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Forecast of the covid19 epidemic in France

Pottier, L.

2021-04-20 epidemiology
10.1101/2021.04.13.21255418 medRxiv
Show abstract

With a mathematical method based on linear algebra, from open access data (data.gouv.fr, google, apple) we produce forecasts for the number of patients in intensive care in France with an average error of 4% at 7 days, 7% at 14 days, 8% at 21 days, 10% at one month, 17% at 2 months, and 31% at 3 months. For the other epidemic indicators, the error is on average 6% at 7 days and 25% at 2 months.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 10%
18.5%
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2
PLOS Computational Biology
● 1863 papers in training set
Top 3%
11.9%
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3
Journal of The Royal Society Interface
● 235 papers in training set
Top 0.5%
6.7%
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4
Scientific Reports
○ 3612 papers in training set
Top 15%
5.5%
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5
Journal of Mathematical Biology
○ 40 papers in training set
Top 0.2%
3.2%
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6
Infectious Disease Modelling
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Top 0.5%
2.8%
7
Epidemiology and Infection
◐ 89 papers in training set
Top 0.8%
2.4%
50% of probability mass above
8
Mathematical Biosciences
○ 49 papers in training set
Top 0.4%
2.4%
9
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.4%
10
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
● 12 papers in training set
Top 0.1%
2.1%
11
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 23%
2.1%
12
eLife
● 5828 papers in training set
Top 45%
2.1%
13
Patterns
○ 78 papers in training set
Top 1%
2.0%
14
Journal of Theoretical Biology
○ 162 papers in training set
Top 1%
1.5%
15
PeerJ
◐ 308 papers in training set
Top 6%
1.5%
16
PNAS Nexus
◐ 159 papers in training set
Top 1%
1.4%
17
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.7%
1.1%
18
Journal of Computational Biology
○ 48 papers in training set
Top 0.8%
1.1%
19
Frontiers in Public Health
○ 148 papers in training set
Top 5%
1.1%
20
Epidemics
○ 116 papers in training set
Top 1%
1.0%
21
Nature Communications
○ 5641 papers in training set
Top 53%
1.0%
22
Royal Society Open Science
● 214 papers in training set
Top 6%
0.8%
23
Frontiers in Physics
○ 21 papers in training set
Top 0.3%
0.8%
24
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
0.8%
25
MethodsX
○ 16 papers in training set
Top 0.2%
0.8%
26
Physical Biology
○ 46 papers in training set
Top 0.9%
0.8%
27
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.5%
0.6%
28
Statistical Methods in Medical Research
○ 11 papers in training set
Top 0.2%
0.6%
29
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 1%
0.6%
30
IFAC-PapersOnLine
○ 13 papers in training set
Top 0.3%
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.