Influenza-type epidemic risks by spatio-temporal Gaidai-Yakimov method

Background: Global public health was recently hampered by reported widespread spread of new coronavirus illness, although morbidity and fatality rates were low. Future coronavirus infection rates may be accurately predicted over a long-time horizon, using novel bio-reliability approach, being especially well suitable for environmental multi-regional health and biological systems. The high regional dimensionality along with cross-correlations between various regional datasets being challenging for conventional statistical tools to manage. Methods: To assess future risks of epidemiological outbreak in any province of interest, novel spatio-temporal technique has been proposed. In a multicenter, population-based environment, assess raw clinical data using state-of-the-art, cutting-edge statistical methodologies. Results: Authors have developed novel reliable long-term risk assessment methodology for future coronavirus infection outbreaks. Conclusions: Based on national clinical patient monitoring raw dataset, it is concluded that although underlying data set data quality is questionable, the proposed method may be still applied.