Copyright © 2016 - 2020, The Troy Press
Copyright © 2016 - 2020, The Troy Press
This page is a part of our broader Covid-19 coverage:
Ohio's "Stay-at-home" was ordered March 22, and expires May 29.
In looking at the data we see some peturbations likely caused by social events that resulted in a significant enough number of new infections to be visible in the data.
Someone from Ohio has informed us that April 9 was the first statehouse protest. So some of that increase could have been from that. There's also the possibility there were some number of people who'd been in the throngs of spring-breaks in places like Florida who were manifesting symptoms - or had passed it along to others who got sick.
The April 28 increase was about two weeks following another couple days of statehouse protests. Clearly the protests have had some impact visible in the data as well, starting Memorial Day.
The only of these to ever drop is the number of active cases, which is bound to go down some day. So, other than active cases, it's the rate of change that's important. Most of our other graphs are focused on showing you these changes in the right scale since here, any squiggle from one day to the next is pretty well lost to the eye.
Ohio's crossing of 5,000 cases and then having a lag of about 34 days until there's any significant increase in recoveries is interesting and strongly suggests that's how long their case-resolution time is.
Obviously something weird went on during the week of about April 15 through 21 that adversely affected the number of cases since they shot up by about five thousand more than would have been expected! Someone from Ohio will have to report to us what that might have been! Was there some kind of big public event?!
The trajectory of the number of cases changed again for the worse, though not as bad, on about April 28 or thereabouts.
We would genuinely like to know what might have caused these events. Even if it's just your guess, please email us.
These data were created by taking the daily values for the data used to create the Totals graph above, and comparing with the previous day's value and computing the difference. New Cases and Newly Active cases closely match most of the time and this is because testing is nearly exclusively being done on those people who present themselves for health care. (And, folks, this is definitely "doing it wrong.") However, when people recover (or die), that also has an impact on the daily changes to the number of active cases, and therefore the change from one day to the next which drives how these data are collected.
As we observe with the graph above, there's a big whomping chunk of bad news from about April 15 to April 22. We suspect some religious folks decided to have a big gathering anyway, and around 5,000 new cases were the result, but we really don't know. Please send us your thoughts. ...At the usual death rate of about two percent, whatever it was probably cost around 100 lives.
The purpose of this graph is to show two values also shown on the "Totals" graph above with much better scaling. These are the total deaths and the total recovered cases.
Notably, the big negative spike for Active Cases being precisely timed with the big spike in recoveries illustrates the point made above about "New Active" really meaning "new changes to the currently active cases," and so it can go negative as it does here when there's a larger number of recoveries (and deaths) than new infections.
The "New" lines are all shown with better scaling in the graph above, and are included here for better comparison with total recoveries and deaths.
This was broken out, again, for better scaling because the variation is simply lost at other scales.
As seen in most states, there's an absense of reporting deaths on Sundays by some health-care providers, and that's just where all the down-spikes are.
We hope the huge spike in deaths reported on April 29 was just a reporting anomally - perhaps, say, from their prison system or something like that.
The concept behind "Case Doublings" is to try and get a handle on whether one is winning the war against a pandemic or not. It's driven by how many cases of confirmed infection are recorded. It's simple: From today, looking to the past, how long has it been since we had half as many cases as we have today? That is, today represents a doubling from what day in the past?
One can also look at it in a forward direction but the information isn't as useful beacuse it jumps around lot due to subtle variations in the day-to-day number of reported cases. And, that also tends to obscure the scale. In contrast, looking backwards always gives a stable line because the daily variations shown and discussed above aren't present.
The longer it takes for the number of cases to double, the better you're doing against the pandemic, and the CHANGE in the number of days indicates whether your current actions are helping or hurting. We also think a pertinent timescale is the average time of an infected person's disease to run its course to either recovery or death, presently believed to be about 21 days.
The early waviness of this data illustrates how pre-lockdown, things could have gotten out of hand quickly, but lockdown gave a good start to an increasing line, but there was a hiccup from about the 25th to 28th of March, and a few unsettling squiggles throuh into early April when things got markedly better on April 6. But then something happened, as noted in the other graphs, around April 14 and things took a decided turn for the worse. (On this graph, up is good, down is bad!) But by April 24, things were looking OK unil around May 18 when something has started curving the line the wrong way! Oops!
We suspect people are tired of lockdown there and are violating it, and the result is new cases.
Ohio's own data suggests a 34 day case-resolution timeframe, and if that is accurate, Ohio is presently well short. So, their medical wards are likely filling faster than they're emptying, and this is worysome.
Do let us know what you think or even make requests or ask questions! Please either comment on our main Covid-19 page's comment section, email us, or perhaps, as we're an all volunteer team, donate to our cause.