Back

Predictive Modelling of COVID-19 New Cases in Algeria using An Extreme Learning Machines (ELM)

Djeddou, M.; A. Hameed, I.; Nejatian, A.; Loukam, I.

2020-09-29 epidemiology
10.1101/2020.09.28.20203299 medRxiv
Show abstract

In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from the Algeria health ministry has been used to train and test the ELM models. The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID). The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performances (NSE = 0.9999, IO = 0.9988, R2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 6%
23.3%
Similar papers in this journal
2
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
5.4%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 18%
5.1%
Similar papers in this journal
4
Infectious Disease Modelling
○ 54 papers in training set
Top 0.3%
5.1%
Similar papers in this journal
5
JMIRx Med
◐ 32 papers in training set
Top 0.2%
4.5%
Similar papers in this journal
6
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.5%
7
BioMed Research International
○ 28 papers in training set
Top 0.3%
3.4%
50% of probability mass above
8
BMC Infectious Diseases
○ 133 papers in training set
Top 1%
2.5%
9
PLOS Global Public Health
● 344 papers in training set
Top 5%
2.2%
10
Computers in Biology and Medicine
○ 128 papers in training set
Top 2%
1.8%
11
Heliyon
○ 152 papers in training set
Top 3%
1.8%
12
Epidemiology and Infection
◐ 89 papers in training set
Top 1%
1.8%
13
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.4%
1.8%
14
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 3%
1.6%
15
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.7%
1.6%
16
Annals of Translational Medicine
○ 18 papers in training set
Top 0.3%
1.4%
17
International Journal of Medical Informatics
○ 26 papers in training set
Top 0.8%
1.4%
18
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.4%
1.2%
19
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.3%
1.2%
20
Diagnostics
○ 50 papers in training set
Top 2%
1.1%
21
Risk Management and Healthcare Policy
○ 10 papers in training set
Top 0.3%
1.1%
22
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.4%
1.1%
23
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.1%
24
BMC Public Health
○ 158 papers in training set
Top 5%
0.9%
25
Cureus
○ 68 papers in training set
Top 5%
0.6%
26
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.8%
0.6%
27
Frontiers in Medicine
○ 120 papers in training set
Top 4%
0.6%
28
BMC Medical Research Methodology
○ 47 papers in training set
Top 1%
0.6%
29
Computational Biology and Chemistry
○ 28 papers in training set
Top 1%
0.6%
30
Biology Methods and Protocols
◐ 61 papers in training set
Top 2%
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.