Flu transmission – a review

Hi there everyone, it is time for ducks and flu again! Just the other week we published a review on how host and virus traits affect transmission of low-pathogenic avian influenza viruses in wild birds. You should check it out – it is freely available in Current Opinion in Virology.

In this piece, Jacintha van Dijk, Josanne Verhagen, Michelle Wille and I synthesized the current knowledge of wild bird/flu interactions focusing on exposure and susceptibility. It is always challenging to write a review, especially when there are restrictions on length. But it is also fun (especially with such a talented team). We identified nine key host traits that can affect transmission: migration, non-migratory movements (e.g. dispersal), foraging, molt, reproduction, age, sex, pre-existing immunity and body condition, and provided the most recent findings from the literature regarding these traits. We also looked at five virus traits that can affect LPIAV transmission: virus stability, virus binding, virus replication, and the ability of the virus to evade the host innate and adaptive immune response. Many of these traits are not mutually exclusive, some have inherent spatial and temporal variation and can be affected by other confounding or unidentified factors.

Compared to many other wildlife pathogens, there is actually quite a lot of studies on LPIAV disease ecology to draw from. Yet, there is a clear need for additional and more integrative studies. You could say there are two sides: one more traditional approach with controlled infection experiment, and one more ecological approach with field samples and observations. Both are good, but neither is perfect. In lab studies, there is uncertainty in how well the experiment mimics the natural situation, and in field studies there are often many uncontrolled factors or results are correlative. We argue that these lines of research should be combined more often, either to use field studies to generate hypotheses to test in the lab with higher ecological realism, or to do semi-natural approaches in the field. A particularly challenging part is to study virus in free-living wild birds. Hopefully, the ongoing developments of remote-tracking could also be used to follow individual birds in the field for assessments of movement ecology, contact rates and other parameters of importance for predicting LPIAV maintenance.

Anyway, since the article is open access I strongly recommend you to click on this link and download the full review.

van Dijk, JGB., Verhagen, JH, Wille, M. & Waldenström, J. 2017. Host and virus ecology as determinants of influensa A virus transmission in wild birds. Current Opinion in Virology 28: 26-36.

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

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.

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.

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.

Do wild birds give you campylobacteriosis?

Blackbird, Turdus merula. Photo from Flickr under a CC BY-NC-ND 2.0 license.

There is magic in large numbers. Most often we scientist – regardless if we are field scientists or lab rats – struggle with acquiring sufficiently large sample sizes for the statistical tests we have set out to do. There are ways to deal with sparse data, but nothing beats a good-looking huge dataset if you want to test your hypothesis with confidence. Moreover, given that every biological system we measure has a degree of uncertainty, so called noise, means that if we are to find effects that are small we need to collect a lot of data.

Earlier this year, I co-authored a publication on Campylobacter epidemiology that really took advantage of large numbers. In this case, Cody et al. investigated if people get campylobacters from wild birds. This is something that has been suspected given the huge impact domestic poultry has – the single largest source of human campylobacteriosis – but not really proven. Over the years, the lab in Oxford has collected an enormous  dataset on the occurrence of Campylobacter jejuni in patients in Oxfordshire, UK. Not only is there a lot of data, each and every clinical case is associated with a genotyped bacterial isolate. That is an awesome treasure trove to investigate.

In this study, 5628 genotyped clinical isolates from Oxfordshire were run in a STRUCTURE analysis to try to associate each isolate with a putative source. The rationale here is that there are distinct sets of C. jejuni genotypes in different types of animals, especially in different species of birds. And as campylobacteriosis is a zoonotic infection with little to non human-to-human transmission such an analysis can indicate the degree of relevance of different sources for human epidemiology.

Did that sound awfully advanced? Perhaps. It really is quite simple. Consider you make a row of bins. Each bin gets a name, such as ‘chicken’, ‘cattle’, ‘goose’, ‘blackbird’ etc. Then you take each bacterial isolate in your hand, scrutinize it and put in a bin that you think it fits best in. A little bit like a sorting box for children. Starshaped objects go into the starshape hole, square objects in the square hole, etc. Except that it in this case it is the degree of resemblance at the genetic level that decides whether an isolate should be grouped with a particular source. The second thing is that you let the computer rerun this procedure over and over again until you get a probabilistic assignment to each bin.

The principle of STRUCTURE analysis.

In this paper, it was shown that the proportion of clinical isolates from Oxfordshire attributed to wild birds was 2.1%-3.5% each year. That is way lower than the values for chicken products, but given the very high incidence of campylobacteriosis in the human population it still means a large number of actual infections caused by bacteria that normally are found in wild birds. Which wild birds, you may ask. Primarily thrushes, is the answer – at least in Oxfordshire. The blackbird and the song thrush are two common garden birds that like to live close to us humans. Looking at the seasonal variation, the analysis showed that wild bird associated campylobacteriosis cases was more common during the warmer months of the year. This makes sense, as it is in summer when we loiter around in our gardens, and in nature, eating fruits and vegetables potentially contaminated with bird feces.

There is magic in large numbers, for sure.

Link to the paper:

Cody, A.J., McCarthy, N.D., Bray, J.E., Wimalarathna, H.M.L., Colles, F.C., Jansen van Rensburg, M.J., Dingle, K.E., Waldenström, J. & Maiden, M.C.J. 2015. Wild bird-associated Campylobacter jejuni isolates are a consistent source of human disease, in Oxfordshire, United Kingdom. Environmental Microbiology Reports 7: 782-788.

Not all birds are equal – a new paper debunks the notion of passerines as influenza A virus reservoirs

Influenza A viruses are elusive, just like the Scarlet Pimpernel – scientist seek them everywhere!

By Jonas Waldenström

In each scientific field there are findings that stand out as peculiar; odd findings that are not widely replicated. Still, as they are part of the scientific record, you need to relate to them in your own work, even cite them at times. For the influenza A virus field, one such oddity has been the detections of virus in passerines. A bird is a bird, you might say – so if ducks and other waterfowl are loaded with these viruses, why cannot other birds be infected?

However, birds cannot (and should not) be lumped together in a big pile just because they have feathers. Among the world’s 10,000 or so species there are both physiological and ecological differences – not to neglect millions of years of evolution. Thus, there are likely differences both related to exposure (geographical distribution, habitat preferences, behaviors, diet, etc.) and to susceptibility or pathogenesis (distribution of receptors and perceptive cell types, physiology of the gastrointestinal tract, immune responses, etc.) that govern how readily different bird species are infected. On top of this, the very methods we use to detect virus have their issues. It is not uncommon to have lab contaminations, especially of PCR-products, that can make the very sensitive RRT-PCRs say ‘bing’, when they should say ‘bong’.

This week, Morgan Slusher et al. in Georgia, US, published a comprehensive review of influenza A virus in passerines. Not only did they critically evaluate all articles reporting findings, they also conducted a large prospective study where they sampled and screened wild birds.

So what did they find? First of all, the review (in total 60 papers published up till 2012) revealed that the majority of virus findings in passerines were associated with outbreaks in domestic birds, or were from birds in periurban settings. Only few cases were described from wild birds in more natural settings. Furthermore, the authors identified a general lack of confirmatory proof, e.g. if samples were positive in a PCR screening there was no subsequent isolation (or sequence) of virus from those samples. Some papers were even pinpointed as potentially flawed, due to non-validated screening methods (nested PCRs that are prone to yield false positives) or to potential lab contaminants of viruses (where the same subtype was isolated in many samples collected from several locations, but processed in the same lab).

Second, the prospective screening of samples, both by RRT-PCR, isolation attempts, and an antibody-based ELISA, yielded very few positive signals. Actually, none of the birds tested by RRT-PCR (547 samples) or virus isolation (900) were positive, and only 3 out 3,358 tested with the ELISA method gave a signal for past infections.

The conclusions, at least to me, is that terrestrial passerines should not be considered as reservoir hosts. This is not the same as saying that they are never infected, but that in terms of influenza A virus epidemiology and evolution they are accidental hosts, often caused by spillover infections from infected poultry in connection to outbreaks. I think this is similar to what most influenza A virus ecologists thought already, but it is extremely important that a study such as this was published – again, because it becomes part of the scientific literature, and not just opinions of the individual researcher.

On a general note, I think this exemplifies how one needs to distinguish between different types of hosts. As most pathogens can infect multiple hosts, but with varying proficiency, a mere positive finding in a species should not be implied as that species is a functional host, or a reservoir. Most spillovers are dead-end infections, or result in short stuttered transmission chains. They should of course be studied – not the least because a pathogen may evolve better transmissibility in the new hosts – but some level of caution in language use is needed, as we otherwise give the wrong information about host range and epidemiology.

So, at last, let me paraphrase the Scarlet Pimpernel:

We seek it here, we seek it there,
Those Scientists seek AIV everywhere!
Is it in sparrows? Is it in trogons?
Where are those damn elusive AIV virions!

A Red-headed Trogon – not exactly a passerine, but it was the only bird to rhyme (although not great) with virion. Photo by JJ Harrison [CC-BY-SA-3.0, via Wikimedia Commons].

Link to the paper:

Slusher, M.J., Wilcox, B.R., Page Lutrell, M., Poulson, R.L., Brown, J.D., Yabsley, M.J. and Stallknecht, D.E. 2014. Are passerine birds reservoirs for influenza A viruses? Journal of Wildlife Diseases, ahead of print.

*******************************************************************************************************************

If you enjoyed this post, or other posts on this blog, why not follow the blog via email, Feedly or get updates via Twitter by following @DrSnygg?

The New Testament for Campylobacter studies

By Jonas Waldenström

I am a happy (associate) professor today! Instead of the usual invoices and commercial leaflets there was a thick envelope in my mailbox. A big fat envelope that clearly contained a book. And not just any book, it was The Book – the long awaited book on Campylobacter Ecology and Evolution!

I love books, I really do! And even if I don’t read all books I buy, it is always nice to see them standing there in the bookshelf. A living testimony of the collective pursuit of knowledge.

Some people think that academic books are living dinosaurs, a way of publishing that is no longer up to date with how modern academia works. Perhaps they are right, but I hope they are wrong. A good edited book can really bring together the current knowledge in a field, and serve as a starting point for those that are new to the subject.

Three books and a cup of coffee.

In this particular book, Petra Griekspoor and I contributed with a chapter on Ecology and Host Associations of Campylobacter in Wild Birds. And that is a contributing factor to my happiness, of course. But really, it is nice with books, and I will definitely read this book from cover to cover. Among the contributors and the book editors, the book already is known as the New Testament!

The Campylobacter research field is fairly young (see for instance previous posts on this here and here) and has had the tradition of publishing books at a fairly regular basis. The first one, I believe, was published in 1994. When I started in 2001, I read the very recent Campylobacter book, edited by Irving Nachamkin and Martin Blaser, which was the pillar of wisdom at that time; published when the field as a whole started to move forward rapidly. In 2005, that book was replaced by Campylobacter Molecular and Cellular Biology, edited by Julian Ketley and Michel Konkel. And with time, of course, our New Testament will be replaced by a new book.

Very surprisingly, the three generations of Campylobacter books are almost identically thick.

A big applause for Sam Sheppard and Guillaume Meric that managed to steer this book into a final product! Twenty-four chapters, and more than 50 authors – that is quite an achievement! Cheers to Swansea! And to Campylobacter! And to books in general!

*******************************************************************************************************************

If you enjoyed this post, or other posts on this blog, why not follow the blog via email, Feedly or get updates via Twitter by following @DrSnygg?