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 growth rate
Lancastle, N. M.
Show abstract
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 growth rate, and it cautions against relying on the method proposed in (Pike & Saini, 2020): relying on the data for China to calculate future trajectories is likely to be subject to overfitting, a common problem in financial and economic modelling. This paper finds, surprisingly, that the data for China are double-exponential, not exponential; and that different countries are showing a range of different trajectories. The paper proposes using non-parametric and local regression methods to support epidemiologists and policymakers in assessing the relative effectiveness of social distancing policies. All works contained herein are provided free to use worldwide by the author under CC BY 2.0.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics 92%
- Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus 92%
- Effect of human behavior on the evolution of viral strains during an epidemic 92%
Similar papers in this journal
- Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19 95%
- On the use of growth models for forecasting epidemic outbreaks with application to COVID-19 data 95%
- Tracking the Dynamics and Allocating Tests for COVID-19 in Real-Time: an Acceleration Index with an Application to French Age Groups and Départements * 94%
Similar papers in this journal
- A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths 93%
- A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations 93%
- Multi-model forecasts of the ongoing Ebola epidemic in the Democratic Republic of Congo, March - October 2019 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.