A novel multi regional reliability method for Covid-19 death forecast

Abstract

Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. The present study describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period, resulting in a reliable long-term forecast of the novel coronavirus death rate. To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. The present study presents a novel statistical method to analyse raw clinical data using a multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of the novel coronavirus, it is challenging to model such a phenomenon. The present study describes a novel bio-system reliability approach, particularly suitable for multi-region environmental and health systems, observed over a sufficient period, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. The method also indicates the accuracy by presenting the 95% confidence interval band. The suggested methodology can be used in various public health applications based on their clinical survey data.