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Covid19 Surveillance in Peru on April using Text Mining

Chire Saire, J. E.; Oblitas, J.

2020-05-25 public and global health
10.1101/2020.05.24.20112193 medRxiv
Show abstract

The present outbreak as consequence by coronavirus covid19 has generated an big impact over the world. South American countries had their own limitations, challengues and pandemic has highlighted what needs to improve. Peru is a country with good start with quarantine, social distancing policies but the policies was not enough during the weeks. So, the analysis over April is performed through infoveillance using posts from different cities to analyze what population was living or worried during this month. Results presents a high concern about international context, and national situation, besides Economy and Politics are issues to solve. By constrast, Religion and Transport are not very important for peruvian citizens.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 4%
31.3%
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2
Frontiers in Public Health
○ 148 papers in training set
Top 0.3%
7.9%
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3
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.1%
4.9%
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4
JMIRx Med
◐ 32 papers in training set
Top 0.2%
4.1%
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5
Epidemiology and Infection
◐ 89 papers in training set
Top 0.4%
3.5%
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50% of probability mass above
6
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 2%
3.2%
7
Scientific Reports
○ 3612 papers in training set
Top 33%
3.2%
8
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.5%
1.9%
9
Cureus
○ 68 papers in training set
Top 2%
1.7%
10
PeerJ
◐ 308 papers in training set
Top 5%
1.7%
11
PLOS Global Public Health
● 344 papers in training set
Top 6%
1.5%
12
Heliyon
○ 152 papers in training set
Top 4%
1.5%
13
Public Health
○ 36 papers in training set
Top 0.3%
1.4%
14
Sustainability
○ 10 papers in training set
Top 0.3%
1.1%
15
Biology
○ 45 papers in training set
Top 0.4%
1.1%
16
Vaccines
○ 198 papers in training set
Top 3%
1.1%
17
Journal of Medical Internet Research
◐ 87 papers in training set
Top 2%
1.1%
18
Journal of Public Health
◐ 24 papers in training set
Top 0.8%
1.1%
19
Virology Journal
○ 32 papers in training set
Top 0.6%
1.1%
20
BMC Infectious Diseases
○ 133 papers in training set
Top 3%
1.0%
21
F1000Research
○ 88 papers in training set
Top 3%
1.0%
22
PLOS Neglected Tropical Diseases
● 466 papers in training set
Top 5%
0.9%
23
Frontiers in Psychology
○ 56 papers in training set
Top 1%
0.9%
24
Global Health Action
○ 10 papers in training set
Top 0.4%
0.9%
25
The American Journal of Tropical Medicine and Hygiene
● 68 papers in training set
Top 2%
0.9%
26
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.8%
0.6%
27
Frontiers in Medicine
○ 120 papers in training set
Top 5%
0.6%
28
BMC Public Health
○ 158 papers in training set
Top 6%
0.6%
29
Risk Management and Healthcare Policy
○ 10 papers in training set
Top 0.6%
0.6%
30
Pan African Medical Journal
● 11 papers in training set
Top 0.6%
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.