arXiv 29 Jun 2020 · Statistics — Methodology · publishedJournal of Econometrics (2020) · 3 citations (OpenAlex)
arXiv:2006.16214 · PDF · DOI · OpenAlex · Extracted main text
We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%-1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate ( 0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%-17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%-8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bendavid, E., Mulaney, B., Sood, N., Shah, S., Ling, E., Bromley-Dul… (2020) Covid-19 antibody seroprevalence in santa clara county, california | 1.000 | 11 | 4 | 100% |
| 2 | Chen, X., Christensen, T. M. and Tamer, E (2018) Monte carlo confidence sets for identified sets | 0.894 | 7 | 3 | 71% |
| 3 | Kaido, H., Molinari, F. and Stoye, J (2019) Confidence intervals for projections of partially identified parameters | 0.644 | 2 | 2 | 100% |
| 4 | Stoye, J (2009) More on confidence intervals for partially identified parameters | 0.644 | 2 | 2 | 100% |
| 5 | Hortacsu, A., Liu, J. and Schwieg, T (2020) Estimating the fraction of unreported infections in epidemics with a known epicenter: an application to covid-19 | 0.585 | 3 | 1 | 100% |
| 6 | Garcia-Basteiro, A. L. et al (2020) Seroprevalence of antibodies against sars-cov-2 among health care workers in a large spanish reference hospital | 0.405 | 1 | 1 | 100% |
| 7 | Alvarez, F. E., Argente, D. and Lippi, F (2020) A simple planning problem for covid-19 lockdown | 0.405 | 1 | 1 | 100% |
| 8 | Baggett, T. P., Keyes, H., Sporn, N. and Gaeta, J. M (2020) Prevalence of sars-cov-2 infection in residents of a large homeless shelter in boston | 0.405 | 1 | 1 | 100% |
| 9 | Beresteanu, A., Molchanov, I. and Molinari, F (2012) Partial identification using random set theory | 0.405 | 1 | 1 | 100% |
| 10 | Chernozhukov, V., Hong, H. and Tamer, E (2007) Estimation and confidence regions for parameter sets in econometric models 1 | 0.405 | 1 | 1 | 100% |
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