Statistical analysis of Covid-19 mortality rate via probability distributions

Area of Science:

• Epidemiology
• Biostatistics
• Mathematical Modeling

Background:

• Coronavirus Disease 2019 (Covid-19) has caused a global health crisis with millions of fatalities.
• Public health interventions like social distancing and vaccination have been crucial in managing mortality.
• Accurate modeling of mortality rates is essential for effective public health strategies.

Purpose of the Study:

• To model Covid-19 death rates using various probability distributions.
• To evaluate and compare the performance of different probability models in predicting mortality.
• To identify the most accurate model for forecasting Covid-19 mortality.

Main Methods:

• Application of probability distributions to model Covid-19 mortality data.
• Comparative analysis of model performance using mortality rates from Pakistan and Afghanistan.
• Numerical evaluation to determine the best-fitted probability model.

Main Results:

• The NFW (Navarro-Fenton-Weibull) model was identified as a highly accurate probability distribution for modeling Covid-19 mortality.
• The NFW model outperformed other considered probability models in predicting mortality rates.
• The study provides a robust framework for analyzing and predicting disease-specific mortality.

Conclusions:

• The NFW model offers a reliable tool for predicting Covid-19 patient mortality.
• Accurate mortality prediction is vital for informing public health policy and resource allocation.
• This modeling approach can be extended to other infectious diseases to aid in pandemic preparedness.