Back

Spread of COVID-19 in India: A Simple Algebraic Study

Roy, S.

2020-05-14 epidemiology
10.1101/2020.05.10.20097691 medRxiv
Show abstract

The number of patients, infected with COVID-19, began to increase very rapidly in India from March 2020. The country was put under lockdown from 25 March 2020. The present study is aimed at providing a simple algebraic analysis of the trend that is evident in the spread of the disease in this part of the world. The purpose of this algebraic approach is to simplify the calculation sufficiently by deviating from the standard techniques that are conventionally used to construct mathematical models of epidemics. The predictions, obtained from this algebraic study, are found to be in reasonable agreement with the recorded data. Using this mathematical formulation we have determined the time variation of the number of asymptomatic patients, who are believed to play a major role in spreading the disease. We have discussed the effect of lockdown in reducing the rate of transmission of the disease. On the basis of the proposed models, predictions have been made regarding the possible trend of the rise in the number of cases beyond the withdrawal of lockdown. All these things have been calculated by using very simple mathematical expressions which can be easily understood and used by those who have a rudimentary knowledge of algebra.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 5%
27.1%
Similar papers in this journal
2
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
9.1%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 18%
5.0%
Similar papers in this journal
4
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
4.1%
Similar papers in this journal
5
Cureus
○ 68 papers in training set
Top 1%
3.5%
Similar papers in this journal
6
Epidemiology and Infection
◐ 89 papers in training set
Top 0.4%
3.3%
50% of probability mass above
7
Nonlinear Dynamics
○ 10 papers in training set
Top 0.1%
2.8%
8
JMIRx Med
◐ 32 papers in training set
Top 0.6%
2.4%
9
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.3%
2.4%
10
PeerJ
◐ 308 papers in training set
Top 4%
2.2%
11
Mathematical Biosciences
○ 49 papers in training set
Top 0.5%
1.9%
12
Journal of Theoretical Biology
○ 162 papers in training set
Top 1%
1.9%
13
Infectious Disease Modelling
○ 54 papers in training set
Top 0.7%
1.8%
14
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.5%
15
Royal Society Open Science
● 214 papers in training set
Top 4%
1.4%
16
BMC Research Notes
○ 33 papers in training set
Top 0.5%
1.4%
17
Environmental Research
○ 49 papers in training set
Top 0.7%
1.2%
18
Tropical Medicine and Infectious Disease
○ 12 papers in training set
Top 0.2%
1.2%
19
Frontiers in Public Health
○ 148 papers in training set
Top 5%
1.2%
20
The European Physical Journal Plus
○ 13 papers in training set
Top 0.2%
1.1%
21
COVID
○ 14 papers in training set
Top 0.4%
0.9%
22
Applied Sciences
○ 25 papers in training set
Top 0.8%
0.9%
23
Physical Biology
○ 46 papers in training set
Top 0.9%
0.9%
24
BioMed Research International
○ 28 papers in training set
Top 2%
0.9%
25
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
0.6%
26
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.3%
0.6%
27
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
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.