Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments

doi:10.1371/journal.pone.0253381

	Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments
	Chalkiadakis, Ioannis 6; Yan, Hongxuan 2,5; Peters, Gareth W.4; Shevchenko, Pavel, V 1,3
刊名	PLOS ONE
	2021-06-28
卷号	16 期号:6 页码:39
ISSN号	1932-6203
DOI	10.1371/journal.pone.0253381
英文摘要	During the COVID-19 pandemic, governments globally had to impose severe contact restriction measures and social mobility limitations in order to limit the exposure of the population to COVID-19. These public health policy decisions were informed by statistical models for infection rates in national populations. In this work, we are interested in modelling the temporal evolution of national-level infection counts for the United Kingdom (UK-Wales, England, Scotland), Germany (GM), Italy (IT), Spain (SP), Japan (JP), Australia (AU) and the United States (US). We model the national-level infection counts for the period January 2020 to January 2021, thus covering both the pre- and post-vaccine roll-out periods, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We achieve this by exploring a variety of stochastic population growth models and comparing their calibration, with respect to in-sample fitting and out-of-sample forecasting, both with and without exposure adjustment, to the most widely used and reported growth model, the Gompertz population model, often referred to in the public health policy discourse during the COVID-19 pandemic. Model risk as we explore it in this work manifests in the inability to adequately capture the behaviour of the disease progression growth rate curve. Therefore, our concept of model risk is formed relative to the standard reference Gompertz model used by decision-makers, and then we can characterise model risk mathematically as having two components: the dispersion of the observation distribution, and the structure of the intensity function over time for cumulative counts of new infections daily (i.e. the force of infection) attributed directly to the COVID-19 pandemic. We also explore how to incorporate in these population models the effect that governmental interventions have had on the number of infected cases. This is achieved through the development of an exposure adjustment to the force of infection comprised of a purpose-built sentiment index, which we construct from various authoritative public health news reporting. The news reporting media we employed were the New York Times, the Guardian, the Telegraph, Reuters global blog, as well as national and international health authorities: the European Centre for Disease Prevention and Control, the United Nations Economic Commission for Europe, the United States Centres for Disease Control and Prevention, and the World Health Organisation. We find that exposure adjustments that incorporate sentiment are better able to calibrate to early stages of infection spread in all countries under study.
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000671695800036
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/58893]
专题	中国科学院数学与系统科学研究院
通讯作者	Chalkiadakis, Ioannis
作者单位	1.St Petersburg State Univ, Ctr Econometr & Business Analyt, St Petersburg, Russia 2.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China 3.Macquarie Univ, Dept Actuarial Studies & Business Analyt, Sydney, NSW, Australia 4.Heriot Watt Univ, Sch Math & Comp Sci, Dept Actuarial Math & Stat, Edinburgh, Midlothian, Scotland 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 6.Heriot Watt Univ, Sch Math & Comp Sci, Dept Comp Sci, Edinburgh, Midlothian, Scotland
推荐引用方式 GB/T 7714	Chalkiadakis, Ioannis,Yan, Hongxuan,Peters, Gareth W.,et al. Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments[J]. PLOS ONE,2021,16(6):39.
APA	Chalkiadakis, Ioannis,Yan, Hongxuan,Peters, Gareth W.,&Shevchenko, Pavel, V.(2021).Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments.PLOS ONE,16(6),39.
MLA	Chalkiadakis, Ioannis,et al."Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments".PLOS ONE 16.6(2021):39.