Posted on 09/19/2014 8:46:26 AM PDT by scouter
Before starting, let me say that I do NOT believe the Ebola epidemic will get as bad as these projections indicate. I believe it will get pretty bad, especially for those in Africa, but not as bad as the current numbers say. I think there will be a number of factors that will significantly mitigate these numbers. How much? I don't know. But no one else does, either. I am not predicting the end of humanity. I'm simply showing where the current numbers lead, should nothing change. But of course, things will change. In any case, I do believe we need to take action now to prepare for the worst, hope for the best, and pray for the those affected.
The following information is presented for the reader's information and discussion. It it not a prediction of future events. As I mentioned when I originally posted my projections, my motivation is out of professional curiosity, the fact that my daughter will be on the front lines if Ebola does appear in the U.S., and because I think the numbers indicate that it is time to take prudent prepatory measures.
Another FReeper, DannyTN, has convinced me that version 1.0 of my model is too complicated, and that one relying more on the concepts of simple compound interest is likely to be more accurate. I've created the Scouter Ebola Projection Model Version 2.0. It has changed in the following ways:
1. Rather than calculating an exponent to apply to the number of cases on a given date, I now use a simple "compound interest" approach in which the Daily Transmission Rate (DTR) is determined using the Microsoft Excel Rate function, as follows:
DTR = RATE(NumDays, 0, NumStartCases, -NumEndCases)
where NumDays is the number of days from the start of the period being examined to the end of the period being examined, NumStartCases is the number of cases on the first day of the period, and NumEndCases is the number of cases on the last day of the period.
The DTR is then applied to the future, using the EndDate of the period selected as the first day of the "future", using the following formula:
ProjectedCases = StartingCases * (1 + DTR)^NumDays
where StartingCases is the number of cases on the End Date of the period being examined, DTR is the Daily Transmission Rate calculated above, and NumDays is the number of days from the End Date.
This allows me to examine how accurate it is by using past performance to project into the "future" and compare that to what actually happened. Using June's numbers, for example, projects that there will be 1,604 cases on August 1. In fact, there were 1,603. Pretty darn good. Using July's numbers projects that there will be 2,971 cases on September 1. There were 3,707. So it underestimated by 736 cases, or about 20% of the actual number. This is probably due to improved reporting. In other words, the number of reported cases in July was probably low.
2. The original model used the case report data as published on the Wikipedia article Ebola virus epidemic in West Africa. I have verified and changed the more recent values, and added additional values, based on my own research. The references for the data will be provided in the spreadsheet when I publish it.
3. I now calculate a separate Daily Transmission Rate for each date for which there is published data. It is calculated for the period of time between that date and the previous date for which there is data.
4. I've included some graphs, displayed below.
5. I now calculate the number of new cases and deaths on the last day of the period being projected.
Many readers have commented on several points that must be kept in mind when interpreting these numbers, and they need to be enumerated here.
1. Obviously the epidemic will not kill trillions of people. But if you project far enough into the future, that's what it will tell you. That's because I have not yet included anything to limit the number of cases. I'm working on that. But in the near case (out until 6 months or so), I don't see that as being a significant factor. But clearly, if the epidemic develops as these numbers suggest, there will come a point when the number of uninfected people in any given area will become significantly smaller, due to death and possible immunity, which will cause the rate of transmission to slow naturally.
2. Some credible epidemiologists and virologists are now saying that it may, in fact, be possible for Ebola to be transmitted through the air, without direct contact with the bodily fluids of an infected person. See COMMENTARY: Health workers need optimal respiratory protection for Ebola at the Center for Infectious Disease Research and Policy.
3. Besides the issues mentioned above, there are many, many factors that can and will affect the rate at which the epidemic is spreading. Some of these will increase the rate, and some will decrease the rate. We don't know how it is going to play out. This is why I'm trying to find a good way to incorporate the change in the Daily Transmission Rate (DTR) in future projections, and why I'm including graphs showing the change in the DTR over time, including trending lines.
4. There are various arguments for which time period to use for projecting into the future. One can argue that the longest interval for which we have data should be used because that smooths out the variability in the Daily Transmission Rate the most. Another argument is that a shorter interval is best because it doesn't matter how we got to the current numbers. Rather, what's important is how fast the epidemic is spreading now. For the numbers presented below, I've chosen a path between those two extremes. The period of time on which I based the DTR for the numbers below is August 1 through September 14.
5. Experts with front line knowledge of the current epidemic have testified before Congress that the reported numbers only represent 25% to 50% of the true size of the epidemic.
6. I do NOT account for variability in the accuracy or completeness of reporting, or for the possibility of bias in reporting. Garbage in, garbage out.
7. I do NOT include the cases of Ebola in a separate, unrelated outbreak in Congo.
I hope to publish the spreadsheet so you can make your own projections based on the parameters you are interested in. I've worked out how to sanitize it and host it. But I'll have to pretty it up first.
So here are the revised numbers, based on the most recent data and my revisions. It assumes that all actual cases have been reported. Graphs follow the numbers.
Scouter Ebola Projection Model Version 2.0 - Ebola Case Projections
*********************************************************
Projection Parameters
*********************************************************
Run Date/Time: 09/18/2014 at 23:02:18
Model: DTR Model
Start Date: 8/1/2014
End Date: 9/14/2014
Reported cases represent 100% of the true epidemic size
Rate of Increase per Day: 2.81%
*********************************************************
Weekly for the Next 8 Weeks
Date Cases Deaths Daily New Cases Daily New Deaths
========== ==================== ==================== ==================== ====================
09/14/2014 5,418 2,589 148 71
09/21/2014 6,576 3,143 180 86
09/28/2014 7,982 3,814 218 104
10/05/2014 9,689 4,630 264 126
10/12/2014 11,760 5,620 321 153
10/19/2014 14,274 6,821 390 186
10/26/2014 17,326 8,279 473 226
11/02/2014 21,030 10,049 574 274
End of Month for the Next 2 Years from the End Date
Date Cases Deaths Daily New Cases Daily New Deaths
========== ==================== ==================== ==================== ====================
09/30/2014 8,437 4,031 230 110
10/31/2014 19,898 9,508 543 260
11/30/2014 45,648 21,813 1,246 595
12/31/2014 107,662 51,446 2,939 1,404
01/31/2015 253,922 121,337 6,932 3,312
02/28/2015 551,157 263,371 15,046 7,190
03/31/2015 1,299,911 621,165 35,486 16,957
04/30/2015 2,982,161 1,425,030 81,410 38,902
05/31/2015 7,033,459 3,360,950 192,005 91,750
06/30/2015 16,135,646 7,710,444 440,485 210,486
Note: The negative "increase" in the above chart is due to reporting corrections made in the data by the reporting agencies. The straight lines indicate the trends in Daily Transmission Rate and Fatality Rate.
1. The first indication of Ebola in more civilized parts of Africa (Egypt, South Africa, Morocco.)
They are more civilized in an “in a world of the blind, the one eyed man is king” sort of way.
1) I am clear that I am not making ANY predictions. I am only projecting the numbers out into the future based on past performance, and assuming a stable rate of transmission. I'm explicit and clear about that.
2) I make your general point myself. There is no way to predict how it will behave in a more advanced country. My personal belief is that it will spread quickly, but not as quickly as in Africa. But there's plenty of evidence and logic to argue the other way, including how other epidemics have spread in the U.S.
While this article seems a little overly gentle for my taste, it does describe some important differences between Africa and here.
“Modern Plumbing: The Answer to Ebola epidemic”
http://www.ushealthworks.com/blog/index.php/2014/09/ebola-epidemic/
Poe's "The Masque of the Red Death"
Yes! I was trying to word that carefully. South Africa has slums, like every country. But they also have very “westernized” cities and facilities. Perhaps that is one of the places we will can examine how the virus works in a more sanitary environment.
I look at Cairo, for example, of how would act in a HUGE city that is connected through the Med to Europe and the Levant.
At this point I think examining the process and trying to consider the different impact points to come up with some points where the signals go from clear to concerned to really concerned to “I am heading for the hills.”
The debate, if kept “reasonable” is fascinating to me.
The doctor makes a some good points, but I agree, I think he's minimizing the threat. The very stomach flu he cites as an example of the superior hygiene in the U.S. is likewise an example of how fast epidemics can spread despite such infrastructure. And there's a huge difference between cleaning up after a vomiting session from a non-fatal stomach virus, and cleaning up after explosive diarrhea, vomiting, and bleeding that covers the walls of the room, as with Ebola.
I don’t disagree with the premise.
However, what scares me the most are the people who are so sick they cannot make it to the bathrooms in their homes. They will infect their housemates (families, caregivers, friends, etc.)
If you have ever been in a skilled care nursing facility when one of those “bugs” sweeps through, causing a lot of “gastro sickness”, you know what I mean. And those are skilled nursing facilities. Imagine what would happen in a tenement in the Bronx.
The part that frightens me the most is the lack of hospital facilities to deal with a pandemic. My wife works at a pretty decent sized hospital, and they would be working out of tents and setting up old style “wards.”
I honestly do not think it will come to that. I think it will get stopped before it jumps in Africa. But, being prepared was drilled into me when I was a kid. And risk management was drilled into my as a professional.
There was a Redbook article about Aids back in the early 80’s making the case that “Women are a natural firebreak to AIDS”. He then went on to explain why.
The CDC vilified the author. They said, and I paraphrase, “What he said is true, but it gives women a false sense of low risk and will increase the number of cases, so he should not have made that information public.”
Another good one.
Hope you are well.
There were times when I thought life boring,
now I just wish it was.
At this point I think examining the process and trying to consider the different impact points to come up with some points where the signals go from clear to concerned to really concerned to “I am heading for the hills.”
My 32 acres:
http://s409.photobucket.com/user/robbbb4/slideshow/Kentucky%20home
That was a lot of work, thank you for sharing.
I have only one question did you or is there even a way to take in account situations in which because of the death rate in combination of the lack of and deterioration of health service allows for both the increase spread and even higher death rate because of the lack of support treatment?
Depends a lot on mode of transmission.
If it is airborne, our sanitation and health care system won’t be of much help.
If it is passed by sweat, it could tear through an urban area in the summer, especially areas that depend on public transportation and plastic seating.
If it is fecal/oral then yes our sanitation system will provide a lot of protection.
As far as health care, it could swing either way, currently, we have no way of treating a mass outbreak, and a trip to the ER could be the primary source of exposure.
Mine is only four acres in Vermont. But “getting there” might be the problem. I am sure there are plenty of folks who will be concerned with any “migration.”
That is why I am working on the “trip wire” scenarios. I am also thankful I have close ties to the local Emergency Management Directors. (I figure I will get an hour or so head start!)
I bought it two weeks before the 2008 election and moved there three years ago. It took a while to get plugged into the IT community around here. I come home to paradise every night.
I call it “The Garden of Eden, but with more chiggers.”
Is that near the George Washington NF? I drove through that area this summer on my motorcycle (on my way to CA.)
What beautiful country.
Our biggest threat will be our government. I'm keeping my eyes and ears open, and will self-quarantine for up to a few months if it does seem prudent. I do not believe the gov would do all that's necessary. Because of the economy and that they'll want to reassure the public by keeping things running normally, the disease would spread.
When it leaves Africa, the disease probably won't be as effective. It spreads in those conditions. I'd think that refugee camps and situations in war-torn countries, and those with third world living conditions are where the real danger is.
Not impossible that it'll spread here, but the models don't factor in how conducive an environment is for spreading the disease.
I did not take that into account. But I think it is at least partially included in the Daily Transmission Rate (DTR), since the DTR was calculated based on the actual spread of Ebola under the conditions you describe.
I think a bigger limitation of the model is that it relies on reported cases, and there is plenty of evidence, and even testimony before Congress from people who would know, that the reported cases underestimate the true size of the epidemic by a factor of at least 2, and possibly as much as 4.
My spreadsheet allows me to assume any percentage of underreporting, but I chose not to publish those numbers, since no one really knows. The numbers, based on the reported cases only, are frightening enough.
I plan to publish the spreadsheet once I pretty it up and finish updating all the references for the sources of data. You can do those calculations then, if you want.
Another big limitation of the model as it stands now, is that it doesn't account for the decrease in the number of "available victims" as the population in a given area dies out. I'm pondering that one.
Can you show the graphs on a semi-log scale too, if you get a chance?
It’s pretty much dead center in KY. And it is a motorcyclist’s paradise.
This video really captures the beauty of this area. It’s from a local Model T club:
https://www.youtube.com/watch?v=DIIhwE8Yo2Y
Some of those unstriped roads are the ones I commute on every day in my FR-s.
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