Back

A dynamic model for Covid-19 in Brazil.

Lichtenthaler Filho, R.; Lichtenthaler, D. G.

2020-05-14 epidemiology
10.1101/2020.05.10.20097550 medRxiv
Show abstract

1A dynamic model for the current coronavirus outbreak is presented. The most important parameters are identified which determine the number of cases progression. Results of a numerical simulation are compared with existing data of the number of COVID-19 cases and deaths in Sao Paulo and Brazil. On the basis of these results measures are proposed to control the epidemics and to flat the infection curve. A simple three steps procedure is proposed to predict the time evolution of the epidemics and a criteria to start resuming normal activities after quarantine is suggested.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 4%
31.8%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 9%
6.9%
Similar papers in this journal
3
Infectious Disease Modelling
○ 54 papers in training set
Top 0.2%
6.4%
Similar papers in this journal
4
Journal of Clinical Medicine
○ 97 papers in training set
Top 0.7%
4.1%
Similar papers in this journal
5
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 1%
4.1%
Similar papers in this journal
50% of probability mass above
6
The European Physical Journal Plus
○ 13 papers in training set
Top 0.1%
3.3%
7
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.3%
2.8%
8
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.2%
2.2%
9
Epidemiology and Infection
◐ 89 papers in training set
Top 0.9%
2.0%
10
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.5%
1.8%
11
Biology
○ 45 papers in training set
Top 0.2%
1.5%
12
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.4%
1.2%
13
PeerJ
◐ 308 papers in training set
Top 7%
1.2%
14
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.2%
15
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
1.2%
16
Frontiers in Public Health
○ 148 papers in training set
Top 5%
1.2%
17
Nature Communications
○ 5641 papers in training set
Top 52%
1.1%
18
Cureus
○ 68 papers in training set
Top 4%
1.1%
19
JMIRx Med
◐ 32 papers in training set
Top 2%
1.0%
20
Frontiers in Physics
○ 21 papers in training set
Top 0.2%
1.0%
21
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
0.9%
22
Applied Sciences
○ 25 papers in training set
Top 0.8%
0.9%
23
Mathematical Biosciences
○ 49 papers in training set
Top 1%
0.9%
24
Nonlinear Dynamics
○ 10 papers in training set
Top 0.3%
0.9%
25
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.7%
0.6%
26
Mathematics
○ 11 papers in training set
Top 0.3%
0.6%
27
Viruses
○ 332 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.