Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the‘doubling rate
Epidemiologists use mathematical models to predict epidemic trends, and these results are inherently uncertain when parameters are unknown or changing. In other contexts, such as climate, modellers use multi-model ensembles to inform their decision-making: when forecasts align, modellers can be more certain. This paper looks at a sub-set of alternative epidemiological models that focus on the ‘doubling rate’, and it cautions against relying on the method proposed in (Pike & Saini, 2020) which relies on the data for China to calculate future trajectories. Such approaches are subject to overfitting, a common problem in financial and economic modelling. This paper finds, surprisingly, that the data for China are hyper-exponential, not exponential. Instead, this paper proposes using non-parametric methods, and local regression methods, to support epidemiologists and policymakers in assessing the relative effectiveness of social distancing across multiple settings. All works contained herein are provided free to use worldwide by the author under CC BY 2.0.
Citation : Lancastle, N.M. (forthcoming). Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the doubling rate. Pre-print.
Research Institute : Finance and Banking Research Group (FiBRe)