A Novel Bio-system Reliability Approach for Multi-state COVID-19 Epidemic Forecast

Oleg Gaidai1

Yihan Xing2,Email

1Shanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
2Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Norway. 


Novel coronavirus disease is spread worldwide with specific mortality and burdens worldwide public health. Due to the non-stationarity and complicated nature of the novel coronavirus, it is challenging to model such a phenomenon, delivering reliable long-term forecasts of the extreme death rate. The present study describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental health systems, observed over a sufficient period, resulting in a reliable long-term forecast of the novel coronavirus registration rate. This study analysed COVID-19 patient numbers from different US states, constituting an example of a multi-state model observed during the years 2020-2022. 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 and medical survey data-based bio-statistical approach. Non-dimensional parameter λ is introduced to measure multi-state risk level. The suggested method predicts a 100-year return period risk level along with its 95% confidence interval band. This methodology can be used in various public health applications based on clinical survey data.

A Novel Bio-system Reliability Approach for Multi-state COVID-19 Epidemic Forecast