I have been posting my daily COVID-19 updates here, on my personal blog. But it now makes sense for this to have its own space. So I will be posting all future daily updates there. If you have been following me here for those updates, I recommend subscribing over there:
There have now been 25,417,886 confirmed cases of COVID-19 world wide. That's 0.33% of the world population. According to WorldOMeter, 6,840,933 of those cases are "active". However, as we discussed in the last update, WorldOMeter's recovery estimate is too low. I estimate that there are currently 4,395,451 confirmed cases that are still "active'.
However, how many people have actually been infected? Many people who are infected are never tested, and never become a "confirmed" case.
One way to estimate infections that I have used before, is to use the number of deaths, and the range of possible infection fatality rates to get a range of possible true infections (and assuming an average 20 day lag between infection and death).
This estimate will only be as good as the approximations to the Infection Fatality Rate (IFR), and the reliability of the death data used. I am currently using the range of IFR estimates given by the CDC.
If we apply this to the World-Wide data, these are the results:
We can see that the number of true infections inferred in this way is significantly larger than the number of confirmed infections. But it's also worth pointing out that this is possibly an under estimate, because it is highly likely that the true death tole world wide is under-reported, especially in less developed regions.
We can then apply the same formula we used last week for estimating recoveries to turn the cumulative number infected into an estimate of the number of active cases, those currently infected:
By this estimate somewhere between 0.15% and 0.3% of the world's population are currently infected with SARS-CoV-2, the virus that causes COVID-19.
Both of these estimates seem unreasonably low. Certainly it is significantly lower than we see in Europe or the US (although the US should hardly be held up as a model of success). Certainly a large portion of the world's population live in China, and they have done remarkably well at limiting the virus' spread.
Nevertheless, it seems likely to me that the true death tole from COVID-19 is significantly under-reported world wide.
It can be very helpful when locations report either cases by date of symptom onset, or deaths by date of death, rather than by the date the case or death was reported/recorded. This can give us a better idea of the true shape of the curve... but it also has a lag to it, that always makes it look like cases/deaths are going down while we wait for these cases/deaths to be reported.
When modeling this lag, it's important to have good data on what the lag has been historically. For Florida, there is an excellent archive of historical information showing how the data has come in over time, available here:
https://github.com/mbevand/florida-covid19-deaths-by-day
This is what that delay looks like for Florida:
The proposed fit is 1-e^(-0.1513x).
It's been over a month since peak cases back in mid July, but the recoveries have not yet caught up. But they should have by now. And if this was wrong, it would be impacting everything I did with global trends... the % growth rate, doubling times, etc.
So just as a sanity check, I implemented a common "recovery estimation" algorithm (used for a while by Texas). That is, I took the number of cases-deaths, and I assumed that 80% of those will have a mild case, and will recover in 14 days, while 20% of those will have a more severe case and recover in 32 days.
(Note, I am not looking for "long haulers" who seem to have a lingering immune over-reaction that persists and causes sometimes debilitating problems long aver the virus itself has left their system. Rather, I am looking for the number of people who have cleared the virus and are therefore no longer contagious, regardless of long term impacts.)
As you can see, this approach paints a far more optimistic picture of what is happening. I also think that it is more likely to be correct.
If we use these estimates of recoveries to estimate the daily change in active cases, we get the following:
Again we see that my estimates look very different from those of WorldOMeter, and, they paint a much more optimistic picture of what is happening. Right now, my estimate shows that the number of active cases is declining (the change is below 0) while WoM's estimate for recoveries has the number of active infections still growing (above 0).
If we use these new recovery estimates to calculate the % growth (in active cases) this is the result:
I highly suspect that right now more people are recovering each day than are being newly infected each day. Which we would not suspect if we just naively used WoM's recovery data.
Some good news.
The world wide daily change in new cases now appears to be roughly flat in both the 7 and the 14 day averages:
For the last month we have been watching three countries that initially did very well in responding to the virus, namely South Korea, New Zealand, and China. They each used a different approach, but all three initially succeeded in handling the pandemic in a way that most of the rest of the world has failed to do. New Zealand even completely eliminated all local transmission for over 100 days, and completely reopened.
However, near the end of July/start of August, all three have seen a second resurgence of cases. It will be important to watch and see if they are capable of controlling the virus again, if so, it will demonstrate that their initial success was not a 'fluke', and that it was something the rest of us COULD have done, had we chosen to.
Cases in New Zealand appear to be declining. Their response to this failure at their boarder which allowed the virus back in has been phenomenal so far. If this continues, it seems likely that New Zealand will soon be virus free again, and can again reopen.
When they do, they will show the world that the best response both economically and from the perspective of public health is to "go early, and go hard."
China is also seeing a steep decline in the number of daily cases, which is now well below 100/day.
I am now automatically generating several figures each day and pushing them to an online "git" repository. I have had two volunteers offer to work on creating a web page that will provide a more useful interface to the figures/data. But for now, you can browse the figures with this link.
There are several things available so far.
First, I am running the rt.live algorithm on the US as a whole (which they don't do). There are some good reasons they don't do that (the US is a diverse place, and some areas are doing better than others). But for those interested in what their algorithm has to say about the US as a whole, I now provide that visual updated every day.
The first truly confirmed case of reinfection has now been reported from Hong Kong. Previous reports of reinfection may have been due to false positives, or to a test picking up on dead viral material, or from a resurgence of the previous infection.
However, in this case, we know it's real. The RNA of the virus was sequenced from both infections, and it is clear that the second infection was both real, and different from the first.
I have seen quite a few news stories, and/or posts on social media with people freaking out.
What does this really mean? And should we be panicking?
First, it doesn't mean that the virus has mutated enough to reinfect people. The mutations were enough to use RNA tests to show this was a different strain, but we have been tracking mutations of the virus, and using those to track the family tree of the pandemic from the start. There is no evidence (yet) that the virus is mutating in ways that help it to avoid immunity (like the flu does).
Instead, it would appear that the man's natural immunity simply decreased to the point where he could be reinfected. This is how most other corona viruses (like the corona virus versions of the common cold) end up re-infecting us. Meaning this was expected. The reinfection took place after four and a half months from the time of initial infection, meaning immunity likely lasts at least that long, and most likely longer on average, since this was the FIRST confirmed case like this so far.
That we lasted this long is GOOD news.
What is even more exciting was that his second case was completely asymptomatic. Meaning, that his immunity wasn't actually gone. While it didn't prevent him from being reinfected, it DID prevent him from getting ill with ANY symptoms the second time through.
This is also good news, this is VERY good news.
We also know that many of the vaccines currently in development appear to be producing a stronger, and longer lasting immune response than the disease itself.
While this does mean that natural herd immunity is a losing strategy, we already knew that. By the time we could conceivably anything close to herd immunity levels of population immunity... that immunity would be going away for many, and things would just start over. Although the disease would likely be less deadly to may the second time around, this would make it impossible to prevent the elderly and/or vulnerable from catching the disease at least once, and that would mean an unacceptably large death tole.
However, with a vaccine, things look very different.
While this case of reinfection may mean that we will need a booster at 6 months, it does not mean that a vaccine would not work. And, with a vaccine, we could actually reach (and maintain) herd immunity levels such that we could allow people who are elderly and/or vulnerable to go back out into society with minimal risk of getting infected.
The news articles sound scary... but I believe that this isn't actually bad news.
Today is a model discussion day. I will give a pandemic status update tomorrow.
I have been working recently on automating the process of gathering data every morning. Frankly, I'm surprised it took me this long.
Previously I had been moving data to my spreadsheet from various sources by hand. For those interested, here's a link to my spreadsheet, with all my old data.
As nice as it was to have all the data (and figures) in one place, where everyone could look at it, and intuitively see how I did any calculation... as I began to track (and correlate) a larger set of data, this became unwieldy, and simply took too much of my time.
I spent yesterday and this morning writing python code to automate this process.
For those interested, the code is available on github: https://github.com/jlc42/JLC-COVID-19-Tools
This is a very preliminary work in progress. Currently, it has scripts to gather data which I ca run each day. I still need to write some code to automatically parse some of the web pages I scrape. But at least the data is saved. (Some date, notably from Georgia and Texas goes away if you don't scrape it daily. Some of that data is saved each day by CovidTracking, but other bits are simply lost if I don't scrape it myself daily, namely the testing data on antigen tests or serology tests. Texas puts that in their spreadsheet, but only has the "daily" value for the antigen and serology tests there... so yesterday's numbers are 'wiped out' by tomorrow's numbers.)
I can now put that on a chron job, and have it run every night while I am sleeping, and the data will all be saved.
Next I need to write some code to go through all the saved data, and organize it into the useful fields I track... that's a larger task, that will take a LOT of time. For NOW, I have it pulling down (and organizing) the US state data from CovidDTracking... and then building a single plot...
So... after two days of work... drum roll please.... here's my single plot, automatically generated for me this morning:
And there it is, in all its glory. Here's the same figure plotted from my spreadsheet:
Side note: things ARE improving in New Mexico, yes Tests are down along with cases, but that is because the DEMAND for tests is falling, as true infections fall... you can tell because the % of the tests that are positive is also falling.
Back to automation...
This single figure may not look like much, HOWEVER, with a simple loop, tomorrow I will be ready to produce this figure for EVERY state in the US.
And with a little bit of additional work, some parsing and gathering of data from my other sources, combined into a single data-file, I will be able to do this for every country as well.
The case data and testing data is already available from other sources, but the next step will be to do some of the unique calculations I sometimes do, like my estimates of the % infected, and % currently infected, etc. And that's where the real potential lies. At that point, I may want to think about hosting this somewhere where people can see updated estimates for all these quantities for whatever location they are in.
Yesterday was Groundhog's Day, the holiday where everyone waits with baited breath for a rodent to decide if it saw its shadow, and ther...
