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Executive Summary
Pages 1-14

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From page 1...
... The seven data types are: the number of confirmed cases, hospitalizations, emergency department visits, reported confirmed COVID-19 deaths, excess deaths, fraction of viral tests that are positive, and representative prevalence surveys (including both viral and antibody tests)
From page 2...
... This enhanced understanding can lead to more informed decisions on critical issues that depend on those indicators, such as when to lift social distancing restrictions, allow public gatherings, or reopen businesses. Drawing on relevant literature and expert judgment, this rapid expert consultation describes the considerations that apply in using the available data while taking account of their limitations.
From page 3...
... over prior comparable time periods as a measure of the total number of deaths that may be directly or indirectly attributable to COVID-19 • Fraction of viral tests that are positive as a measure of the total number of currently infected persons • Representative prevalence surveys (including both viral and antibody tests) administered to a representative sample of a defined population to estimate the percentage of persons in that population either currently or formerly positive for COVID-19 Given the rapid evolution of understanding of the virus that causes COVID-19, additional data types are emerging.
From page 4...
... Table 1 shows the seven data types listed above against the five criteria for assessing their reliability and validity. Check marks indicate that a data type generally meets a criterion, while the triangles denote the need for caution, meaning that the questions listed above under a criterion should be asked to better understand the quality of the data.
From page 5...
... ⚠ ⚠ ⚠ ⚠ ⚠ Fraction of viral tests that are positive Key Implication for Decision Making: These data may not be an adequate measure of prevalence, depending on testing criteria. If mainly symptomatic people are tested, this figure is expected to overestimate the true community prevalence.
From page 6...
... • Uncertainty, and measurement and sampling error: Sampling error due to small numbers of cases is likely to be a much smaller problem than bias. If multiple positive tests are reported for the same person over time, the number of positive tests divided by the base population could possibly produce an overestimate of the actual number of cases.
From page 7...
... In areas where hospitals have reached capacity, it is important to track transfers to other hospitals, especially from areas with limited facilities. Emergency Department Visits Implications for decision making: In some jurisdictions, data on emergency department (ED)
From page 8...
... , leading to errors in calculated death rates by race and ethnicity. • Time: Local health authorities initially report deaths quickly, but the final, complete, cleaned data may take time to produce.
From page 9...
... While the total number of deaths is reasonably accurate, it is difficult to calculate "excess deaths" because deaths in each year reflect unique public health phenomena. As a result, computing excess deaths is a statistical procedure that entails comparing current deaths with expected deaths based on historical averages, and the magnitude of the excess will depend on the time period chosen for comparison.
From page 10...
... There will be some time lag involved, however, in mounting and interpreting such a survey. While prevalence surveys in general, such as surveys of health care workers or convenience samples (defined in footnote 7 below)
From page 11...
... • Bias: If prevalence surveys are based on representative samples and if the sensitivity and specificity of the viral tests are known, bias due to errors in the tests can be corrected using well-known statistical formulas. It is important to make these corrections so that unbiased estimates can be obtained; see footnote 1 (Biemer and Lyberg, 2008)
From page 12...
... . Washington State's actual coronavirus death toll may be higher than current tallies, health officials say.
From page 13...
... We thank Oxiris Barbot, New York City Department Health and Mental Hygiene; Paul Biemer, RTI International and University of North Carolina, Chapel Hill; Ron Carlee, Old Dominion University; Jeffrey Eaton, Imperial College London; Thomas Farley, Philadelphia Department of Public Health; William Hanage, Harvard T.H. Chan School of Public Health; Stéphane Helleringer, The Johns Hopkins University; Claude-Alix Jacob, Cambridge Public Health Department; Nancy Krieger, Harvard T.H.
From page 14...
... , Georgetown University DOMINIQUE BROSSARD, University of Wisconsin, Madison JANET CURRIE, Princeton, University MICHAEL HOUT, New York University ARATI PRABHAKAR, Actuate ADRIAN E RAFTERY, University of Washington JENNIFER RICHESON, Yale University Staff: MONICA N


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