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Predicting the end-stage of the COVID-19 epidemic in Brazil

Webb, G. F.; Fitzgibbon, W. E.; Morgan, J. J.; Wu, Y.

2020-05-29 epidemiology
10.1101/2020.05.28.20116103 medRxiv
Show abstract

We develop a dynamic model of a COVID-19 epidemic as a system of differential equations. The model incorporates an asymptomatic infectious stage and a symptomatic infectious stage. We apply the model to the current COVID-19 epidemic in Brazil. We compare the model output to current epidemic data, and project forward in time possible end-stages of the epidemic in Brazil. The model emphasizes the importance of reducing asymptomatic infections in controlling the epidemic.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
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○ 54 papers in training set
Top 0.1%
15.2%
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2
PLOS ONE
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Top 16%
11.1%
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3
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○ 92 papers in training set
Top 0.2%
7.9%
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4
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○ 3612 papers in training set
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6.3%
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5
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Top 7%
5.2%
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6
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
4.9%
50% of probability mass above
7
Mathematical Biosciences
○ 49 papers in training set
Top 0.2%
4.3%
8
Journal of Theoretical Biology
○ 162 papers in training set
Top 1%
2.8%
9
Biomechanics and Modeling in Mechanobiology
○ 29 papers in training set
Top 0.2%
2.7%
10
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
2.7%
11
Journal of Mathematical Biology
○ 40 papers in training set
Top 0.2%
2.4%
12
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.4%
2.1%
13
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● 214 papers in training set
Top 3%
1.9%
14
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○ 10 papers in training set
Top 0.2%
1.7%
15
Computer Methods in Biomechanics and Biomedical Engineering
○ 10 papers in training set
Top 0.1%
1.7%
16
Epidemics
○ 116 papers in training set
Top 1%
1.7%
17
Journal of The Royal Society Interface
● 235 papers in training set
Top 3%
1.3%
18
The European Physical Journal Plus
○ 13 papers in training set
Top 0.2%
1.1%
19
Epidemiology and Infection
◐ 89 papers in training set
Top 2%
1.1%
20
Applied Sciences
○ 25 papers in training set
Top 0.6%
1.1%
21
Mathematics
○ 11 papers in training set
Top 0.3%
0.8%
22
Physical Biology
○ 46 papers in training set
Top 0.9%
0.8%
23
Mathematical Medicine and Biology: A Journal of the IMA
◐ 10 papers in training set
Top 0.2%
0.8%
24
Frontiers in Public Health
○ 148 papers in training set
Top 6%
0.8%
25
PeerJ
◐ 308 papers in training set
Top 11%
0.8%
26
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.3%
0.8%
27
BMC Infectious Diseases
○ 133 papers in training set
Top 5%
0.6%
28
Journal of Clinical Medicine
○ 97 papers in training set
Top 5%
0.6%

"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.