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A simple mathematical model for Coronavirus (COVID-19)

El Allaoui, A.; Melliani, S.; Chadli, L. S.

2020-04-28 epidemiology
10.1101/2020.04.23.20076919 medRxiv
Show abstract

A novel coronavirus (COVID-19) was identified in Wuhan, China in the end of 2019, it causing an outbreak of viral pneumonia. It caused to the death rate of 4.63% among 571, 678 confirmed cases around the world to the March 28th, 2020. In this brief currentstudy, we will present a simple mathematical model where we show how the probability of successfully getting infected when coming into contact with an infected individual and the per-capita contact rate affect the healthy and infected population with time. The proposed model is used to offer predictions about the behavior of COVID-19 for a shorter period of time.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
PLOS ONE
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Top 12%
15.3%
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2
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Top 0.1%
8.0%
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3
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Top 9%
6.8%
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4
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● 23 papers in training set
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6.8%
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5
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6
Journal of Theoretical Biology
○ 162 papers in training set
Top 0.8%
3.6%
7
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.2%
3.3%
8
Frontiers in Public Health
○ 148 papers in training set
Top 2%
3.3%
50% of probability mass above
9
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 2%
2.5%
10
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
2.4%
11
PLOS Computational Biology
● 1863 papers in training set
Top 12%
2.4%
12
The European Physical Journal Plus
○ 13 papers in training set
Top 0.1%
2.4%
13
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 0.7%
1.9%
14
BMC Infectious Diseases
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Top 2%
1.9%
15
Epidemics
○ 116 papers in training set
Top 0.9%
1.9%
16
Mathematics
○ 11 papers in training set
Top 0.1%
1.5%
17
Royal Society Open Science
● 214 papers in training set
Top 4%
1.5%
18
Mathematical Biosciences
○ 49 papers in training set
Top 0.7%
1.5%
19
Journal of Clinical Medicine
○ 97 papers in training set
Top 3%
1.1%
20
Applied Sciences
○ 25 papers in training set
Top 0.5%
1.1%
21
JMIRx Med
◐ 32 papers in training set
Top 1%
1.1%
22
Quantitative Biology
○ 12 papers in training set
Top 0.1%
1.1%
23
PeerJ
◐ 308 papers in training set
Top 10%
0.9%
24
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 2%
0.9%
25
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.3%
0.9%
26
Physical Biology
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Top 0.9%
0.9%
27
Epidemiology and Infection
◐ 89 papers in training set
Top 2%
0.9%
28
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.3%
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29
Biology
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Top 1%
0.6%
30
Frontiers in Physics
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Top 0.4%
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.