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Impact of optimal vaccination and social distancing on COVID-19 pandemic.
Saha, Sangeeta; Samanta, Guruprasad; Nieto, Juan J.
  • Saha S; Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India.
  • Samanta G; Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India.
  • Nieto JJ; Instituto de Matemáticas, Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain.
Math Comput Simul ; 200: 285-314, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1814927
ABSTRACT
The first COVID-19 case was reported at Wuhan in China at the end of December 2019 but till today the virus has caused millions of deaths worldwide. Governments of each country, observing the severity, took non-pharmaceutical interventions from the very beginning to break the chain of higher transmission. Fortunately, vaccines are available now in most countries and people are asked to take recommended vaccines as precautionary measures. In this work, an epidemiological model on COVID-19 is proposed where people from the susceptible and asymptomatically infected phase move to the vaccinated class after a full two-dose vaccination. The overall analysis says that the disease transmission rate from symptomatically infected people is most sensitive on the disease prevalence. Moreover, better disease control can be achieved by vaccination of the susceptible class. In the later part of the work, a corresponding optimal control problem is considered where maintaining social distancing and vaccination procedure change with time. The result says that even in absence of social distancing, only the vaccination to people can significantly reduce the overall infected population. From the analysis, it is observed that maintaining physical distancing and taking vaccines at an early stage decreases the infection level significantly in the environment by reducing the probability of becoming infected.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Math Comput Simul Year: 2022 Document Type: Article Affiliation country: J.matcom.2022.04.025

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Math Comput Simul Year: 2022 Document Type: Article Affiliation country: J.matcom.2022.04.025