The Worst-Case Estimate for U.S. Coronavirus Deaths
Source: New York Times
Officials at the U.S. Centers for Disease Control and Prevention and epidemic experts from universities around the world conferred last month about what might happen if the new coronavirus gained a foothold in the United States. How many people might die? How many would be infected and need hospitalization?
One of the agency’s top disease modelers, Matthew Biggerstaff, presented the group on the phone call with four possible scenarios — A, B, C and D — based on characteristics of the virus, including estimates of how transmissible it is and the severity of the illness it can cause. The assumptions, reviewed by The New York Times, were shared with about 50 expert teams to model how the virus could tear through the population — and what might stop it.
The C.D.C.’s scenarios were depicted in terms of percentages of the population. Translated into absolute numbers by independent experts using simple models of how viruses spread, the worst-case figures would be staggering if no actions were taken to slow transmission.
Between 160 million and 214 million people in the U.S. could be infected over the course of the epidemic, according to one projection. That could last months or even over a year, with infections concentrated in shorter periods, staggered across time in different communities, experts said. As many as 200,000 to 1.7 million people could die.
Read more: https://www.nytimes.com/2020/03/13/us/coronavirus-deaths-estimate.html?referringSource=articleShare
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...because not all the cases are resolved.
i.e. in the middle of the outbreak you have a given number who have tested positive, and a known number of deaths but if the recovery takes weeks, it delays the final counts.
In the US, we don't even have a good handle on the number of contacts yet (denominator), so all narrow estimates (high or low), proffered so far should be taken with a grain of salt, or 2.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4504518/?fbclid=IwAR1DPGMPBhS-LoCsC2ArMtEC7XHv3-IWfQcO_NtcgP3lTheHFzy2F3-IXrs
Abstract
Estimating the case-fatality risk (CFR)—the probability that a person dies from an infection given that they are a case—is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR—preferential ascertainment of severe cases and bias from reporting delays—and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases.
Plus, a simple spreadsheet provides the following full range of possible outcomes from what little we know now:
almost 2 million people in ICUs and almost half a million dead.
But, if too many people get sick at once and hospitals and staff are overwhelmed, the mortality rate will rise.
