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A Hybrid Knowledge-Based And Modified Regression Analysis Approach For COVID-19 Tracking In USA

Hussein, R.

2020-07-29 public and global health
10.1101/2020.07.26.20162347 medRxiv
Show abstract

Since its appearance in 2019, the covid-19 virus deluged the world with unprecedented data in short time. Despite the countless worldwide pertinent studies and advanced technologies, the spread is neither contained nor defeated. In fact, there is a record surge in the number of confirmed new cases since July 2020. This article presents a new predictive Knowledge-based (KB) toolkit named CORVITT (Corona Virus Tracking Toolkit) and modified linear regression model. This hybrid approach uses the confirmed new cases and demographic data, implemented. CORVITT is not an epidemiological model, in the sense that it does not model disease transmission, nor does it use underlying epidemiological parameters like the reproductive rate. It forecasts the spread in order to assist the official to make proactive intervention.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 5%
27.3%
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2
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Top 0.1%
15.5%
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3
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◐ 45 papers in training set
Top 0.1%
5.3%
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4
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Top 27%
3.5%
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50% of probability mass above
5
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Top 0.5%
2.5%
6
IEEE Access
● 35 papers in training set
Top 0.4%
2.5%
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7
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Top 0.6%
2.5%
8
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○ 11 papers in training set
Top 0.1%
2.0%
9
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.3%
1.8%
10
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◐ 61 papers in training set
Top 0.7%
1.8%
11
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○ 43 papers in training set
Top 1.0%
1.7%
12
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● 1863 papers in training set
Top 15%
1.5%
13
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◐ 87 papers in training set
Top 2%
1.4%
14
Biology
○ 45 papers in training set
Top 0.2%
1.4%
15
Epidemics
○ 116 papers in training set
Top 1%
1.2%
16
Journal of Translational Medicine
○ 57 papers in training set
Top 1%
1.2%
17
Virology Journal
○ 32 papers in training set
Top 0.5%
1.2%
18
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 4%
1.2%
19
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.5%
1.1%
20
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● 23 papers in training set
Top 0.7%
1.1%
21
Heliyon
○ 152 papers in training set
Top 7%
1.0%
22
Cureus
○ 68 papers in training set
Top 4%
1.0%
23
Frontiers in Digital Health
○ 24 papers in training set
Top 1%
0.9%
24
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○ 22 papers in training set
Top 1%
0.9%
25
Frontiers in Physiology
○ 106 papers in training set
Top 2%
0.9%
26
Applied Sciences
○ 25 papers in training set
Top 1.0%
0.6%
27
PeerJ
◐ 308 papers in training set
Top 12%
0.6%
28
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○ 120 papers in training set
Top 4%
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29
BMC Infectious Diseases
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Top 4%
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"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.