What happens when time sampling density of a series matches its growth

This is the newly updated map of COVID-19 cases in the United States, updated, presumably, because of the new emphasis upon testing:

How do we know this is the recent of recent testing? Look at the map of active cases:

To the degree numbers of active cases fall down on top of cumulative cases means these are recent detections.

In other words, while concerns about importing COVID-19 cases from Europe are of some concern, the virus is here, the disease is here, and a typical person in the United States is much more likely to contract the disease from a fellow infected American who has not travelled than a European person (note there are no prohibitions against Americans coming home) coming here.

The lesson is that if a process has a certain rate of growth, and the sampling density in time isn’t keeping up with that growth, it is inevitable there will be extreme underreporting.

I would like to understand if that suppression of testing was deliberate or not. Between the present administration’s classifications of COVID-19-related information under National Security acts and the documented suppression of information on federal Web sites relating to climate change, I would be highly suspicious that such suppression, which would put 45 in an unpopular light, was accidental.

(update, 2020-03-15, 2113 EDT)

In the above, note that once a sampling density in time is increased to match growth of the counts, then it will appear as if the rate of growth of cases is extraordinary. That is false, of course, but it is a consequence of the failure to have an adequate sampling density (in time) in the first case.

(update, 2020-03-20, 1632 EDT)

Coronavirus status in Massachusetts.

Biologic details on SARS-CoV-2.

MSRI talk:

About ecoquant

See https://667-per-cm.net/about. Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.
This entry was posted in American Association for the Advancement of Science, American Statistical Association, anti-intellectualism, anti-science, climate denial, corruption, data science, data visualization, Donald Trump, dump Trump, epidemiology, experimental science, exponential growth, forecasting, Kalman filter, model-free forecasting, nonlinear systems, open data, penalized spline regression, population dynamics, sampling algorithms, statistical ecology, statistical models, statistical regression, statistical series, statistics, sustainability, the right to know, the stack of lies. Bookmark the permalink.

1 Response to What happens when time sampling density of a series matches its growth

  1. Pingback: New COVID-19 incidence in the United States as AR(1) processes | hypergeometric

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