Where will the next bat virus spillover?

In a previous post, I discussed potential characteristics of bats that might make them “good” at “sharing” novel viruses with humans. There are many hypotheses out there, and probably all of the proposed important bat characteristics play a role for spillover of some viruses in some places at some times. There’s still a lot of research that needs to be done there, so investing in bat research is still a high priority. For today, we’ll just talk about one characteristic: for whatever reason, bats are hosts for a huge diversity of viruses, so there are a lot of viruses for them to potentially transmit to humans.

Even if bats are highly likely to share novel viruses with human populations, that sharing could never happen if bats and humans didn’t directly or indirectly interact. Therefore, there are also lots of hypotheses out there about which anthropogenic activities lead to high rates of interaction between humans and bats. For instance, areas with many people, areas with many domesticated animals that interact with bats and people, and areas where people are particularly likely to encounter bats (e.g., by eating them as bushmeat) might be especially likely to experience spillover of bat viruses into human population.

So, wouldn’t it be awesome if we had a map that showed where these drivers of bat virus spillover were particularly prominent, so that we could predict areas where spillover is most likely to occur? Why yes, yes it would be awesome. And just such a map was recently created by Brierley et al. (2016).

Here’s the just of it: using spatial regression techniques, Brierley et al. (2016) came up with a list of drivers that were good at predicting the total number of viruses shared between humans and bats in 1 decimal degree-sized grid blocks all over the world. They found that bat host diversity and annual rainfall were important drivers, and they suggested that these were links between virus diversity and the potential for virus spillover. They also found that things like human population sizes, the number of domesticated pigs, and the use of bats as bushmeat were important drivers, suggesting that anthropogenic activities are also important to spillover.

Interestingly, the areas where risk is high due to the high diversity of bat viruses (South America) are not the same as the areas where the risk is high due to high human-bat interaction rates (Sub-Saharan Africa). This suggests that when we think about preventing spillover of bat viruses into human populations, we probably need different plans for regions with different drivers. That’s not necessarily a new idea, but now we have a great map to show us which areas need which kinds of prevention!

This cartoon is not intended for people eating bats because they have few sources of protein in their lives. Obviously, eating bats isn’t a decision for them, it’s a necessity. But those of us in positions of relative power can work towards alleviating the socioeconomic situations that push people towards the consumption of bushmeat. And if you do have a choice, don’t eat bats!!

References:

Brierley, L., M.J. Vonhof, K.J. Olival, P. Daszak, and K.E. Jones. 2016. Quantifying global drivers of zoonotic bat viruses: a process-based perspective. The American Naturalist.

Is zombie transmission transmission frequency or density dependent?

For reasons that I cannot explain, visitors have started to stumble upon my blog by googling the question, “is zombie infection frequency or density dependent?” Maybe there’s a really awesome educator out there using that example in class. Or maybe the zombie apocalypse has started and people are secretly beginning to plan for the end. Either way, this is a neat question that I’m willing to speculate about!

First, we need to decide what kinds of zombies we are talking about. Let’s assume we’re looking at World War Z type zombies, where infection is transmitted via bites/saliva/fluid transfer. Let’s say that the zombies are highly mobile and thus the human and zombie populations are well-mixed. Also, let’s assume that zombies don’t really have a contact structure, like humans do, because they’ve lost any kind of social system that they had a humans.

Given those assumptions, I would expect disease-relevant contacts to increase with host density. So, if I had to pick between density dependent and frequency dependent transmission, I’d expect density dependent transmission. But don’t forget that there are nonlinear contact functions, too. Those might work better, because even a tireless biting machine can only bite so many people per day.

When might zombie transmission be frequency dependent? FD transmission would be appropriate if larger populations covered larger areas, so that host density was constant. I suppose that could happen if humans were dispersing as much as possible and running away from zombie-packed areas. What do you think?

Force of Infection

I haven’t posted about a paper recently, so here’s a random grab out of my massive folder of “PDFs to Read Someday.”  (I should use differential equations to model how the number of PDFs in that folder changes over time…except that it appears to be exponentially increasing, so maybe I don’t want to know.) 

First, let’s talk about force of infection (FOI).  FOI is the rate at which susceptible individuals become infected per unit time.  It is an important parameter (the most important?) for transmission in epidemiological models.  The problem is that FOI isn’t as easy to measure as you might think.  In order to get good estimates of the rate of infection, you typically need to monitor infection status in many, many individuals.  For instance, even assuming you can identify infected individuals with 100% accuracy, your estimate of FOI is going to be better if you sample 100 individuals (1% resolution) than 20 individuals (5%) resolution. 

Serious lack of FOI-related pictures on the Internet. Diagram from here.

But of course, you can’t identify infected individuals with 100% accuracy.  This is especially apparent when organisms have a period of latent infection, or when infection gets more obvious the longer the individual is infected.  For instance, if a snail has had first intermediate trematode infection for a while, crushing the snail might result in a mini-explosion of rediae/sporocysts all over your dissection surface.  (A brief aside for the curious.)  If it’s a newly developed infection, you might be lucky to spot a single redia/sporocyst.  The same is true when looking for Plasmodium falciparum (malaria parasite) in blood smears; you’re much more likely to identify an infection if parasitemia is very high.

What once were snail gonads are now trematode duplexes. Trematode sprawl. Photo from here.

Finally, if an individual can get infected multiple times, you would underestimate FOI if you could only identify the first infection event.  This is where the Mueller et al. (2012) paper from PNAS comes in.  For P. falciparum in Papua New Guinea, most people have infections with multiple different clones of P. falciparum.  That means that they got multiple clonal infections from one mosquito bite and/or they got subsequent infections with different clones from different mosquito bites. 

To get a super duper accurate FOI measurement with molecular techniques (molFOI), Mueller et al. (2012) took blood samples from ~400 kids aged 0-4 every two months for 69 weeks.  Impressive!  Then they used genotyping of the mps2 gene to determine how many P. falciparum clones each child had at each time point.

Cutting to the chase:  molFOI and the incidence of clinical cases of malaria (i.e., number of reported cases with high parasite loads) had very similar patterns across child age and week/season.  molFOI had a strong relationship with week/season, and this relationship explained ~50% of the relationship between malaria incidence and season.  Furthermore, most of the age*incidence relationship was explained by the age*molFOI relationship.  So, cool new tool for monitoring transmission in malaria control programs!  Neat!

Though they certainly aren’t the first people to demonstrate the efficacy of using insecticide treated bed nets, they also found that ITNs reduced infection risk by ~50%.  Awesome.

Multiple clonal infections are the norm in the P. falciparum system mentioned here.  Do you think that is true in many disease systems? 

Reference:

Muellera, I., S. Schoepflind, T.A. Smithd, et al. 2012. Force of infection is key to understanding the epidemiology of Plasmodium falciparum malaria in Papua New Guinean children.  PNAS 109:25, 10030-10035.