Abstract
The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods also needs to be built into any effective model for making such predictions. We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The ensuing data collapse renders the transmission dynamics largely independent of geopolitical variations, the effectiveness of various mitigation strategies, population demographics, etc. We propose a simple two-parameter model—the Blue Sky model—and show that one class of transmission dynamics can be explained by a solution that lives at the edge of a blue sky bifurcation. In addition, the data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can also be realized in a model-independent manner as we show using a deep neural network. The methodology adopted in this work can potentially be applied to the transmission of other infectious diseases and new universality classes may be found. The predictability in transmission dynamics and the simplicity of our methodology can help in building policies for exit strategies and mitigation methods during a pandemic.
Highlights
The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease
We propose the Blue Sky model (BSM)—an extension of the logistic growth model—which explains the existence of these two classes, the first, through a solution that lives at the edge of blue sky bifurcation and a second that maps the BSM onto the logistic growth model
Everything from testing strategies to containment procedures followed very different patterns in different nations and variation even at the regional levels were very palpable. This has made the analysis of the data to model the underlying dynamics of disease spread a formidable task that has rarely yielded to any generalization
Summary
The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods needs to be built into any effective model for making such predictions We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can be realized in a model-independent manner as we show using a deep neural network. Given that the scaling and data collapse have stood well the test of time for a very diverse set of systems and is grounded in well understood physical principles, we are proposing a robust method for making long-term predictions that will help in evaluating disease mitigation efficacies and planning exit strategies for the future. We hope to convince the readers that we have established a method which does not require parameter tuning or model selection to gain a handle over the prediction of disease spread
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