TY - GEN
T1 - The cognitive and mathematical foundations of analytic epidemiology
AU - Wang, Yingxu
AU - Plataniotis, Kostas N.
AU - Wang, Jane Z.
AU - Hou, Ming
AU - Zhou, Menchu
AU - Howard, Newton
AU - Peng, Jun
AU - Huang, Runhe
AU - Patel, Shushma
AU - Zhang, Du
N1 - Publisher Copyright:
©2020 IEEE
PY - 2020/9/26
Y1 - 2020/9/26
N2 - 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 (Ro) 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 Ro(t), forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.
AB - 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 (Ro) 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 Ro(t), forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.
KW - Analytic epidemiology
KW - Big data experiments
KW - COVID-19
KW - Cognitive algorithms
KW - Cognitive informatics
KW - Cognitive pandemic models
KW - Infectious transmission models
KW - R0
UR - http://www.scopus.com/inward/record.url?scp=85098886220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098886220&partnerID=8YFLogxK
U2 - 10.1109/ICCICC50026.2020.09450250
DO - 10.1109/ICCICC50026.2020.09450250
M3 - Conference contribution
AN - SCOPUS:85098886220
T3 - Proceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
SP - 6
EP - 14
BT - Proceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
A2 - Wang, Yingxu
A2 - Ge, Ning
A2 - Lu, Jianhua
A2 - Tao, Xiaoming
A2 - Soda, Paolo
A2 - Howard, Newton
A2 - Widrow, Bernard
A2 - Feldman, Jerome
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
Y2 - 26 September 2020 through 28 September 2020
ER -