ABSTRACT
Clinical importance: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. 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 infection rate and mortality. Objective: 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. Design: Apply modern novel statistical methods directly to raw clinical data. Setting: Multicenter, population-based, medical survey data based bio statistical approach. Main outcome and measure: Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Conclusions and relevance: The suggested methodology can be used in various public health applications, based on their clinical survey data. © 2023, Indian National Science Academy.