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| Credit NIAID |
#12,869
Despite the extraordinary work of thousands of very smart researchers around the world - much of which I try to cover in this blog - we've an astonishingly bad record of reading the tea leaves and predicting the next big public health threat.
- A decade ago, while we were watching H5N1 burn across Indonesia, Vietnam, and China we were blind sided by a North American Swine H1N1 pandemic.
- In 2013, while we concentrated on H7N9 in China and MERS-CoV in Saudi Arabia, the worlds first urban Ebola epidemic began to devastate West Africa.
- And in 2015, while both MERS and Ebola still had our attention - Chikungunya, followed by Zika - took the Americas by surprise.
Virologists - using everything from cutting edge reverse genetics to old-school serial passage studies - manipulate and push viruses in order to see their pandemic potential, while epidemiologist go through tons of shoe leather tracking down and mapping the of spread pathogens to better understand how they work.While our overall knowledge of these human health threats has increased greatly due to their combined efforts . .. so far at least . . . our ability to reliably predict the next pandemic - or which viruses look threatening but will ultimately fade away - remains an elusive goal.
All of which brings us to an opinion piece, published this past week in Open Biology, which argues the reservoir of viruses is too vast, their evolution occurs over too great a time scale, and their interconnections are too complicated, for current methods to solve these riddles.
After reviewing existing avenues of research and finding them wanting, they argue that a more practical approach is needed. One that focuses primarily on virological surveillance at what they dub the `fault-line' of the human–animal interface and in regions of ecological disturbance, in order to predict the emergence of viral threats.Even with this new approach, they freely admit `that any attempt to predict what virus may emerge next will face substantial, and probably crippling, difficulties.'
This article provides a fascinating overview the enormity of the task. While their approach provides excellent food for thought, I imagine some scientists following other, more traditional research paths will take some degree umbrage over their conclusions.
I've only included some excerpts, so follow the link below to read it in its entirety.
Predicting virus emergence amid evolutionary noise
Jemma L. Geoghegan, Edward C. Holmes
Published 25 October 2017.DOI: 10.1098/rsob.170189
Abstract
The study of virus disease emergence, whether it can be predicted and how it might be prevented, has become a major research topic in biomedicine. Here we show that efforts to predict disease emergence commonly conflate fundamentally different evolutionary and epidemiological time scales, and are likely to fail because of the enormous number of unsampled viruses that could conceivably emerge in humans.
Although we know much about the patterns and processes of virus evolution on evolutionary time scales as depicted in family-scale phylogenetic trees, these data have little predictive power to reveal the short-term microevolutionary processes that underpin cross-species transmission and emergence.
Truly understanding disease emergence therefore requires a new mechanistic and integrated view of the factors that allow or prevent viruses spreading in novel hosts. We present such a view, suggesting that both ecological and genetic aspects of virus emergence can be placed within a simple population genetic framework, which in turn highlights the importance of host population size and density in determining whether emergence will be successful. Despite this framework, we conclude that a more practical solution to preventing and containing the successful emergence of new diseases entails ongoing virological surveillance at the human–animal interface and regions of ecological disturbance.
(Very Large SNIP)
7. Conclusion
Predicting virus emergence has risen to become a key goal of the study of infectious disease. The study of virus evolution has revealed much about the nature of virus emergence and its history over evolutionary time scales. However, due to the fundamental differences between evolutionary and epidemiological time scales, a focus on virus evolution may in fact be a distraction when it comes to predicting the next virus pandemic.
Similarly, while virological features that increase the likelihood of virus emergibility can be identified, these features cannot be treated as hard and fast rules determining which viruses will successfully emerge. Further, many of these features are only capable of being observed after emergence occurs, such that they are likely to be of little predictive power.
In partial response to these problems, we suggest that the field may be advanced by using a population genetic framework that melds genetic and ecological studies of virus emergence, and which highlights how the effective susceptible population size of a new host plays a major role in dictating the chance of successful emergence.
In this manner we identify the possibility of a meaningful theoretical framework for the study of emergence that is grounded in evolutionary theory, but that tunes out the ‘noise’ of virus macroevolution.
Despite such a framework, the inconvenient truth for all those working in the realm of disease emergence is that the vastness of the unknown virosphere and the diverse range of viruses that have achieved endemic transmission in humans means that any attempt to predict what virus may emerge next will face substantial, and probably crippling, difficulties.
In light of this we suggest it may be of more benefit to public health to target, via surveillance, the fault-line of disease emergence that is the human–animal interface, particularly those shaped by ecological disturbance. Once a virus is identified as being of interest in this manner, other analyses may be able to assess its impact and pandemic potential. Such a shift in focus, away from being able to make predictions of emergence based on fundamental rules and towards the better assessment of emergence impact, is both more achievable and more likely to provide positive public health outcomes.
(Continue . . . )
