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The World Health Organization, Samaratins Purse, Doctors Without Borders, and other international medical emergency relief programs are desperately calling for additional resources in the international fight against Ebola that has already killed thousands and is likely to kills thousands more even if a full arsenal of aid was made available.
 Figure 1: The lines are projected values while the points are data points.

The World Health Organization released a statement on August 28th that the epidemic could afflict more than 20,000 people before it could be brought under control. This however, assumes full international backing for an intervention to control the deadly outbreak. Failure to fully support the WHO’s plan presumably would cause the disease to continue to spread in a similar manner as it already has.

At first a figure as high as 20,000 seems exaggerated especially when looking just at the number of 3,000 cases reported the same day as the announcement. However, I believe that this estimate is vastly too small and is entirely based on an effective and well funded international relief mission. Using a projection from all of the WHO reports to date I calculate that if the disease continues to spread at the rate it currently is then we will have more than 20,000 cases by October 24.

The report also states that it will likely take six to nine months in order to stop the epidemic. However if the epidemic if nothing changes and the epidemic continues to rage as it currently does then my projects estimate that as many as 4.7 million people will have been infected and 1.2 million will have already died.

These are extremely dire predictions and I hope that they are purely extrapolations based on what will later be seen as the scariest month of this epidemic. However, the exponential growth model fits the data very well and at least in the short term should be expected to be fairly accurate.

All of this analysis is done in R and the code can be found on Github.

From 41 CDC Ebola reports I have assembled a small database of cases by country listing the number of ‘Suspected or Confirmed Cases’, the number of ‘Deaths’ suspected to be associated with Ebola, and the number of ‘Laboratory Confirmed’ cases of Ebola. You can find and add to this database as a google spreadsheet here. If running the code for yourself it will import the spreadsheet data directly.

Mapping this data by country and fitting a local polynomial regression to give a fitted line for each country gives us some signs of a very disturbing trend. The country in which the current outbreak originated is Guinea and though the disease continues to claim new victims it is much less worrisome compared with Sierra Leone and Liberia where rates of suspected cases and numbers of deaths are exponentially growing.

 Figure 2: The increase of deaths in Liberia is much steeper than the other two heavily afflicted countries of Guinea and Sierra Leone.

 Figure 3: The increase of laboratory confirmed cases in Liberia is much less steep than the increase is deaths indicating that the poor medical infrastructure is not able to keep up with the number of diagnoses demanded.

 Figure 4: The increase of deaths in Liberia is much steeper than the other two heavily afflicted countries of Guinea and Sierra Leone.
By exponential growth, we mean that whatever the current number of infected people are, we can expect them to infect some additional number of people proportion to the transmission rate. The problem with exponential growth is that while the inclusion of new victims can initially start out small the more victims there are the more are likely to be added to the victim pool each day.
 Figure 5: The total number of cases is rising extremely quickly.
When we look at the total numbers of each case summed across country we arrive at the above graph.
From this graph it is clear that a direct linear model cannot fit well at all. Suspecting that the change over time might fit an exponential growth function, I take the natural log of the values mapped above.
 Figure 6: A log transformation of the total number of cases creates a relatively linear relationship between time and number of cases reported.

This new transformed graph demonstrates an extremely distributing confirmation that using an exponential growth model would be an appropriate way of modelling the spread of Ebola. In order to estimate the spread of Ebola I define a simple model with a constant and a linear relationship between days since the outbreak was announced and the log of the measure we are concerned with:
$$log(Y)=alpha+beta_1 Day$$
And estimate the model using weights to weight the data based on the number of days into the survey so that more recent observations are considered more important. I also discard the observations for the first 21 days because we can expect the preliminary data at that time was less accurate. Using the above model gives:

           Intercept          Day
Suspected  4.38881946  0.02245505
Deaths     4.00491144  0.02096758
Laboratory 3.86052949  0.02314866

While intercept estimates are generally considered to be less important the coefficients on Day can be directly interpreted as percent changes by day. Thus we can expect from the current data that each day we will have a little over 2% additional suspected cases, deaths, and laboratory confirmations.

In order allow for the model to be a little more flexible in my projections I include a slightly more complex model including a squared term for the days since announcement.
$$log(Y)=alpha+beta_1 Day+beta_2 Day^2$$
I use this model to project suspected cases, deaths, and laboratory results for the next three weeks. The values up until today show the comparison between the expected values estimated from the model (EDeaths, ESusp, and ELab) with that from the data (Death, Susp, and Lab). We can see the model fits the data quite well with all estimates within 100 of the observed while most are much closer. Using this model we can see that the total number of deaths is expected to be around 3,500 by the 24th and 7,200 suspected cases. Things just get worse from there.

          date day     Deaths EDeaths  Susp ESusp     Lab ELab
1   2014-03-25   1         59   89       86  140        0   49
2   2014-03-26   2         60   90       86  141        1   50
3   2014-03-27   3         66   90      103  143        4   51
7   2014-03-31   7         70   94      112  149       24   56
8   2014-04-01   8         80   95      122  151       24   57
9   2014-04-02   9         83   97      127  152       35   59
14  2014-04-07  14         95  102      151  161       52   66
17  2014-04-10  17        101  106      157  167       66   71
24  2014-04-17  24        122  115      197  182      101   83
28  2014-04-21  28        129  122      203  192      109   91
30  2014-04-23  30        136  125      208  197      112   95
37  2014-04-30  37        146  137      221  218      126  112
42  2014-05-05  42        155  148      231  235      127  126
51  2014-05-14  51        157  169      233  270      129  155
60  2014-05-23  60        174  196      258  315      146  190
65  2014-05-28  65        191  213      290  344      170  214
70  2014-06-02  70        199  232      341  377      186  240
73  2014-06-05  73        222  245      425  399      238  257
78  2014-06-10  78        244  269      462  440      253  289
79  2014-06-11  79        261  274      494  449      277  296
86  2014-06-18  86        337  313      528  517      364  348
92  2014-06-24  92        338  353      599  587      441  399
100 2014-07-02 100        467  416      759  700      544  481
105 2014-07-07 105        481  462      779  784      557  540
106 2014-07-08 106        518  472      844  803      626  552
112 2014-07-14 112        539  539      888  925      664  634
114 2014-07-16 114        601  564      964  971      706  665
122 2014-07-24 122        632  677     1048 1183      745  800
126 2014-07-28 126        672  744     1201 1311      814  877
129 2014-07-31 129        728  800     1323 1417      909  941
132 2014-08-03 132        826  860     1439 1533      953 1008
133 2014-08-04 133        887  882     1603 1574     1009 1032
137 2014-08-08 137        961  974     1779 1753     1134 1132
141 2014-08-12 141       1013 1077     1848 1956     1176 1242
142 2014-08-13 142       1069 1105     1975 2011     1251 1271
144 2014-08-15 144       1145 1163     2127 2127     1310 1332
148 2014-08-19 148       1229 1290     2240 2381     1383 1461
150 2014-08-21 150       1350 1360     2473 2522     1460 1530
151 2014-08-22 151       1427 1397     2561 2596     1528 1566
157 2014-08-28 157       1552 1641     3069 3094     1752 1800
166 2014-09-06 166            2106          4062          2218 Today
167 2014-09-07 167            2166          4189          2270
168 2014-09-08 168            2228          4321          2323
169 2014-09-09 169            2292          4457          2378
170 2014-09-10 170            2359          4599          2433
171 2014-09-11 171            2427          4745          2491
172 2014-09-12 172            2498          4897          2549
173 2014-09-13 173            2572          5055          2609
174 2014-09-14 174            2647          5218          2670
175 2014-09-15 175            2725          5386          2733
176 2014-09-16 176            2806          5562          2797
177 2014-09-17 177            2890          5743          2863
178 2014-09-18 178            2976          5931          2930
179 2014-09-19 179            3065          6126          2998
180 2014-09-20 180            3157          6329          3069
181 2014-09-21 181            3253          6539          3141
182 2014-09-22 182            3351          6756          3215
183 2014-09-23 183            3453          6982          3290
184 2014-09-24 184            3559          7217          3367


Falseness of my Model
This model by definition globally (into the distant future) cannot be true. This is obvious when we use the model to project out to one year. At one year the number of infected cases is estimated as 436 billion. Since the entire population of the Earth is only 8 billion or so we know that this cannot be true.

However, this kind of model can be a good approximation locally (in the near future). If it is a good approximation locally then the next WHO report is going to list around 2100 deaths and 4060 suspected cases as of today.

So, I ask the question: is 1.2 million deaths a projection which is either local or global. I cannot answer that but it certainly is within the realm of feasibility since the nation of Liberia alone has over 4 million people and Guinea 10 million and Sierra Leone 6 million. The real question becomes, do we think the ability of Liberia and other afflicted nations to control the spread of Ebola will increase, decrease, or remain the same over time?

From Figure 3 we can see that Liberia is significantly behind other nations in its ability to diagnose Ebola. This and the well known lack of medical facilities suggests to me that as the crisis escalates the ability of Liberia to maintain any sense of order and with it any hope of controlling the spread of the disease is likely to degrade. If this is the case then it is quite possible that even this horrifying projection is an underestimate of the pain and carnage likely to result from this outbreak.

What to Do

News reports and the governments they are reporting on seem to have been placing a good deal of emphasis on investing in vaccines and treatment options. However, while all of these options are good, they are long term options (6 to 12 months).  In the meantime, every resource available must be used to contain and restrict the spread of this outbreak.

It is extremely foolish to think that any nation is immune to this disease. So far in the entire history of Ebola outbreaks up until the present less than 10 thousand people have been infected. This relatively low infection count coupled with rapid mortality makes it unlikely that the disease will significantly mutate among the human population.

However, if my projections are anywhere close to accurate then the number of infected people are going to be much higher than has ever occurred previously. This will create many more habitats for which the virus can possible mutate new traits which could increase its transmission rate. These mutations could take the form of longer gestation periods which might lead to a greater time between being infectious and being detectable.

Another possible trait might be the ability to go airborne which would significantly increase its ability ability to be transmitted. Some scientists it very unlikely to become airborne because it is too heavy. This may be the case. However, as the possibility of it becoming airborne could result in a global spread of the disease resulting in unprecedented number of deaths world wide it is more than prudent to heavily invest in controlling the number of new patients infected by this disease.

In addition, even if the disease does not mutate from the state that it is in currently to a new one, it has shown itself to be extremely effective at being transmitted with a large number of health workers becoming infected and dying from the disease. These health workers should have known how to control the spread of the disease and prevent infection. Do we really expect that if the disease were to enter any other nation on Earth that the general population is going to be better prepared to protect themselves than the specialists who have already fallen victim to this disease?

Thus, it is imperative that we do everything within our power to control the spread of this terrible disease. Even if my model only has a ten percent chance of being accurate over the next six months, we would be extremely foolish to risk not responding to this outbreak with every resource within reason humanity can muster.