This morning I awoke to find the fruits of his labour generously presented to the world via Twitter.
I'm constantly impressed by how much info can and is being provided for everyone to share, discuss and constructively mull over. This is just the latest fantastic effort.
Prof Fisman's (@DavidFisman) model has provided a very close estimate when compared to the real figures on which it is, of course, based (Figure 1.). His estimates have not changed with the latest data. He calculates an overall R0 of 1.75, and 'd' (a value that can indicate the level of control; when d is zero, you have uncontrolled exponential growth) is at 0.0078. The d values for different countries in the outbreak, differ.
|Figure 1. Showing that the model (black line) fits extremely well |
to actual reported case numbers (red bars) to date
|Figure 2. Extending the model into 2017.|
Red curve (right y-axis): incidence by 15-day generation.
Blue curve (left y-axis): cumulative cases.
|Figure 3. Ebola virus disease cumulative curve for Nigeria.|
The proportion of fatal cases is markedly lower than for
the more overwhelmed countries. This does
not appear to be an artefact as most cases have
been laboratory confirmed.
The addition of that calculation spikes the PFC to >80% at times (see the post by @maiamajumder post on HealthMap), but seems to vary to lower figures depending on country and population for example, in Nigeria (Figure 3). But whatever way you look at it, many people will die from Ebola virus infection, as well as all the other diseases and medical care needs that going with sufficient attention.
- Early Epidemic Dynamics of the West African 2014 Ebola Outbreak: Estimates Derived with a Simple Two-Parameter Model