Back

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

Morgenstern, C.; Rawson, T.; Routledge, I.; Kont, M.; Imai-Eaton, N.; Skarp, J. E.; Doohan, P.; McCain, K.; Johnson, R.; Unwin, H. J. T.; Naidoo, T.; Dee, D. P.; Parchani, K.; Cracknell Daniels, B. N.; Vicco, A.; Drake, K. O.; Christen, P.; Sheppard, R. J.; Leuba, S. I.; Hicks, J. T.; McCabe, R.; Nash, R. K.; Santoni, C. N.; Pathogen Epidemiology Review Group, ; Cuomo-Dannenburg, G.; van Elsland, S.; Bhatia, S.; Cori, A.

2024-08-14 epidemiology
10.1101/2024.08.13.24311934 medRxiv
Show 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 288 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. SARSs 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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS Medicine
● 110 papers in training set
Top 0.1%
14.8%
Similar papers in this journal
2
BMC Medicine
○ 176 papers in training set
Top 0.1%
11.7%
Similar papers in this journal
3
eLife
● 5828 papers in training set
Top 16%
7.1%
Similar papers in this journal
4
The Lancet Global Health
○ 27 papers in training set
Top 0.1%
5.1%
Similar papers in this journal
5
The Lancet Infectious Diseases
○ 73 papers in training set
Top 0.2%
4.8%
Similar papers in this journal
6
Science
● 477 papers in training set
Top 2%
4.0%
7
Eurosurveillance
● 83 papers in training set
Top 0.2%
3.4%
50% of probability mass above
8
Nature Communications
○ 5641 papers in training set
Top 36%
3.2%
9
PLOS ONE
● 5266 papers in training set
Top 39%
2.6%
10
Emerging Infectious Diseases
● 105 papers in training set
Top 0.5%
2.6%
11
Nature Human Behaviour
○ 95 papers in training set
Top 1.0%
2.1%
12
International Journal of Epidemiology
◐ 88 papers in training set
Top 0.6%
2.1%
13
BMJ Global Health
● 113 papers in training set
Top 2%
2.1%
14
International Journal of Infectious Diseases
○ 129 papers in training set
Top 1%
2.1%
15
PLOS Biology
● 486 papers in training set
Top 5%
1.7%
16
Epidemiology and Infection
◐ 89 papers in training set
Top 1%
1.7%
17
American Journal of Epidemiology
◐ 67 papers in training set
Top 0.8%
1.4%
18
BMJ Open
● 601 papers in training set
Top 10%
1.4%
19
The Lancet Microbe
○ 44 papers in training set
Top 0.5%
1.4%
20
Nature Medicine
○ 125 papers in training set
Top 2%
1.3%
21
European Journal of Epidemiology
○ 43 papers in training set
Top 0.6%
1.1%
22
Science Advances
● 1243 papers in training set
Top 26%
1.1%
23
Clinical Infectious Diseases
◐ 235 papers in training set
Top 2%
1.1%
24
eBioMedicine
○ 183 papers in training set
Top 5%
1.0%
25
Epidemics
○ 116 papers in training set
Top 1%
1.0%
26
PLOS Computational Biology
● 1863 papers in training set
Top 19%
1.0%
27
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 39%
0.9%
28
Frontiers in Public Health
○ 148 papers in training set
Top 6%
0.8%
29
Philosophical Transactions of the Royal Society B: Biological Sciences
● 72 papers in training set
Top 1%
0.8%
30
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.