Reopening Under COVID-19: What to Watch For / Jeffrey E. Harris.

By:

Harris, Jeffrey E

Contributor(s):

National Bureau of Economic Research

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w27166.Publication details: Cambridge, Mass. National Bureau of Economic Research 2020.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: 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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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w27166 (Browse shelf(Opens below))	Not For Loan

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May 2020.

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.