Predicting zoonotic spillover

[We’re taking a break from the “how to become a successful parasite ecologist” post series. More on that in a few weeks!]

Poop is pretty gross, and some poop is more disgusting than other poop. I’m sure you’d agree with both of those statements, but why? Imagine, if you will, that you are participating in one of my favorite activities: crawling in a narrow cave passage, with just enough room above you to wear your pack while you’re crawling. You round a corner and discover a very interesting conundrum: the small passage forks momentarily, and one fork contains a large pile of fresh raccoon poop, while the other is sprinkled with bat guano (less fresh). You’ll obviously avoid crawling directly through either one, but which is most important to avoid?

When parasites and pathogens that infect wildlife or domesticated species spillover into humans, it can be pretty terrible – think Ebola, SARS, rabies, etc. And depending on how you define “zoonosis” – we’ll get back to that in an upcoming post – you might say that most emerging infectious diseases of humans are caused by zoonotic parasites and pathogens. So disease ecologists should and do spend a lot of time trying to understand what causes the spillover of wildlife parasites into human populations, and how to predict and even control such spillover events.

The EcoHealth Alliance group is well known for tackling this important and complicated issue, and they recently published some great synthesis science in Nature that works towards understanding and predicting the origins of zoonotic viruses (Olival et al. 2017). Olival et al. (2017) created a database that contained every known virus of mammals and the 754 mammal species infected by those viruses. They also had trait information for each virus and each mammal species. Then they explored their massive mammal-virus data mountain with the intention of  answering ~4 big questions:

Which mammal species host the most known viruses, and what makes some mammal species have more viruses than others? As we’ve seen in other studies, the most important determinant of viral richness in each mammal species was the total disease-related research effort that has focused on that mammal species in the past. (This was also true for the number of zoonotic viruses per host species – see next). In other words, the more we look, the more we find! But Olival et al. (2017) take this one step further, and use model predictions to tell us where we should look to find the most new viruses and the most new zoonotic viruses – see below.

Which mammals host the most known zoonotic viruses, and what makes some mammal species have more zoonotic viruses than others? For the purposes of this paper, zoonotic viruses were defined as viruses detected at least once in humans and at least once in another mammal species. Proportionally speaking, bats, primates, and rodents had more zoonotic viruses than other mammal taxa. And some host traits that correlated with the number of zoonotic viruses per species included phylogenetic distance to humans, ratio of urban to rural human population in the host’s range (a possible measure of human-wildlife contact), and whether the species was hunted (another measure of human-wildlife contact). Even after controlling for all of those covariates, bats hosted higher proportions of zoonotic viruses than other mammal taxa.

If you’re a long time follower of this blog or the disease ecology literature, then you know that this isn’t the first study to find that bats host more than their fair share of zoonotic viruses. For instance, previous work had shown that bat species have more zoonotic viruses than rodent species, on average. (But there are more rodent species than bat species, so rodents host more total zoonotic viruses). Olival et al. (2017) confirm this with a dataset including many more viruses and mammal taxa, so the “bats are special” pattern is quite robust! If you’re wondering why bats host more proportionally more zoonotic viruses than other mammal taxa, you might be interested in these previous posts: here, here, and here.

Where do we expect to find the most undescribed viruses, and in particular zoonotic viruses? It turns out that if you want to find new zoonotic viruses, the best place to look would be bats in Northern South America. Cool! You can check out the neat maps in the paper if you’re interested in other taxa or geographic areas.

Did particular virus traits correlate with whether a virus has been observed to be zoonotic or not? Yes! For instance, viruses that that infected a greater range of non-human host species (i.e., host breadth), replicated in the cytoplasm, or were transmitted by vectors were more likely to be zoonotic. Of course, these viral traits don’t 100% predict whether a newly discovered virus will be zoonotic or not, but these descriptive models help to identify hypotheses that can explain why some viruses easily jump into humans and others don’t.

So… what does all of this tell us about poop in caves? Well, not much, actually. The Olival et al. (2017) study was meant to describe broad patterns and make predictions to guide future survey/surveillance efforts, not to inform specific risk assessments. But to follow up on my admittedly tenuous hook, we DO know that some mammals are far more likely to pass on viruses to humans than others. So if you have to choose between hugging a bat or a rabbit (or crawling through their poop), pick the rabbit!

But of course, it isn’t just viruses that we need to worry about, so I gladly chose guano over raccoon poop – I was worried that the raccoon poop might contain Baylisascurus eggs. I’ll keep my eye out for their next Nature paper that does this study with all parasites and pathogens!

Batsarefriends

2 thoughts on “Predicting zoonotic spillover

  1. Love the blog! I read the Olival et al. paper shortly after seeing your post on the definition of zoonotic. I thought it was interesting that they used a definition (found in both humans and other mammals) that didn’t appear in your poll.

    Also, for the “all parasites and pathogens” approach and some similar questions, check out our recent paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180716

    Thanks for writing!

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