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 loss 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 (R0) 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 R0(t), forecasted infectives/resources, and
the expected date of pandemic termination, are derived via
rigorous experiments on worldwide big data of epidemiology.
| Original language | English |
|---|---|
| Publication status | Published - 28 Sept 2020 |
| Externally published | Yes |
| Event | 19th IEEE International Conference on Cognitive Informatics and Cognitive Computing - Duration: 28 Sept 2020 → … |
Conference
| Conference | 19th IEEE International Conference on Cognitive Informatics and Cognitive Computing |
|---|---|
| Period | 28/09/20 → … |
Keywords
- big data experiments
- Analytic epidemiology
- cognitive pandemic models
- cognitive informatics
- COVID-19
- infectious transmission models
- cognitive algorithms