Predicting the Outbreak Risks and Inflection Points of COVID‐19 Pandemic with Classic Ecological Theories

Advanced Science

Zhanshan (Sam) Ma¹ 

[1] Computational Biology and Medical Ecology Lab State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 China;

关键词: Allee effects;    coronaviruses;    infection aggregation critical threshold;    outbreak inflection (turning or tipping) points;    ratio of migrational infections to local contagions |COVID‐19 (coronavirus disease);   

DOI  :  10.1002/advs.202001530

来源: DOAJ

【 摘 要 】

Abstract Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID‐19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID‐19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor’s power‐law) and Ma’s population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell’s neutral theory, and Ma’s diversity‐time relationship adapted for the infection−time relationship. Fisher‐Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b‐values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID‐19. (ii) The infection aggregation critical threshold (M0) adapted from PACD varies with time (outbreak‐stage), space (region) and public‐health interventions. Exceeding M0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.

【 授权许可】

Unknown