在COVID-19下重新开放:需要注意什么

J. Harris
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引用次数: 24

摘要

我们批判性地分析当前可用的COVID-19疫情状况指标,以便州长掌握必要的路标,以决定是否进一步放松或重新收紧对社会和经济活动的控制。我们发现,对威斯康星州州级数据的过度依赖,混淆了春季初选和肉类加工商爆发疫情的影响。洛杉矶放宽的检测标准可能使观察到的新感染率趋势有所上升。根据最终致命病例首次发病的日期而不是死亡日期对新泽西州数据进行的重新分析显示,该州的死亡人数已经达到高峰。来自伊利诺伊州库克县的证据表明,阳性检测百分比的趋势可能完全具有误导性。白宫指南所倡导的流感样疾病急诊就诊趋势不太可能提供信息。加州奥兰治县的医院人口普查数据表明,基于医疗保健系统的指标可能更可靠,信息更丰富。对德克萨斯州圣安东尼奥市累积感染的分析表明,旨在指导放松社交距离决策的数学模型受到未经检验的假设的严重限制。普遍的冠状病毒检测本身可能无法解决数据解释和因果推理的难题。
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Reopening Under COVID-19: What to Watch For
We critically analyze the currently available status indicators of the COVID-19 epidemic so that state governors will have the guideposts necessary to decide whether to further loosen or instead retighten controls on social and economic activity. Overreliance on aggregate, state-level data in Wisconsin, we find, confounds the effects of the spring primary elections and the outbreak among meat packers. Relaxed testing standards in Los Angeles may have upwardly biased the observed trend in new infection rates. Reanalysis of New Jersey data, based upon the date an ultimately fatal case first became ill rather than the date of death, reveals that deaths have already peaked in that state. Evidence from Cook County, Illinois shows that trends in the percentage of positive tests can be wholly misleading. Trends on emergency department visits for influenza-like illness, advocated by the White House Guidelines, are unlikely to be informative. Data on hospital census counts in Orange County, California suggest that healthcare system-based indicators are likely to be more reliable and informative. An analysis of cumulative infections in San Antonio, Texas, shows how mathematical models intended to guide decisions on relaxation of social distancing are severely limited by untested assumptions. Universal coronavirus testing may not on its own solve difficult problems of data interpretation and causal inference.
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