Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world
arXiv:2003.05681 · doi:10.1007/s11071-020-05862-6
Abstract
Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.
References in corpus (7)
- Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China
- Modelling transmission and control of the COVID-19 pandemic in Australia
- Predictability of catastrophic events: material rupture, earthquakes, turbulence, financial crashes and human birth
- Game theoretic modelling of infectious disease dynamics and intervention methods: a mini-review
- On computational irreducibility and the predictability of complex physical systems
- Individual vaccination as Nash equilibrium in a SIR model with application to the 2009-10 Influenza A(H1N1) epidemic in France
- Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
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