Back

The connection of growth and medication of COVID-19 affected people after 30 days of lock down in India

Bhattacharyya, A.; Bhattacharyya, D.; Mukherjee, J.

2020-05-23 epidemiology
10.1101/2020.05.21.20107946
Show abstract

The COVID-19 pandemic has already consumed few months of indolence all over the world. Almost every part of the world from which the victim of COVID 19 are, have not yet been able to find out a strong way to combat corona virus. Therefore, the main aim is to minimize the spreading of the COVID-19 by detecting most of the affected people during lockdown. Hence, it is necessary to understand what the nature of growth is of spreading of this corona virus with time after almost one month (30 days) of lockdown. In this paper we have developed a very simple mathematical model to describe the growth of spreading of corona virus in human being. This model is based on realistic fact and the statistics we have so far. For controlling the spread of the COVID-19, minimization of the growth with minimum number of days of lockdown is necessary. We have established a relation between the long-term recovery coefficient and the long-term infected coefficient. The growth can be minimized if such condition satisfies. We have also discussed how the different age of the people can be cured by applying different types of medicine. We have presented the data of new cases, recovery and deaths per day to visualize the different coefficient for India and establish our theory. We have also explained how the medicine could be effective to sustain and improve such condition for country having large population like India.

Matching journals

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

1
PLOS ONE
based on 1737 papers
Top 36%
13.5%
2
Chaos, Solitons & Fractals
based on 17 papers
Top 0.1%
13.0%
3
Scientific Reports
based on 701 papers
Top 11%
11.5%
4
Mathematical Biosciences and Engineering
based on 14 papers
Top 0.1%
4.7%
5
Infectious Disease Modelling
based on 50 papers
Top 2%
3.1%
6
Epidemiology and Infection
based on 80 papers
Top 2%
3.1%
7
Heliyon
based on 57 papers
Top 2%
2.5%
50% of probability mass above
8
Frontiers in Physics
based on 11 papers
Top 0.4%
2.4%
9
JMIRx Med
based on 29 papers
Top 2%
1.8%
10
Frontiers in Public Health
based on 135 papers
Top 15%
1.8%
11
International Journal of Environmental Research and Public Health
based on 116 papers
Top 14%
1.6%
12
Royal Society Open Science
based on 49 papers
Top 4%
1.4%
13
Journal of Medical Virology
based on 95 papers
Top 7%
1.4%
14
PeerJ
based on 46 papers
Top 6%
1.4%
15
Disaster Medicine and Public Health Preparedness
based on 16 papers
Top 2%
1.4%
16
PLOS Computational Biology
based on 141 papers
Top 7%
1.4%
17
Biology
based on 11 papers
Top 0.5%
1.4%
18
Mathematical Biosciences
based on 15 papers
Top 1%
1.4%
19
BMC Public Health
based on 148 papers
Top 19%
1.2%
20
Cureus
based on 64 papers
Top 14%
1.2%
21
COVID
based on 12 papers
Top 0.2%
0.8%
22
Archives of Clinical and Biomedical Research
based on 18 papers
Top 2%
0.8%
23
Frontiers in Medicine
based on 99 papers
Top 18%
0.8%
24
Journal of Theoretical Biology
based on 29 papers
Top 2%
0.8%
25
Frontiers in Genetics
based on 32 papers
Top 6%
0.7%
26
Journal of Family Medicine and Primary Care
based on 10 papers
Top 2%
0.7%