Back

Covid-19 Incidence Rate Evolution Modeling using Dual Wave Gaussian-Lorentzian Composite Functions

Poomari, R.

2020-06-09 epidemiology
10.1101/2020.06.07.20124966 medRxiv
Show abstract

Modeling the evolution of Covid-19 incidence rate is critical to deciding and assessing non-medical intervention strategies that can lead to successful containment of the pandemic. This research presents a mathematical model to empirically assess measures related to various pandemic containment strategies, their similarities and a probabilistic estimate on the evolution of Covid-19 incidence rates. The model is built on the principle that, the exponential rise and decay of the number of confirmed Covid-19 infections can be construed as a set of concurrent non-linear waves. These waves can be characterized by a linear combination of Gaussian and Cauchy Lorentz functions collectively termed as Gaussian-Lorentzian Composite (GLC) function. The GLC function is used for non-linear approximation of officially confirmed Covid-19 incidence rates in each country. Results of fitting GLC based models to incidence rate trends of 20 different countries proves that the models can empirically explain the growth and decay trajectory Covid-19 infections in a given population.

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 11%
17.1%
Similar papers in this journal
2
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
9.8%
Similar papers in this journal
3
Infectious Disease Modelling
○ 54 papers in training set
Top 0.2%
6.8%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 14%
5.6%
Similar papers in this journal
5
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
5.2%
Similar papers in this journal
6
Journal of Theoretical Biology
○ 162 papers in training set
Top 0.7%
4.9%
7
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.3%
2.8%
50% of probability mass above
8
Mathematical Biosciences
○ 49 papers in training set
Top 0.4%
2.8%
9
JMIRx Med
◐ 32 papers in training set
Top 0.5%
2.7%
10
Nonlinear Dynamics
○ 10 papers in training set
Top 0.1%
2.7%
11
PLOS Computational Biology
● 1863 papers in training set
Top 12%
2.4%
12
Frontiers in Public Health
○ 148 papers in training set
Top 3%
2.4%
13
Physical Biology
○ 46 papers in training set
Top 0.3%
2.1%
14
Journal of The Royal Society Interface
● 235 papers in training set
Top 2%
1.7%
15
Cureus
○ 68 papers in training set
Top 2%
1.7%
16
BMC Infectious Diseases
○ 133 papers in training set
Top 3%
1.3%
17
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.1%
1.3%
18
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
1.1%
19
The European Physical Journal Plus
○ 13 papers in training set
Top 0.2%
1.1%
20
Mathematics
○ 11 papers in training set
Top 0.2%
1.1%
21
BMC Public Health
○ 158 papers in training set
Top 5%
1.0%
22
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 1%
0.9%
23
Epidemiology and Infection
◐ 89 papers in training set
Top 3%
0.8%
24
Biomechanics and Modeling in Mechanobiology
○ 29 papers in training set
Top 0.4%
0.8%
25
Royal Society Open Science
● 214 papers in training set
Top 6%
0.8%
26
PLOS Global Public Health
● 344 papers in training set
Top 8%
0.8%
27
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.6%
28
Applied Sciences
○ 25 papers in training set
Top 1%
0.6%
29
Physical Review E
● 112 papers in training set
Top 1%
0.6%
30
Quantitative Biology
○ 12 papers in training set
Top 0.2%
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.