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COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms

Punn, N. S.; Sonbhadra, S. K.; Agarwal, S.

2020-04-11 health informatics
10.1101/2020.04.08.20057679 medRxiv
Show abstract

The catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 15%
12.4%
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2
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.1%
6.6%
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3
Informatics in Medicine Unlocked
○ 22 papers in training set
Top 0.1%
6.6%
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4
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.1%
6.1%
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5
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
5.4%
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6
Expert Systems with Applications
○ 11 papers in training set
Top 0.1%
5.4%
7
Scientific Reports
○ 3612 papers in training set
Top 20%
4.7%
8
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.2%
50% of probability mass above
9
Mathematics
○ 11 papers in training set
Top 0.1%
4.0%
10
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.7%
3.4%
11
Computers in Biology and Medicine
○ 128 papers in training set
Top 2%
2.6%
12
JMIRx Med
◐ 32 papers in training set
Top 0.5%
2.6%
13
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.2%
2.4%
14
International Journal of Medical Informatics
○ 26 papers in training set
Top 0.5%
2.3%
15
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.5%
2.1%
16
Life
○ 29 papers in training set
Top 0.2%
1.9%
17
Patterns
○ 78 papers in training set
Top 1%
1.7%
18
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 0.7%
1.6%
19
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.5%
20
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 1%
1.4%
21
Infectious Disease Modelling
○ 54 papers in training set
Top 0.9%
1.3%
22
BioMed Research International
○ 28 papers in training set
Top 1%
1.3%
23
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 4%
1.1%
24
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.6%
1.0%
25
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.6%
1.0%
26
Biology Methods and Protocols
◐ 61 papers in training set
Top 2%
0.8%
27
PLOS Digital Health
● 106 papers in training set
Top 5%
0.8%
28
IEEE Access
● 35 papers in training set
Top 2%
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.