Statistical aspects of the Covid-19 pandemic - Online registration

Date: Wednesday 08 September 2021, 5.15PM - 7.15PM
Location: Online
Discussion Paper Meeting
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Paper 1: 'Modeling the COVID-19 infection trajectory: a piecewise linear quantile regression

Authors: F Jiang (Fudan University, Shanghai, China); Z Zhao, (University of Notre Dame, Indiana, USA); X Shao (University of Illinois Urbana-Champaign, USA)

Paper 2: 'Quantifying the economic response to COVID-19 mitigations and death rates via forecasting Purchasing Managers' Indices using Generalised Network Autoregressive models with exogenous variables'

Authors: G Nason & J Wei, Imperial College London, UK

Paper 3: 'Small Data, Big Time---A retrospect of the first weeks of COVID-19'
Author: Q Zhao, Statistical Laboratory, University of Cambridge, UK

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Paper 1: 'Modeling the COVID-19 infection trajectory: a piecewise linear quantile regression We propose a piecewise linear quantile trend model to analyze the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles.

The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by COVID-19 infection curves) and automatically delivers both point and interval forecasts with minimal assumptions. Building on a self-normalized (SN) test statistic, this paper proposes a novel segmentation algorithm for multiple changepoint estimation. Theoretical guarantees such as segmentation consistency are established under mild and verifiable assumptions. Using the proposed method, we analyze the COVID-19 infection curves in 35 major countries and discover patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. A simple change-adaptive two-stage forecasting scheme is further designed to generate short-term prediction of COVID-19 cumulative new cases and is shown to deliver accurate forecast valuable to public health decision-making.

Paper 2: 'Quantifying the economic response to COVID-19 mitigations and death rates via forecasting Purchasing Managers' Indices using Generalised Network Autoregressive models with exogenous variables'

Knowledge of the current state of economies, how they respond to COVID-19 mitigations and indicators, and what the future might hold for them is important. We use recently-developed generalised network autoregressive (GNAR) models, using trade determined networks, to model and forecast the Purchasing Managers’ Indices for a number of countries. We use networks that link countries where the links themselves, or their weights, are determined by the degree of export trade between the countries. We extend these models to include node-specific time series exogenous variables (GNARX models), using this to incorporate COVID-19 mitigation stringency indices and COVID-19 death rates into our analysis. The highly parsimonious GNAR models considerably outperform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predicts the extent to which that the UK economy will be affected by harsher, weaker or no interventions


Paper 3: 'Small Data, Big Time---A retrospect of the first weeks of COVID-19'

This article reviews some early investigations and research studies in the first weeks of the coron avirus disease 2019 (COVID-19) pandemic from a statistician’s perspective. These investigations were based on very small datasets but were momentous in the initial global reactions to the pandemic. The article discusses the initial evidence of high infectiousness of COVID-19 and why that conclusion was not reached faster than in reality. Further reanalyses of some published COVID-19 studies show that the epidemic growth was dramatically underestimated by compartmental models, and the lack of fit could have been clearly identified by simple data visualization. Finally, some lessons for statisticians are discussed.

 

Meeting organized by Discussion Meetings Committee

Meeting organiser: Judith Shorten journal@rss.org.uk

 
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