World COVID-19 bivariate statistics by multi-modal Gaidai reliability approach

The global spread of coronavirus (COVID-19 or SARS-CoV-2) in the years 2020-2022 and beyond was characterized by high rates of transmission, low rates of morbidity and fatality, and challenges posed to national public health systems. A novel multi-modal bio-reliability approach is benchmarked and cross-validated in this case study. This case study delivers long-term epidemiological prognostics for biological, health, and environmental multi-regional systems, measured across representative periods. Assessing future epidemiological risks and hazards, related to high coronavirus mortality rates in any given location or region of interest at any given time horizon was the main goal of the current case study. Long-term epidemiological outbreak risks, posed to national public health systems, using raw (unprocessed) clinical histories, were assessed along with respective confidence intervals. Complex multi-variate inter-correlations between different regional data, given high regional dimensionality cannot be adequately incorporated by existing statistical methods, except for Monte Carlo Simulation (MCS) based numerical methods, as those are limited to univariate and bivariate systems. This case study presented a novel biosystem bio-reliability method, particularly suitable for multi-regional health and environmental systems, observed during a representative observational period. Long-term high-death rate prognostics were carried out. The proposed multi-variate bio-reliability approach, based on raw clinical survey data, may be utilized for a wide range of clinical public health, epidemiologic, and environmental applications.