Back

An SEIDR Model for the Early Spread of COVID-19

Cao, T.

2023-02-21 infectious diseases
10.1101/2023.02.17.23286115 medRxiv
Show abstract

In this article, we conduct a literature review on the history and mathematical modeling of infectious diseases and COVID-19. Next, some simple epidemic dynamic models and the basic reproductive number theory are introduced. We propose a SEIDR model for COVID-19 and provide the solution methods for the basic reproduction number, parameters, and dynamic model. Finally, we simulate the early stages of the COVID-19 epidemic in Argentina, Indonesia, Mexico, and South Africa with the SEIDR model.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 14%
13.0%
Similar papers in this journal
2
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.1%
12.2%
Similar papers in this journal
3
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
8.1%
Similar papers in this journal
4
Nonlinear Dynamics
○ 10 papers in training set
Top 0.1%
5.0%
Similar papers in this journal
5
Scientific Reports
○ 3612 papers in training set
Top 21%
4.4%
Similar papers in this journal
6
Infectious Disease Modelling
○ 54 papers in training set
Top 0.4%
4.1%
7
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.1%
50% of probability mass above
8
Royal Society Open Science
● 214 papers in training set
Top 1%
3.3%
9
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 2%
2.8%
10
Heliyon
○ 152 papers in training set
Top 2%
2.2%
11
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.1%
2.2%
12
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.8%
13
Infectious Diseases of Poverty
○ 11 papers in training set
Top 0.1%
1.7%
14
JMIRx Med
◐ 32 papers in training set
Top 1%
1.4%
15
Physical Review E
● 112 papers in training set
Top 0.9%
1.2%
16
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 1.0%
1.2%
17
The European Physical Journal Plus
○ 13 papers in training set
Top 0.2%
1.2%
18
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 1.0%
1.1%
19
Cureus
○ 68 papers in training set
Top 4%
1.0%
20
Applied Sciences
○ 25 papers in training set
Top 0.7%
1.0%
21
Medicine
○ 31 papers in training set
Top 2%
0.9%
22
Mathematical Biosciences
○ 49 papers in training set
Top 1%
0.9%
23
Vaccines
○ 198 papers in training set
Top 3%
0.9%
24
PeerJ
◐ 308 papers in training set
Top 12%
0.6%
25
IEEE Access
● 35 papers in training set
Top 1%
0.6%
26
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.6%
27
Frontiers in Physics
○ 21 papers in training set
Top 0.4%
0.6%
28
F1000Research
○ 88 papers in training set
Top 4%
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.