Back

Using different epidemiological models to modeling the epidemic dynamics in Brazil

de Alcantara, L. R. P.; Silva, L.; de Almeida, A. R.; Pitta, M. G. d. R.; Coutinho, A. P.

2020-05-02 public and global health
10.1101/2020.04.29.20085100 medRxiv
Show abstract

In this paper we provide forecasts of the cumulative number of confirmed reported cases in Brazil, specifically in Pernambuco and Ceara, by using the generalized logistic growth model, the Richards growth model and Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) phenomenological model. We rely on the Nash-Sutcliffe efficiency (NSE), root-mean-square error (RMSE) and mean absolute relative error (MARE) to quantify the quality of the models fits during the calibrationAll of these analyzes have been valid until the present date, April 14, 2020. The different models provide insights of our scenario predictions.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 6%
23.2%
Similar papers in this journal
2
Infectious Disease Modelling
○ 54 papers in training set
Top 0.1%
12.4%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 9%
7.0%
Similar papers in this journal
4
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.2%
Similar papers in this journal
5
Applied Sciences
○ 25 papers in training set
Top 0.1%
3.3%
Similar papers in this journal
50% of probability mass above
6
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.3%
7
Infectious Diseases of Poverty
○ 11 papers in training set
Top 0.1%
2.7%
8
PLOS Global Public Health
● 344 papers in training set
Top 5%
2.5%
9
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.3%
2.5%
10
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 0.6%
2.5%
11
PeerJ
◐ 308 papers in training set
Top 5%
1.8%
12
PLOS Neglected Tropical Diseases
● 466 papers in training set
Top 4%
1.6%
13
Cureus
○ 68 papers in training set
Top 3%
1.5%
14
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.6%
1.5%
15
Epidemics
○ 116 papers in training set
Top 1%
1.4%
16
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 4%
1.2%
17
BMC Infectious Diseases
○ 133 papers in training set
Top 3%
1.2%
18
JMIRx Med
◐ 32 papers in training set
Top 2%
1.1%
19
Biology
○ 45 papers in training set
Top 0.5%
1.0%
20
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
0.9%
21
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.8%
0.9%
22
Epidemiology and Infection
◐ 89 papers in training set
Top 2%
0.9%
23
BMC Public Health
○ 158 papers in training set
Top 5%
0.9%
24
Biology Methods and Protocols
◐ 61 papers in training set
Top 2%
0.9%
25
Nature Communications
○ 5641 papers in training set
Top 58%
0.6%
26
Transactions of The Royal Society of Tropical Medicine and Hygiene
◐ 18 papers in training set
Top 0.6%
0.6%
27
Acta Tropica
○ 13 papers in training set
Top 0.6%
0.6%
28
Royal Society Open Science
● 214 papers in training set
Top 7%
0.6%
29
Frontiers in Physiology
○ 106 papers in training set
Top 4%
0.5%
30
Expert Systems with Applications
○ 11 papers in training set
Top 0.6%
0.5%

"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.