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Covid19data.website

malouche, d.

2020-08-20 epidemiology
10.1101/2020.08.14.20174995 medRxiv
Show abstract

Covid19data.website Project is a website that contains more than 250 Dashboards about the COVID-19 Toll areas affected by the virus. You can follow-up every day using this website the number of COVID-19 cases, deaths, active cases, the shares by 1 Million population, the mortality rate, the active rate in every country, continent, and territory and all States in the United States. Unlike the other websites, you can also follow-up an estimation of the reproduction number in the previous sixteen days. These statistics tell you how many secondary infections are likely to occur in a specific area. Furthermore, I provide a classification algorithm of the countries and the affected areas. Its based on the observation during the previous 14 days of six criteria. A global score is then computed, allowing to evaluate the COVID-19 safeness toll of each area.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 10%
18.6%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 10%
6.8%
Similar papers in this journal
3
Frontiers in Public Health
○ 148 papers in training set
Top 0.6%
6.3%
Similar papers in this journal
4
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 1%
4.3%
Similar papers in this journal
5
Viruses
○ 332 papers in training set
Top 1%
4.1%
Similar papers in this journal
6
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.8%
3.2%
7
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.2%
8
Scientific Data
○ 209 papers in training set
Top 0.9%
2.7%
9
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.3%
2.4%
50% of probability mass above
10
Patterns
○ 78 papers in training set
Top 1.0%
2.1%
11
JMIRx Med
◐ 32 papers in training set
Top 0.7%
2.1%
12
PeerJ
◐ 308 papers in training set
Top 5%
1.9%
13
Biology
○ 45 papers in training set
Top 0.2%
1.4%
14
Heliyon
○ 152 papers in training set
Top 4%
1.3%
15
BMJ Open
● 601 papers in training set
Top 11%
1.1%
16
BMC Public Health
○ 158 papers in training set
Top 4%
1.1%
17
Informatics in Medicine Unlocked
○ 22 papers in training set
Top 0.8%
1.1%
18
Frontiers in Immunology
○ 638 papers in training set
Top 9%
0.9%
19
Vaccines
○ 198 papers in training set
Top 3%
0.9%
20
Entropy
○ 21 papers in training set
Top 0.3%
0.9%
21
Journal of Translational Medicine
○ 57 papers in training set
Top 2%
0.8%
22
IEEE Access
● 35 papers in training set
Top 1%
0.8%
23
iMeta
○ 10 papers in training set
Top 0.2%
0.8%
24
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.9%
0.8%
25
COVID
○ 14 papers in training set
Top 0.4%
0.8%
26
Mathematical Biosciences
○ 49 papers in training set
Top 1%
0.8%
27
Quantitative Biology
○ 12 papers in training set
Top 0.2%
0.8%
28
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 41%
0.8%
29
Nature Communications
○ 5641 papers in training set
Top 56%
0.8%
30
PLOS Digital Health
● 106 papers in training set
Top 5%
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.