A Statistical Analysis of Excess Mortality Mean at Covid-19 in 2020-2021 | ||||
| Computational Journal of Mathematical and Statistical Sciences | ||||
| Volume 2, Issue 2, November 2023, Page 223-239 PDF (1.24 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/cjmss.2023.229207.1014 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Md Nurul Raihen   1; Sultana Akter2; Fariha Tabassum3; Farjana Jahan2; Shakera Begum2
| ||||
| 1Department of Mathematics and Computer Science, Fontbonne University, Saint Louis, MO, USA | ||||
| 2Department of Statistics, Western Michigan University, Kalamazoo, 49006, MI, USA | ||||
| 3Department of Sociology, Western Michigan University, Kalamazoo, 49006, MI, USA | ||||
| Abstract | ||||
| When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the measurement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthinkable in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic's effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under 'normal' circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 "Spanish Flu"6. Excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affected mortality rate. The estimates of excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lacking complete reported data using the Binary logistic regression, and Probit regression analysis count framework. Standardized residual plots, AIC, and Variance Inflation Factor (VIF) after checking all of those, we found some significant predictors from our choosing model , and the coefficient of all predictors gave the information that some factors have positive effect, and some has a negative effect at excess mortality at COVID-19 (2020-2021). | ||||
| Keywords | ||||
| COVID-19; Excess Mortality; Pandemic; Probit Regression; Logistic regression | ||||
|
Statistics Article View: 162 PDF Download: 171 |
||||
