Cross-Sectional In Silico Analysis of Covid-19 Pandemic

A major problem with healthcare administration is the ability to accurately quantify threats to health security−in real time. Here, a cross-sectional risk analysis for Covid -19 was performed using an online biological defense, artificial intelligence (AI) algorithm. Risk of surviving SARS CoV 2 contagion was quantified and assigned a unit based on the Biological Agents index (BAI) using the following variables for Pareto analysis: morbidity, mortality, population density, viral infection dose in half the population (ID50), economic indicators and modes of transmission. Results show survival for SARS CoV 2 is independent of person to person and or environment to person transmission. Rather, survival is dependent on human behavior. The benefit here is that this protocol can provide evidence-based science needed to identify appropriate mitigation and benchmark for use by governments, non-governmental, humanitarian, international affairs, medical and public policy professionals, journalists, and scholars—at all levels, for use in planning, finance and evaluation of performance to justify budgets when combating global contagion.