory syndrome (SARS) transmission models and parameters characterising its transmission, evolution, natural history, severity, risk factors and seroprevalence. Information was extracted using a custom database and quality assessment tool. We extracted 519 parameters, 243 risk factors, and 112 models from 289 papers. Our analyses show SARS is characterised by high lethality (case fatality ratio 10.9\%), transmissibility (R0 range 1.1-4.59), and is prone to superspreading (20\% top infectors causing up to 91\% of infections). Infection risk was highest among healthcare workers and close contacts of infected individuals. Severe disease and death were associated with age and existing comorbidities. SARS's natural history is poorly characterised, except for the incubation period and mean onset-to-hospitalisation. Our associated R package, epireview, contains this database, which can continue to be updated to maintain a living review of SARS epidemiology and models, thus providing a key resource for informing response to future coronavirus outbreaks. ",

author = "Christian Morgenstern and Thomas Rawson and Isobel Routledge and Kont, \{Mara D.\} and Natsuko Imai-Eaton and Janetta Skarp and Patrick Doohan and Kelly McCain and Robert Johnson and Unwin, \{H Juliette T\} and Tristan Naidoo and Dominic Dee and Kanchan Parchani and \{Cracknell Daniels\}, Bethan and Anna Vicco and Kieran Drake and Paula Christen and Richard Sheppard and Sequoia Leuba and Hicks, \{Joseph T.\} and Ruth McCabe and Nash, \{Rebecca K.\} and Cosmo Santoni and Gina Cuomo-Dannenburg and Elsland, \{Sabine L. van\} and Sangeeta Bhatia and Anne Cori",

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doi = "10.1016/j.lanmic.2025.101144",

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Morgenstern, C, Rawson, T, Routledge, I, Kont, MD, Imai-Eaton, N, Skarp, J, Doohan, P, McCain, K, Johnson, R, Unwin, HJT, Naidoo, T, Dee, D, Parchani, K, Cracknell Daniels, B, Vicco, A, Drake, K, Christen, P, Sheppard, R, Leuba, S, Hicks, JT, McCabe, R, Nash, RK, Santoni, C, Cuomo-Dannenburg, G, Elsland, SLV, Bhatia, S & Cori, A 2025, 'Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review', The Lancet Microbe, vol. 6, no. 9, 101144. https://doi.org/10.1016/j.lanmic.2025.101144

TY - JOUR

T1 - Severe acute respiratory syndrome (SARS) mathematical models and disease parameters

T2 - a systematic review

AU - Morgenstern, Christian

AU - Rawson, Thomas

AU - Routledge, Isobel

AU - Kont, Mara D.

AU - Imai-Eaton, Natsuko

AU - Skarp, Janetta

AU - Doohan, Patrick

AU - McCain, Kelly

AU - Johnson, Robert

AU - Unwin, H Juliette T

AU - Naidoo, Tristan

AU - Dee, Dominic

AU - Parchani, Kanchan

AU - Cracknell Daniels, Bethan

AU - Vicco, Anna

AU - Drake, Kieran

AU - Christen, Paula

AU - Sheppard, Richard

AU - Leuba, Sequoia

AU - Hicks, Joseph T.

AU - McCabe, Ruth

AU - Nash, Rebecca K.

AU - Santoni, Cosmo

AU - Cuomo-Dannenburg, Gina

AU - Elsland, Sabine L. van

AU - Bhatia, Sangeeta

AU - Cori, Anne

PY - 2025/7/23

Y1 - 2025/7/23

N2 - We conducted a systematic review (PROSPERO CRD42023393345) of severe acute respiratory syndrome (SARS) transmission models and parameters characterising its transmission, evolution, natural history, severity, risk factors and seroprevalence. Information was extracted using a custom database and quality assessment tool. We extracted 519 parameters, 243 risk factors, and 112 models from 289 papers. Our analyses show SARS is characterised by high lethality (case fatality ratio 10.9%), transmissibility (R0 range 1.1-4.59), and is prone to superspreading (20% top infectors causing up to 91% of infections). Infection risk was highest among healthcare workers and close contacts of infected individuals. Severe disease and death were associated with age and existing comorbidities. SARS's natural history is poorly characterised, except for the incubation period and mean onset-to-hospitalisation. Our associated R package, epireview, contains this database, which can continue to be updated to maintain a living review of SARS epidemiology and models, thus providing a key resource for informing response to future coronavirus outbreaks.

AB - We conducted a systematic review (PROSPERO CRD42023393345) of severe acute respiratory syndrome (SARS) transmission models and parameters characterising its transmission, evolution, natural history, severity, risk factors and seroprevalence. Information was extracted using a custom database and quality assessment tool. We extracted 519 parameters, 243 risk factors, and 112 models from 289 papers. Our analyses show SARS is characterised by high lethality (case fatality ratio 10.9%), transmissibility (R0 range 1.1-4.59), and is prone to superspreading (20% top infectors causing up to 91% of infections). Infection risk was highest among healthcare workers and close contacts of infected individuals. Severe disease and death were associated with age and existing comorbidities. SARS's natural history is poorly characterised, except for the incubation period and mean onset-to-hospitalisation. Our associated R package, epireview, contains this database, which can continue to be updated to maintain a living review of SARS epidemiology and models, thus providing a key resource for informing response to future coronavirus outbreaks.

U2 - 10.1016/j.lanmic.2025.101144

DO - 10.1016/j.lanmic.2025.101144

M3 - Review article (Academic Journal)

C2 - 40713974

SN - 2666-5247

VL - 6

JO - The Lancet Microbe

JF - The Lancet Microbe

IS - 9

M1 - 101144

ER -

Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review

Abstract

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