A Statistical Analysis of Positive Excess Mortality at Covid-19 in 2020-2021

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. Positive excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affects the mortality rate. The estimates of positive 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 Poisson, Negative Binomial count framework. By overdispersion test, we checked the assumption of the Poisson model, and then we chose the negative binomial as a good fitting model for this analysis through Akaike Information Criteria (AIC) and Standardized residual plots, after that checking the P-value<0.05; we found some significant predictors from our choosing model Negative binomial model, and the coefficient of all predictors gave the information that some factors have a positive effect, and some has a negative effect at positive excess mortality at COVID-19 (2020-2021).

A Statistical Analysis of Excess Mortality Mean at Covid-19 in 2020-2021

“…By Raihen and Akter [38], the present study undertook the computation of excess death rates for the period encompassing the global pandemic in the years 2020 and 2021, focusing on both the Global and World Health Organization (WHO) Member State Regions. Instead of presenting novel estimations, our objective was to rigorously evaluate more efficient methodologies and ascertain the key factors that impact COVID-19's Excess Mortality, comparisons, and limitations, along with the implications of the dataset; See Raihen et al [39].…”

Prediction modeling using deep learning for the classification of grape-type dried fruits

“…When working with a certain dataset, it is paramount importance to choose a validating method that is suitable for the situation. Because it generates reliable findings, hold-out validation is frequently the method of choice when dealing with huge datasets [33]. In this investigation, we utilized the hold-out validation strategy to test 30% of the dataset while simultaneously training 70% of it.…”