Predicting COVID-19 Cases and Deaths in the USA from Tests and State Populations

Q2 Decision Sciences Advances in Decision Sciences Pub Date : 2021-01-01 DOI:10.47654/V25Y2021I2P1-27
D. Allena, M. McAleer
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引用次数: 2

Abstract

School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan,The paper presents a novel analysis of the US spread of the SARS-CoV-2 causes the COVID-19 disease across 50 States and 2 Territories. Simple cross-sectional regressions are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. Population density appears to play a significant role in transmission. This throws in sharp relief the relative e_ectiveness of the at-tempts to risk manage the spread of the virus by flattening the curve' (aka planking the curve) of the speed of transmission, and the effcacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic tech-niques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers, and risk management and deficision making of healthcare by state, regional and national governments in all countries. © 2021 Hindawi Limited. All rights reserved.
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从测试和州人口预测美国的COVID-19病例和死亡
悉尼大学数学与统计学院,台湾亚洲大学金融系,本文对美国50个州和2个地区的SARS-CoV-2传播引起的COVID-19疾病进行了新颖的分析。简单的横截面回归能够相当准确地预测病例总数和死亡人数,这使人们对旨在通过封锁控制疾病的措施产生怀疑。人口密度似乎在传播中起着重要作用。这让人们明显认识到,通过拉平传播速度曲线来风险管理病毒传播的尝试的相对有效性,以及封锁措施在疾病传播和死亡率方面的有效性。本文中提出的算法技术、结果和分析应该证明对所有国家的州、地区和国家政府的医疗保健专业人员、科学顾问以及风险管理和赤字制定有用。©2021 Hindawi Limited版权所有。
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来源期刊
Advances in Decision Sciences
Advances in Decision Sciences Mathematics-Applied Mathematics
CiteScore
4.70
自引率
0.00%
发文量
18
审稿时长
29 weeks
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