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Monitoring COVID-19 progression: Look at Us Today, See Yourself Tomorrow

Noh, J.

2020-04-23 health systems and quality improvement
10.1101/2020.04.20.20072991 medRxiv
Show abstract

The coronavirus disease 2019 (COVID-19) pandemic is causing public health emergency and economic crisis all over the globe. Being widely spread, the virus can make any place in the world a new epicenter of the possible second wave of outbreaks. To control the pandemic progression, monitoring of the virus spreading is imperative. This paper proposes a simple and robust approach to monitor the COVID-19 pandemic progression in many countries or regions. This data science pipeline can provide actionable insights via straightforward COVID-19 data visualization for many regions at a glance, which informs of relative time delay of the pandemic progression, projected numbers of confirmed cases in the near future, and the sizes of infections.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 5%
29.2%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 9%
6.9%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 26%
5.7%
Similar papers in this journal
4
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.4%
Similar papers in this journal
5
PLOS Computational Biology
● 1863 papers in training set
Top 8%
4.1%
Similar papers in this journal
50% of probability mass above
6
Heliyon
○ 152 papers in training set
Top 1%
3.3%
7
Scientific Data
○ 209 papers in training set
Top 0.7%
3.3%
8
Patterns
○ 78 papers in training set
Top 0.5%
3.3%
9
GigaScience
◐ 212 papers in training set
Top 1%
2.9%
10
IEEE Access
● 35 papers in training set
Top 0.4%
2.5%
11
BMC Medical Research Methodology
○ 47 papers in training set
Top 0.8%
1.4%
12
Nature
○ 645 papers in training set
Top 8%
1.4%
13
International Journal of Infectious Diseases
○ 129 papers in training set
Top 2%
1.2%
14
JMIR Medical Informatics
◐ 18 papers in training set
Top 0.6%
1.2%
15
Royal Society Open Science
● 214 papers in training set
Top 4%
1.2%
16
Frontiers in Digital Health
○ 24 papers in training set
Top 0.8%
1.2%
17
Journal of Medical Internet Research
◐ 87 papers in training set
Top 2%
1.1%
18
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 1%
1.1%
19
JMIRx Med
◐ 32 papers in training set
Top 2%
1.1%
20
Sensors
○ 43 papers in training set
Top 1%
1.1%
21
iMeta
○ 10 papers in training set
Top 0.1%
1.1%
22
PLOS Global Public Health
● 344 papers in training set
Top 7%
1.1%
23
Journal of Medical Virology
○ 140 papers in training set
Top 2%
1.0%
24
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 5%
1.0%
25
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 2%
0.9%
26
BMC Public Health
○ 158 papers in training set
Top 5%
0.6%
27
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 3%
0.6%
28
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
● 15 papers in training set
Top 0.3%
0.6%
29
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.9%
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.