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Analysis of internet trends related to medications for COVID-19 in ten countries with the highest number of cases

Chire Saire, J. E.; Lemus-Martin, R.

2020-09-27 public and global health
10.1101/2020.09.08.20190785 medRxiv
Show abstract

During the last months, the pandemic generated by coronavirus SARS-CoV-2 moved governments, research groups and health organizations to plan, test and execute health policies. At the beginning, the treatment was not clear but in the next months, research groups have conducted studies to recommend or ban some medicines. At the same time, countries with most cases were changing from Asia, Europe to America. Different treatments and medications have been tested and recommended in many countries. such as Hydroxycloroquine, Ivermectin, Azithromycin, Dexamethasone, Prednisone, Desivir. This paper is a preliminary study about what people are searching on Internet, considering ten countries with most cases in the world such as Chile, Spain, United Kingdom, Brazil, United States, India, Russia, South Africa, Peru, Mexico.

Matching journals

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

1
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.1%
18.7%
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2
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Top 0.1%
12.0%
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3
PLOS ONE
● 5266 papers in training set
Top 16%
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Top 0.1%
7.3%
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5
Frontiers in Public Health
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Top 1.0%
4.9%
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50% of probability mass above
6
Cureus
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Top 1.0%
4.1%
7
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.1%
2.5%
8
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 2%
2.5%
9
Annals of Translational Medicine
○ 18 papers in training set
Top 0.2%
2.1%
10
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.4%
2.1%
11
PeerJ
◐ 308 papers in training set
Top 6%
1.4%
12
Scientific Reports
○ 3612 papers in training set
Top 59%
1.3%
13
DIGITAL HEALTH
○ 17 papers in training set
Top 0.5%
1.3%
14
Frontiers in Medicine
○ 120 papers in training set
Top 3%
1.1%
15
BMJ Open
● 601 papers in training set
Top 11%
1.1%
16
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○ 36 papers in training set
Top 0.6%
1.1%
17
Drug Safety
○ 10 papers in training set
Top 0.2%
1.0%
18
IEEE Access
● 35 papers in training set
Top 1%
1.0%
19
Database
◐ 61 papers in training set
Top 0.9%
0.9%
20
Heliyon
○ 152 papers in training set
Top 8%
0.9%
21
BMC Public Health
○ 158 papers in training set
Top 5%
0.9%
22
BMJ Health & Care Informatics
● 15 papers in training set
Top 1%
0.9%
23
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○ 10 papers in training set
Top 0.5%
0.9%
24
Virology
○ 61 papers in training set
Top 1%
0.9%
25
BioMed Research International
○ 28 papers in training set
Top 2%
0.9%
26
F1000Research
○ 88 papers in training set
Top 4%
0.6%
27
Journal of Public Health
◐ 24 papers in training set
Top 1%
0.6%
28
PLOS Digital Health
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Top 5%
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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.