What lies ahead post-Brexit?

 

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A fridge magnet my daughter uses to tell that ‘she isn’t here’. The question is what fridge magnet we should use for science post-Brexit.

As all of you know, UK voted for Brexit.

In the local village sauna (yes, we have one of those) the reactions ranged from Brexit being stupidity in action, to quotes such as this was EU’s fault for not being on par with the people and that this could serve as a bloody needed wakeup call for the union. I sat mainly silent, stunned by the absoluteness of the decision. Because it is such a major decision, and a decision that have ramifications on so many levels for so many people.

As a scientist, I wonder what this will mean for UK and EU science? Truth is, no one knows. (And the elderly men in my sauna had no answer either). What we do know is that over the years, science and education have become more and more interconnected in the EU as a whole. And that’s a damn good thing. During my career it has become easier to study and conduct research outside Sweden, and for students from other countries to come to Sweden. Actually, after finishing a PhD, one of the most common ways of continuing in science is to apply for EU-funded postdocs (foremost via the Marie Skłodowska-Curie grant programs). I have many friends that have done that journey, and as PI I have had students applying for this funding to come to my lab. Also at the next level, where an aspiring scientist wants to develop his own research group, the EU provides means to do so via the ERC starting grants. Will these opportunities remain for young UK researchers? And what will happen with the opportunities for postdocs from the rest of the EU to do their work in the UK? The UK has been a magnet for talented Europeans for a long time, and it would be a terrible loss if that door, if not closed totally, would be harder to get through. Moreover, what will happen with European-wide calls, such as Horizon 2020 grants? At the moment I am participating on a grant proposal at the second stage where 2 out of the 6 partners are from the UK – will it be considered for funding anyway?

The interconnected science world is manifested also locally, even in a small university such as the one I work at. For instance, I have participated in EU-funded research, acted as an expert in EFSA (European Food Safety Authority) opinions, have had UK colleagues on shared grants, hosted postdocs with roots or training in the UK, and once I was close to actually move to the UK myself. I have met so many talented UK researchers at so many different levels. It is a shame if the ties connecting research between UK and EU will be weakened. It would be a great loss for EU and devastating for the upcoming research generation in the UK if they cannot participate at the same level as other European researchers.

You may say that is doom and gloom talk, that in fact not much will change, that either EU or the UK will make sure that funding and research opportunities will continue more or less as they are now. I hope you are right, but fear that you are wrong. For sure, there will be a backlash if article 50 is invoked, the question is how big and how long-lasting it will be. Because once you’re out, you cannot really continue business as usual. Simply put, why would EU-funds be used to support UK research and infrastructure in the future if not the UK is paying their share, or if EU-researchers cannot move freely to the UK?

It is still early on in the Brexit process, and we’ll have to wait and see what will come out of this mess. If my crowd in the sauna come up with a solution, I will let you know.

Seabirds and flu, a review

A small murre colony on Cabot Island, Canada.

[This post is by Michelle Wille, postdoctoral researcher at Uppsala University]

For those who have visited a seabird colony, you would know that it is a loud and crowded place, with large swaths of the colony covered in guano. It literally stinks of bird poo. If you were to imagine a good host for a virus that is transmitted by the fecal oral route, one could imagine that these conditions would be excellent for transmission. A virus, such as the influenza A virus (IAV).

 

This virus is one of the most important and well-studied avian viruses, especially in its reservoir hosts, the dabbling ducks. However, for seabirds – the majestic creatures that roam the oceans – no real synthesis has been published despite close to 50 years of surveillance. In fact, when I started working on IAV in seabirds, we knew very little about the presence and prevalence of influenza in this group of birds. What we did know was that seabirds were being sampled for influenza – in fact, most bird groups were being sampled for IAV following the highly pathogenic H5N1 outbreaks after 2005 – but we didn’t actually know how seabirds fit into the ecology of influenza. Are they infected? Are some seabirds more important than others? Do they follow similar patterns to ducks or gulls? Are their viruses unique, or more similar to duck or gulls?

 

Antarcric Tern

Antarctic tern

We set out to collate the existing knowledge on IAV in seabirds – a diverse collection of species and are best defined through their shared propensity to spend portions of their lives at sea – and pulled together as much surveillance data as possible from publications and influenza databases to try to evaluate sampling effort in seabirds, and which species play a role in IAV ecology. This review was just published in the journal Avian Diseases. It turns out, scientists have sampled a large number of seabirds over the last 50 years: 41,828 samples from 98 species, spanning 14 avian families in 6 orders. This may seem like a lot of samples, but if broken down it equals only 8.5 samples per species per year. To put it in perspective, from our sampling site in Sweden, 22,229 samples were collected from Mallards between 2002-2009, and it is samples sizes like these that allow us to make stronger inferences on IAV ecology.

 

While this illustrates the lack of effort overall, some seabirds have received more effort and attention. Terns as a group are heavily sampled, although sporadically rather than systematically. Terns are interesting as the first confirmed outbreak of highly pathogenic influenza in wild birds occurred in Common Terns (Sterna hirundo) in South Africa back in 1961. Despite very few isolations of viruses, serology suggests circulation of IAV in terns and noddies and a diversity of virus subtypes – most recently highlighted in the Indian Ocean system. Most interesting, perhaps is the compelling evidence suggesting that Murres/Guillemots (Uria sp.) are hosts for IAV. Research to investigate IAV in murres dates back to the 1970s, and interest in these birds has been renewed with increased sampling effort in the past 10 years. These birds are piscivorous, limited to the northern Holoarctic where they breed predominantly on islands, often on steep cliffs. Within all the seabird groups, the greatest number and diversity of viruses come from murres, with viruses isolated across their range – Russia, Sweden, Greenland, Newfoundland (Canada), Nunavut (Canada), Alaska (USA), and Oregon (USA). Unfortunately there is rather limited serological information in Common and Thick-billed Murre, which would provide a more long-term assessment of influenza dynamics.

 

 

COMU

A few other species/groups have large enough sample sizes to estimate IAV prevalence with confidence, but serology, despite small sample sizes, indicates IAV presence in most seabird species tested. However, more focused work is required to better assess these species as hosts. Regardless, if you are interested in the IAV status of the seabirds you work on – sampling effort and IAV results are presented for all 98 species.

 

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What is the role of seabirds in the epidemiology of low-pathogenic avian influenza?

What was a surprise for us, as we were completing this review, was how little we could say about the role of seabirds in the ecology of seabirds due to limitations in sampling. There is clearly a space to fill for an aspiring IAV researcher. If you want to sample for IAV and be able to draw some conclusions – here are some things to think about:

 

  1. Influenza A in birds is seasonal. Some months the prevalence is high (up to 30%) and some months it is low (>0.00001%). While seabirds are logistically hard to access, temporal and repeated sampling is key.

 

  1. Within an individual, the period of shedding live virus is very short. While longer periods have been detected (up to 14 days), usually birds shed viruses for less than 7 days. This highlights the importance of serology, or assessing the antibody prevalence in a population. This allows us to ascertain whether the population has been infected by IAV in the past, and therefore, whether it is a population to target (if positive).

 

  1. Seabird colonies may have many species, and it is tempting to take a few samples from each species present. Low sample size however limits the detection probability. For example, if prevalence of IAV is about 1% in the population, you need to take well over 100 samples to have a 95% probability of detecting the virus. Putative prevalence of IAV in seabirds is in this 1% range.

 

  1. Maintaining “cold chain” is key. Seabird colonies are logistically hard to sample, and dragging a -80C freezer or vapour shipper may just not seem to be worth the effort. But, RNA viruses degrade rather rapidly, and swaths of negative samples may be false negatives due to poor sampling handling. While it is speculation, perhaps the reason that we are starting to be more successful at isolating influenza from Antarctic Penguins is an improvement in cold chain (who would have through it would be difficult to keep samples at a constant temperature of -80C in Antarctic!).

 

I feel privileged to be writing this piece after recently spending a week working in a Murre colony in Sweden. Seabird colonies really are the best places to be – serene beauty on the steep, the smell of guano-ladened cliffs on (remote) islands, with the flutter of murre wings and peeping of recently hatched murre chicks.

Link to the article:

Andrew S. Lang, A.S., Lebarbenchon, C., Ramey, A.M., Robertson, G.J., Waldenström, J.& Wille, M. 2016. Assessing the Role of Seabirds in the Ecology of Influenza A Viruses. Avian Diseases 60(1s):378-386.

 

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Adelie and Gentoo penguins doing their thing.

Influenza A virus epidemiology – from individual disease histories to disease dynamics

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Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)

Wildlife disease studies are challenging. That’s a fact. If you want an easy science life you should choose another path with more instant results. However, challenging is also the opposite of boring, and the rewards of getting your results are even more exhilarating when lots of toil, sweat and tears have been invested. As readers of this blog are aware, wildlife disease studies are what we do, and I have repeatedly written about our ongoing work on influenza A virus ecology and epidemiology in wild migratory Mallards. This week another study from our study site was published, entitled Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models, which can be found on early view in the Journal of Applied Ecology.

The challenges of studying wildlife disease dynamics are that you want to capture a dynamic process influenced both by the host and the pathogen, which in turn is compounded by variation in the environment – both biotic factors, such as food abundance and the occurrence of other potential hosts, and abiotic factors, such as weather and climate. Disentangling these interconnected effects is a little like making a cube out of mercury. In most wildlife disease studies the available data is at the population level, usually in the form of prevalence rates at specific time points. This type of data is ‘fairly easy’ to collect – you head out into the field, sample all animals you can lay your hands on and then use this snapshot in time as a proxy for the true disease dynamic in your system. The more times you are out collecting data, the better your model becomes. However, disease is driven by factors operating at the level of individuals, such as infection risk and recovery rate, and that type of data can only be acquired by repeated sampling of individuals across a suitable timescale. This is rarely achieved because of logistical, practical and monetary reasons.

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Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)

We, however, sit on a huge collection of Mallard and flu data gathered at the same study site with similar methods over a period of close to 15 years. Our latest paper, headed by Alexis Avril and with collaboration with colleagues in France, utilizes this dataset to develop individual-based influenza A virus epidemiological models. This proved to a monumental task that stretched over several years and burned the processors of a good number of computers. Part of the difficulty can be attributed to the data itself – capture and disease histories for 3500 individuals collected over 7 seasons, where at each capture occasion axillary data on bird age, sex, condition, infection status and weather were included. But also the patchy nature of recapture probability and the short duration of most influenza virus infections contributed significantly to extensive data crunching.

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The conceptual framework in the multistate CMR model.

The method we used was multistate capture-mark-recapture models, which are extensions of models originally developed to investigate mortality rates from census data, but where one can include the infection state – i.e. infected or not with influenza virus – as a factor in the analyses. Interested readers should head over and read the publication, as I will spear the rest of you any hardcore statistics and model lingo. Parts of the abstract serves as a good summary:

 For most years, prevalence and risk of influenza A virus (IAV) infection peaked at a single time during the autumn migration season, but the timing, shape and intensity of the infection curve showed strong annual heterogeneity. In contrast, the seasonal pattern of recovery rate only varied in intensity across years. Adults and juveniles displayed similar seasonal patterns of infection and recovery each year. However, compared to adults, juveniles experienced twice the risk of becoming infected, whereas recovery rates were similar across age categories. Finally, we did not find evidence that infection influenced the timing of emigration from the stopover site.

Our study provides robust empirical estimates of epidemiological parameters for predicting IAV dynamics. However, the strong annual variation in infection curves makes forecasting difficult. Prevalence data can provide reliable surveillance indicators as long as they catch the variation in infection risk. However, individual-based monitoring of infection is required to verify this assumption in areas where surveillance occurs. In this context, monitoring of captive sentinel birds kept in close contact with wild birds is useful. The fact that infection does not impact the timing of migration underpins the potential for mallards to spread viruses rapidly over large geographical scales.

Our findings corroborate much of the earlier works done on IAV in birds from population level data or from infection experiments, but with higher robustness of the conclusions. Importantly, we provide estimates of the most crucial infection parameters and show how they vary in relation to age in different seasons and years. And from a model point of view, we show that MS-CMRs are a potent method for disease dynamic inferences. We hope this paper will be read and cited by people in the IAV field and in general disease dynamic research, and that it will be useful for stakeholders interested in the contribution of wild birds in the epidemiology of IAV in poultry.

Link to the paper:

Avril, A., Grosbois, V., Latorre-Margalef, N., Gaidet, N., Tolf, C., Olsen, B. & Waldenström, J. 2016. Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models. Journal of Applied Ecology, online early.