Trends


Table of contents

  1. Explanation
  2. Graphs & Notes

The brief focus herein are the continuous per capita graphs that implicitly illustrate continuous rates trends, i.e., continuous positives tests rates, deaths positives rates, hospitalized positives rates, and deaths hospitalized rates trends.     [A notes tab further below briefly discusses positives tests rates, a few observations therein probably apply to all rates.]

Explanation

The continuous per capita graphs are graphs of observations per $\small{100K}$ people against each other. For example

positives$\mathbf{/}$$\small{100K}$ $\small{[C]}$ $\:\; vs. \;$ tests$\mathbf{/}$$\small{100K}$ $\small{[C]}$

wherein

  • positives$\mathbf{/}$$\small{100K}$ $\small{[C]}$:
    $\small{100K}$ $\times$ (positive cases thus far)$\mathbf{/}$population
  • tests$\mathbf{/}$$\small{100K}$ $\small{[C]}$:
    $\small{100K}$ $\times$ (test conducted thus far)$\mathbf{/}$population


The population being the population of the area in question, and $\small{C}$ denotes continuous. Implicitly these graphs illustrate rate trends; following on from the example above, at a point on the curve the $\:\small{y/x}\:$ quotient is simply a date's

(positive observations thus far)$\mathbf{/}$(test observations thus far)

value. It is the shape of a curve over time, as it is re-calculated & updated daily, that is of interest. Can the shapes be better supplementary guides of on-the-ground trends? Especially because the growth/static shape of a curve might (a) signify or anticipate problems, (b) indicate shortages, and/or (c) aid planning, etc. And, the more reliable a state's data is - underlying measures based on rigorous standards and distinct human interactions - the more plausible its curve insights will be.

Consider the shapes of the PTpositives tests tab curves. The more tests a state conducts, the further to the right its curve extends, and if the curve is additionally parallel and “close” to the horizontal axis, the better the state of affairs. Of course there are issues, e.g., how far to the right? Depending on the data, the DHdeaths hospitalized curves could aid hospital care evaluations, and the HPhospitalized positives curves could aid planning ahead. [Upcoming: States & DPdeaths positives]

Graphs & Notes

The graphs:





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