Forecasting when and where Ebola outbreaks will occur is difficult, especially because the ‘reservoir hosts’ of the viruses that cause this disease are not known for certain. There has been a focus on modelling Ebola disease in people and this has informed epidemic control strategies. Less attention has been given to modelling the initial ‘spillover’ events from other species to people, or disease dynamics in reservoirs, because of a lack of data. However, because the wildlife reservoirs and mechanism of spillover are poorly understood, modelling approaches can be used to identify or exclude hypotheses even when data are limited.
The field of phylodynamics hypothesises that the way viruses spread leaves footprints in their genetic information. This opens great potential to provide insights into complex epidemiological dynamics, but in practice, there are many hurdles to be overcome. One of them is that the number of parameters to infer from raw DNA or RNA sequence data is huge, making likelihood-based methods challenging for non-trivial models. A promising alternative is a simulation-based approach called ABC for Approximate Bayesian Computation. Its downside is that it is computationally intensive, since thousands (if not millions) of simulation runs to obtain a good quality model fit. To avoid this, people have proposed all sorts of optimization schemes, such as regression ABC, which relies on state-of-the-art machine learning techniques.
Currently we are faced with two major threats from viral diseases: Over the last 30 years HIV has spread across the world and continues to plague us. Over the last 3 months the hemorrhagic fever caused by the Ebola virus has spread across West Africa killing thousands of people. If we are to contain HIV in the long-run and Ebola, hopefully, in a much shorter time, this will depend on our ability to understand the nature of the threat and the strategies of the disease causing organisms.
The evolutionary origin of Ebolaviruses is not very clear. The simple notion that these viruses have been circulating for many millennia in wildlife in tropical parts of Africa, occasionally spilling over into human populations, often brought on by human activities, may not be correct or at least incomplete. Over time a number of Ebola disease outbreaks reported and a pattern in the outbreak response seemed to have been established. A lot was also learnt about Ebola viruses, their epidemiology and ecology.
However, the 2014 Ebola outbreak challenges our understanding in many respects.
Once more we are hearing about ‘exponential growth’ – popularly some sort of synonym for ‘rapid growth’ or ‘explosive growth’ – but actually a technical term with a quite specific meaning. This time the talk is about the ongoing Ebola outbreak in West Africa, understandably causing increasing disruption (is devastation too strong a word?) in the region, and alarm much further afield.