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.
Nicely done! That’s good information on what we are likely to see, at least in the short term. In the long term, we may add countries to the list, which could be even worse than your numbers.
That is a 2 edged sword though, with us probably having candy assed immune systems compared your average African who’s immune system is probably the physiological equivalent of the Looney Tunes Tasmanian Devil...
I would be surprised that was true, and taking a quick look on the net seems to indicate that it is at least arguable.
“we have a different health care quality here, “
which is still recommending N95 masks
Unfortunately the health care workers wearing N95 masks are dying
don’t count on the U.S. healthcare system pulling your chestnuts out of the fire
Thanks for the ping!
That’s right. The Eyam solution - no one in and no one out.
Better a depression that dying from this horrid disease.
I think I read an article the other day about a case in Saudi Arabia? Any one remember? Guess there will be more when everyone converges on Mecca.
VRE sweeps through nursing homes like wild fire and leaves plenty of deaths in its wake.
That’s a great article. I wonder what sort of antibiotics they gave him?
Interesting seeing after all this time. Do I still see Washington license plates on the vehicles? lol
I’d be happy riding my little trail bike back on forth on that two-track.
Here in the Tri-Cities no two-track through the woods but generally very good people and agriculture and medical services and lots of Columbia River water — happy with the situation.
Living in a rural area, with our own well and septic plus our normal food supply stock, I’m thinking that our best chance, should it make it here, is to lock the door and stay home for as long as it takes.
Grab an old grocery list and check it for things you might normally pick up that will keep and make sure your supplies are up to snuff...things like toilet paper, hygiene supplies, soap, laundry detergent, and some extra fuel are good, too.
If you do plan to sit a spell, remember to fire up the vehicles once a week and run them for a few minutes to keep the batteries topped off and the seals limber. Listen well for anyone around first...
You’re Welcome, Alamo-Girl!
That point about firing up the cars is a good reminder. We do have neighbors closer than I’d like, but we all have our own wells and septic.
I’m thinking I’m going to stock up on a little more Sodium Hypochloride for sure, and a few other items. Adding stuff to medical supplies this month and next month.
I don’t think it’s the pale horse, but it might be the initial formation making the pale horse feasible. MIght burn iself out at 10000 deaths, but leave dormant cells everywhere to come back alive in a decade or so.
don’t count on the U.S. healthcare system pulling your chestnuts out of the fire
I was born in Eastern Washington. I love it out there. BTW, those pictures were taken shortly after we moved here. That grass was harvested as hay. We have a nice rider mower now. and the deck extends 8’ wider than the house at each end now and I’m in the middle of adding 12 feet too its depth coming out from the house.
Porches are a big deal here. Especially after the sun goes behind the trees, as it is in that picture.
And eastern washington, especially if you have water, is a GREAT place to live, IMHO.
A person with AIDS could harbor the virus for years without ever showing symptoms, and is contagious during that time. Thus AIDS was able to spread widely, especially in Africa (where there are 35 million or so cases).
Ebola is only similar to AIDS in that it needs direct contact with infected fluids to spread, and that the virus is fragile outside of the body.
Ebola is only similar to AIDS in that it needs direct contact with infected fluids to spread, and that the virus is fragile outside of the body.
It's highly unlikely to spread in a developed country. Developed countries have a mechanism in place to isolate patients who come to the clinic with odd symptoms and a travel history.
One of my co-workers told me of a man who got Marburg (which is related to Ebola and causes identical symptoms) while traveling. The staff treating him did not know he had Marburg, and it took a year to identify the disease. No one else got the disease from him.
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