Our Ebola chart, originally
introduced in Chronology of the Ebola Outbreak in West Africa (
continued, with data points for all three countries taken from the WHO
updates of confirmed cases up to September 2, 2014. In addition,
trendlines are introduced for all three countries out to the end of the
Epidemiologists have developed sophisticated computer modelling
techniques for predicting the course of an outbreak and the likely
effects of various interventions. For our purposes we are looking for a
simple technique to predict the likely expansion of the outbreak if it
continues at its present rate (ie if nothing happens to change the rate
of expansion) and to assess the impact of any public health
interventions in individual countries. For instance, we would expect to
be able to see the impact of the forthcoming three-day lockdown in
Sierra Leone in the charts in the coming weeks. One would expect to see
a spike in the number of cases, as new cases are discovered by the
outreach teams.If these discoveries are significant in number, they
should eventually, assuming proper isolation and contact tracing, lead
to a reduction in the number of new cases being reported. So the trend
of the graph should change in coming weeks, perhaps increasing in slope
and then declining.
The trendlines are developed using Microsoft Word's TRENDLINE feature
and choosing the equation that gives the best fit to the existing data.
This equation is then plotted out to the end of the year. For Sierra
Leone, the selected equation fits the existing data very closely,
giving some confidence that the projection out to the end of the year
is accurate. As mentioned earlier this projection tells us what would
happen if nothing happens to change the current characteristics of the
The chart above shows lab confirmed cases for all three countries as
reported by WHO in their regular updates to Sept. 2. For Liberia, total
cases are also plotted, as a large percentage of Liberia's cases have
not been confirmed by laboratory analysis. The chart shows the very
rapid rise of Liberia's cases to outstrip those of the other affected
This chart gives the trendline for Sierra Leone to the end of the year,
using the equation that best fits the available data. The R2
number indicated in the chart is a measure of how closely the equation
fits the actual available data. When R2
is equal to one the
fit is perfect. In this case a value of 0.99 indicates a very good fit,
giving some confidence that the trend indicated is accurate. The
trendline shows that by the end of the year, if the outbreak remains
unchanged, Sierra Leone will have approximately 4500 lab-confirmed
cumulative cases. All numbers are for laboratory-confirmed cases and do
not include the probably substantial number of unreported cases.
This chart shows the best-fit curve to Liberia's existing data. It
projects around 50,000 cumulative cases in Liberia by the end of the
year. At 0.9879 the fit is not quite as good as in Sierra Leone's case.
Moreover, the very rapid expansion in forecast
cases, from a relatively low actual
base, leaves a large amount of room for error in the forecast.
Accordingly, we plot a different scenario for Liberia below.
This chart is plotted for Liberia using a polynomial trendline rather
than the power series trendline shown earlier. The R2
is substantially lower than previously, indicating this trendline does
not so closely match the existing data. In this less-pessimistic
scenario, Liberia will have approximately 7,000 cases by the end of the
The chart above plots the trendline for Guinea. At 0.9822, the R2
coefficient indicates more deviation from actual data than in the
Sierra Leone case, and this can clearly be seen in the data points on
the chart. The trend indicates Guinea will have about 800 cumulative
lab-confirmed cases by the end of the year, although the trendline
perhaps does not give sufficient weight to a substantial increase in
cases from Guinea in recent weeks.
: As the weeks pass
these trendlines will be compared with actual WHO data and should help
to indicate whether we are winning the battle against Ebola or not.
They should also help us to evaluate the effectiveness of specific
Ebola control measures, such as the three-day lockdown in Sierra Leone.