Multivariate risks assessment for complex bio-systems by Gaidai reliability method

Abstract

Spread of novel coronavirus and other flu-like illnesses, periodically causing increased death and morbidity rates, places pressures on national health systems. In order to provide a reliable long-term forecast of the new coronavirus infection rate, this research employs novel Gaidai bio-system reliability technique, especially suitable for multi-regional biological, environmental and public health systems. The goal of this study was to directly apply state of art statistical techniques to unprocessed raw clinical data, utilizing a multicenter, population-based biostatistical methodology. Epidemiological risks have been accurately forecasted, specifically for European Union member states. Based on their clinical survey data, suggested spatiotemporal methodology may be applied in a variety of public biological and health applications.

Keywords

• Dynamic Systems
• Public Health
• Prognostics
• SARS-CoV-2
• Artificial Intelligence
• Risk