The Cognitive and Mathematical Foundations of Analytic Epidemiology

Abstract

Analytic epidemiology is a transdisciplinary study on the cognitive, theoretical, and mathematical models of COVID-19 and other contagious diseases. It is recognized that analytic epidemiology may be better studied by big data explorations at the macro level rather than merely biological analyses at the micro level in order to not lose the forest for the trees. This paper presents a basic research on analytic epidemiology underpinned by sciences of cognition, computer, big data, information, AI, mathematics, epidemiology, and systems. It introduces a novel Causal Probability Theory (CPT) for explaining the Dynamic Pandemic Transmission Model (DPTM) of analytic epidemiology. It reveals how the fundamental reproductive rate $(R_{0})$ may be rigorously calibrated based on big data of COVID-19. A theoretical framework of analytic epidemiology is developed to elaborating the insights of pandemic mechanisms in general and COVID-19 in particular. Robust and accurate predictions on key attributes of COVID-19, including $R_{0}(t)$, forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.