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Statistical analysis of Covid-19 mortality rate via probability distributions.
Farooq, Muhammad; Ijaz, Muhammad; Atif, Muhammad; Abushal, Tahani; El-Morshedy, Mahmoud.
  • Farooq M; Department of Statistics, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.
  • Ijaz M; Department of Mathematics and Statistics, The University of Haripur, Haripur, Khyber Pakhtunkhwa, Pakistan.
  • Atif M; Department of Statistics, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.
  • Abushal T; Department of Mathematical Sciences, Umm Al-Qura University, Makkah Al Mukarramah, Saudi Arabia.
  • El-Morshedy M; Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PLoS One ; 17(10): e0274133, 2022.
Article in English | MEDLINE | ID: covidwho-2089401
ABSTRACT
Among other diseases, Covid 19 creates a critical situation around the world. Five layers have been recorded so far, resulting in the loss of millions of lives in different countries. The virus was thought to be contagious, so the government initially severely forced citizens to keep a distance from each other. Since then, several vaccines have been developed that play an important role in controlling mortality. In the case of Covid-19 mortality, the government should be forced to take significant steps in the form of lockdown, keeping you away or forcing citizens to vaccinate. In this paper, modeling of Covid-19 death rates is discussed via probability distributions. To delineate the performance of the best fitted model, the mortality rate of Pakistan and Afghanistan is considered. Numerical results conclude that the NFW model can be used to predict the mortality rate for Covid-19 patients more accurately than other probability models.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0274133

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0274133