If COVID ran rampant in NYC ...

Post #580

… then evidence suggests that there would have been a total of 12,000 COVID deaths.

While reports of early COVID deaths in New York City reach near 26,000, data from the UK and from Santa Clara County indicate that that amount of COVID death is essentially impossible.

If no isolation measures were taken for infected persons, and if no early treatment was given (early treatment cuts COVID death by 45% to 90%), then this deterministic SEIR model represents a plausible mortality outcome from COVID in NYC:

[click to enlarge]

As you can see at right, the COVID death toll passes 12,000 if no isolation and no early treatment is given. The parameters for the model are named in row 4, and described at bottom right. The highlighted parameter in cell C5 is the estimate of COVID IFR from 171,000 COVID infections:

Evidence suggests that only about half of the reported COVID deaths in NYC could have been due to COVID infections.

Reference

[source for the SEIR model] — Western Kentucky University. https://www.wku.edu/da/covid-19-research/excel_sir_model.php

[Santa Clara data] — Covid-19 Fatality Rate Between 0.1 and 0.3%, Gangelt and Santa Clara Combined. https://www.preprints.org/manuscript/202007.0155/v1

[As of 19 Jan 2021, from 117,000 COVID infections, there were 169 total deaths by Day 28 (corrected using “Day 28 deaths” as 89% of all deaths, by using upper bound estimates from Linton et. al.)] — Page 3. Epidemiological findings. UK Technical Briefing #5. https://www.gov.uk/government/publications/investigation-of-novel-sars-cov-2-variant-variant-of-concern-20201201

[“Time-to-death” model for COVID; using the 95% UB of both the mean and SD of the lognormal model that fit the actual deaths best] — Linton NM, Kobayashi T, Yang Y, et al. Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. Journal of Clinical Medicine. 2020 Feb;9(2). DOI: 10.3390/jcm9020538. PMID: 32079150; PMCID: PMC7074197. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074197/