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A simple mathematical model to predict and validate the spread of Covid-19 in India.
Kumar, Harish; Arora, Pawan K; Pant, Meena; Kumar, Anil; Akhtar Khan, Shahroz.
  • Kumar H; National Institute of Technology Delhi, India.
  • Arora PK; Galgotias College of Engineering and Technology, Gautambudh Nagar, U.P, India.
  • Pant M; National Institute of Technology Delhi, India.
  • Kumar A; H.R.Group of Institutions, Ghaziabad, U.P, India.
  • Akhtar Khan S; Galgotias College of Engineering and Technology, Gautambudh Nagar, U.P, India.
Mater Today Proc ; 47: 3859-3864, 2021.
Article in English | MEDLINE | ID: covidwho-1164177
ABSTRACT
The new outbreak of the corona virus (Covid-19) is expanding rapidly worldwide, disrupting millions and prompting authorities to take swift measures to avoid the disease. National lockdown imposed by the Indian government since 25 March 2020, the early lockdown action shows as compared to many other Countries/states can benefit from limiting the final size of the epidemic. A report on the issue of spreading the Covid-19 modeling in India is under review. This study analyzes Covid-19 infections by 20Dec 2021 and presents a mathematical approach for forecasting new cases or cumulative cases in practical situations. This forecast is much needed to schedule/continue medical set-ups for possible action to tackle the Covid-19 outbreak. It is important to mention here that the number of authors has proposed different models for predicting the expansion of Covid-19 to India and other countries; almost no model has yet to be demonstrated viable. With this mathematical model, it is simple to forecast the transfer of Covid-19. It is clear from the data that lockdown has played a significant role in controlling the transmission of the disease. A close match between the predicted empirical results and the available results proves the derived model similarity.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Mater Today Proc Year: 2021 Document Type: Article Affiliation country: J.matpr.2021.03.434

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Mater Today Proc Year: 2021 Document Type: Article Affiliation country: J.matpr.2021.03.434