Tag Archives: epidemiology

A New Vector for Leishmania

It isn’t every day that we learn about the discovery of an entirely new vector for an important vector-borne disease. A new report by the Australian Department of Agriculture and Fisheries has identified a new species of Leishmania that is transmitted by midges, not the usual vector, sandflies. Leishmania is a vector-borne protozoan parasite that causes an ulcerative disease known as Leishmaniasis or Kala-azar. Leishmaniasis is a disease primarily of the tropics and subtropics and is considered one of the most neglected infectious diseases in the world. The usual vectors are phlebotomine sandflies.

Australia (along with Antarctica) was thought to be the only continent free of Leishmania when locally-acquired infection was detected in kangaroos in Northern Territory in 2003.  Researchers investigating this infection thought that the local sandflies (Sergentomyia spp.) seemed highly unlikely vectors because they show a strong preference for feeding on reptiles. Indeed, screening for Leishmania in 3046 Sergentomyia sandflies yielded none infected with Leishmania. This led the researchers to expand the vectors tested. What they found was an unnamed species of day-feeding midge (Lasiohelea sp.) that was infected with a prevalence of up to 15 percent. This is the first identified vector for Leishmania that is not a phlebotomine sandfly. Not much is known about this midge.  The researchers were unable to find breeding sites, for example. The presence of prolegs on the midge larvae suggest that it is not aquatic but is terrestrial or semi-acquatic.  The authors suggest looking for midge breeding sites in the moist soil near water troughs where kangaroos drink.

Finding a totally new vector for a disease carries with it implications for eradication and control. One possibility raised by this work is that the difficulty some control programs have experienced may reflect the fact that Leishmania is being transmitted by multiple vectors. This is an hypothesis well worth investigating in areas other than Australia.

This work formed the basis of the Ph.D. dissertation for Annette Dougall at Charles Darwin University, Menzies School of Health Research.  Nice work, Annette!

Measuring Epidemiological Contacts in Schools

I am happy to report that our paper describing the measurement of casual contacts within an American high school is finally out in the early edition of PNAS. Stanford’s great social science reporter, Adam Gorlick, has written a very nice overview of our paper for the Stanford Report (also here in the LA Times and here on Medical News Today). The lead author, and general force of nature behind this paper, is Marcel Salathé, who until recently was a post-doc here at Stanford in Marc Feldman‘s lab.  This summer, Marcel moved to the Center for Infectious Disease Dynamics at Penn State, a truly remarkable place and now all the better for having Marcel.  From the Penn State end, there is a nice video describing our results as well as well as a brief note on Marcel’s blog.  This paper has not been picked up quite like our paper on plague dynamics this summer, probably because measuring casual contacts in an American high school generally does not involve carnivorous mice.

With generous NSF funding, we were able to buy a lot of wireless sensor motes — enough to outfit every student, teacher, and staff member at a largish American high school so that we could record all of their close contacts in a single, typical day. By “close contact,” we mean any more-or-less face-to-face interaction within a radius of three meters.  As Marcel was putting together this project, we were (once again) exceptionally lucky to find ourselves at Stanford along with one of the world authorities on wireless sensor technology, Phil Levis, of Stanford’s Computer Science department.  Phil and his students, Maria and Jung Woo Lee, made this work come together in ways that I can’t even begin to fathom.  This actually leads me to a brief diversion to reflect on the nature of collaboration.  As with our plague paper or SIV mortality paper, this paper is one where collaboration between very different types of researchers (viz., Biologists, Computer Scientists, Anthropologists) is absolutely fundamental to the success of the work.  In coming up for tenure — and generally living in an anthropology department — the question of what I might call the partible paternity of papers (PPP) comes up fairly regularly. “I see you have a paper with five co-authors; I guess that means you contributed 17% to this paper, no?”  Well, no, actually.  I call this the “additive fallacy of collaboration.” When a paper is truly collaborative, then the contributions of the paper are not mutually exclusive from each other and so do not simply sum.  To use a familiar phrase, the whole is greater than the sum of the parts.  Our current paper is an example of such a truly collaborative project.  Without the contributions of all the collaborators, it’s not that the paper would be 17% less complete; it probably wouldn’t exist. I can’t speak particularly fluently to what Phil, Maria, and Jung Woo did other than by saying, “wow” (thus our collaboration), but I can say that we couldn’t have done it without them.

I’ll talk more about our actual results later.  For now, you’ll either have to read the paper (which is open access), watch the video, or read the overview in the Stanford Report.

The Little Mouse on the Prairie

We have a new paper in the Early Edition of PNAS on the ecology of plague in prairie dogs. The Stanford News Service did a nice little write-up of the paper (and Mark Shwartz’s full version is available on the Woods Institute site) and it has now been picked up by a number of media outlets including USA Today, ScienceDaily, The Register (UK), as well as a couple of radio news shows. This paper has been a real pleasure for me because of my incredible collaborators.  Dan Salkeld, who has been a post-doctoral fellow with me and now splits his time between teaching in Human Biology at Stanford and working as an epidemiologist for the California Department of Health, is the lead author.  Dan is clearly one of the leading young disease ecologists working today and his understanding of the field and willingness to do the sometimes unglamorous grunt work of ecology in pursuit of important research questions continually impresses me. The paper uses data that he collected while he worked for co-author Paul Stapp on Paul and collaborators’ plague project in the Pawnee National Grasslands in Colorado. Dan and Paul had the idea that grasshopper mice (see below) might have something to do with the episodic plague outbreaks in prairie dog towns.  Apparently, this idea was met with skepticism by their colleagues. When Dan came to Stanford, I suggested that we could probably put together a model to test the hypothesis. While we were waiting for our research permits to come through for a project in Indonesia (also dealing with plague; another long story), we decided to take up the challenge. What really made the whole project come together was the fortuitous office-pairing of Dan with Marcel Salathé, another post-doc with whom I have collaborated extensively on questions of social networks and infectious disease.  In addition to being a brilliant theoretical biologist, Marcel is an ace Java programmer.  Following a few white-board sessions in the studio near our offices, Dan and Marcel put together an amazing computer simulation that achieves that perfect balance between simplicity and realism that allows for scientific insight.

I don’t think anyone would have predicted this particular collaboration and this particular outcome.  The results described in this paper come from an incredibly interdisciplinary collaboration. I am really struck at how great science can come from a few simple ingredients: (1) long-term ecological data collection facilitated by a visionary program at the National Science Foundation, (2) a space where people from quite different disciplines and with different scientific sensibilities can get together and brain-storm, (3) flexible funding that permits researchers to explore the interesting – if offbeat – scientific questions that arise from such interactions.  So, I have many debts to acknowledge for this one.  The field data come from the project for which Mike Antolin at Colorado State is the PI (out co-author Paul Stapp is a Co-PI for that as well).  The funding source for this project was the joint NSF/NIH Ecology of Infectious Disease program. This is a cross-cutting program that “supports the development of predictive models and the discovery of principles governing the transmission dynamics of infectious disease agents” (from the EID home page).  The space – both physical and intellectual – that permitted this work to happen was provided by the Woods Institute for the Environment.  This paper literally came into being in the project studio on the third floor of Y2E2 in the Land Use and Conservation area.  Amazingly, this is exactly what these studios were designed to do.  My office in Y2E2 has adjoining office space for grad-students and post-docs and this is where Marcel and Dan did most of their hashing. It was always amusing to pop my head in and see them both huddled around a computer, having animated discussions about how best to represent the complex ecology in a computational model that is simple enough to understand and flexible enough to allow us to test hypotheses.  Finally, funding. Dan was funded by a Woods Environmental Ventures Project grant for which I am the PI.  Marcel was funded by the Branco Weiss Science in Society Fellowship.  My own flexibility was assured by a career grant form the National Institutes of Health. Research funding is almost always important, but the requirements of research funding can sometimes be too constraining to permit exploration of really new ideas.  All three of these mechanisms (Woods EVP, Branco Weiss, NIH K01) provide exactly the type of flexibility that fosters creativity. I wish there were more programs like these.

One of the fundamental questions in disease ecology is how extremely pathogenic infectious agents persist both through time and across landscapes.  Plague is a bacterial disease that affects a wide range of rodents throughout the world and, in North America, particularly afflicts prairie dogs (Cynomys ludovicianus).  Plague epizootics (the animal equivalent of epidemics in humans) are dramatic affairs with almost complete mortality of massive prairie dog ‘towns’ of thousands of animals. If plague is so deadly to prairie dogs, how does it persist?  Is there another reservoir (i.e., an other host species that can maintain an infection in the absence of prairie dogs)?  Does plague get into the soil and persist in some sort of suspended state (the way that some Mycobacteria do, for example) waiting to reinfect a re-colonized prairie dog town? Or is plague really enzootic (i.e., when an infection persists at low levels in an animal population) and we just haven’t detected it? This question has wide applicability. Consider diseases of people such as Ebola Hemorrhagic Fever or SARS, or, going back a few hundred years in human history, that nastiest of bacterial diseases, bubonic plague. Yes, the same beastie.  A disease that killed a third of the population of Europe in the fourteenth century exists in prairie dogs in North America today (and sometimes spills over to produce human infections).

Prairie dogs are a keystone species of the grasslands of the American West. They are  threatened by various anthropogenic forces, including habitat destruction and human persecution.  But most importantly, prairie dog viability is threatened by plague.

Plague, a disease caused by the bacterium (Yersinia pestis) and the causative agent of Black Death, arrived in USA via San Francisco ca. 1900, and still infects (or threatens to infect) people each year, including in California. Plague killed as many as 200 million people in Medieval Europe. It is still important in Africa and Asia.  There have been sizable epidemics as recently as the middle twentieth century in India and China and a substantial outbreak in Surat, India in 1994 that, in addition to death, caused widespread panic and social disruption.

Previous modeling and ecological work tended to assume that die-outs occur very rapidly.  But questions dogged this work (as it were): were the apparently rapid die-offs simply an artifact of finally seeing dead dogs dropping all over the place? Prairie dogs do live underground, after all, and they live in enormous towns.  Who would miss a few dead dogs underground in a town of thousands?  Our paper suggests that previous modeling efforts get the story wrong. They fail to account for observed patterns because they missed key elements of the picture.  Previous models that could describe the phenomena lacked an actual explanation – it’s a magical reservoir? It’s a carnivore? Certainly it’s something somewhere?

While prairie dogs live in enormous towns, they are highly territorial within the towns.  Towns form because of the benefits of predator defense. They live in small family groups known as coteries, and these coteries form a more-or-less regular grid of small defended territories within the towns. Because of this regular structure induced by their territoriality, a directly-transmitted infectious disease can only move so quickly through a town since it could only be transmitted to immediate neighbors and each coterie only has a couple of these.  Plague is not directly transmitted though.  It is carried by flea vectors, but if the dispersal distance of a flea is less than the diameter of a coterie’s territory, then the transmissibility of this vector-borne disease is similar to something that is directly transmitted.  Prairie dogs are territorial and this territoriality limits the rate of disease propagation through prairie dog towns. However, prairie dogs are not alone on their eponymous prairies.

Grasshopper mice – smelly, carnivorous mice, happy to eat through prairie dog carcasses – get swamped by fleas that normally live on prairie dogs. And grasshopper mice have no respect for prairie dog territories. They spread fleas across prairie dog coteries. This is the critical piece of the puzzle provided by our analysis.  Grasshopper mice are the key amplifying hosts for plague in prairie dogs.  Grasshopper mice increase the spread of disease by moving fleas across the landscape, similar to the way that highly promiscuous people may spread HIV or so-called ‘super-spreders‘ transmitted SARS in the global outbreak of 2003.  Of course, there are interesting differences between the plague model and these other diseases. Grasshopper mice are like super-spreaders in that they push the system over the percolation threshold.  They are unlike super-spreaders in that they don’t have that many more contacts than the average – they just connect otherwise unconnected segments of a population already near the threshold of an epidemic.

Without grasshopper mice, plague still kills prairie dog families, one at a time, but it moves very slowly, and it is extremely hard to detect (who misses 5 dead prairie dogs in a colony that stretches for 200 hectares and has upwards of 5000 animals?).  The grasshopper mice take a spatially-organized system that is on the verge of an epizootic and push it over the threshold.  The term ‘percolation threshold’ in the title of our paper relates to a branch of theory from geophysics that explains how and when a fluid can pass through a porous random medium.  This theory uses random graphs, which are the same mathematical structure that we use to model social networks, to understand when, for example, a medium will let water pass through it – i.e., to percolate. When the density of pores in, say, a layer of sandstone passes a critical density, water can pass from the surface through to recharge the aquifer. Similarly, when the density of susceptible prairie dog families crosses a critical threshold, plague can sweep through and wipe out a town of thousands of individuals.  The spatial structure induced by prairie dog territoriality turns out, on average, to be not quite at the percolation threshold (though it’s close).  What the grasshopper mice do is provide the critical connectivity that puts the system over the threshold and allows a slowly simmering enzootic infection turn into a full-blown epizootic.

It is in thinking about percolation thresholds that we see how important the behavior of affected species is for understanding disease dynamics.  Plague in Asian great gerbils, while effectively modeled using the same mathematical formalism, only requires one species in order to achieve the percolation threshold. Because great gerbils roam more widely and mix more, what matters for plague epizootics in this species is simply overall gerbil density.

It seems quite likely that this pattern of diseases smoldering at low-level below the detection threshold before some dramatic occurrence brings them to general attention is common, particularly with emerging infections. For example, there is evidence for extensive transmission of H1N1 ‘swine flu’ in Mexico before a large number of deaths appeared seemingly quite suddenly in April of 2009.  A number of other diseases – both of people and wildlife – show this pattern of being seemingly completely lethal, burning through host communities, and disappearing only to reappear some years later.  Important examples include Ebola in both humans and gorillas, hantavirus in people, anthrax in zebra, or chytrid fungi and frogs.

What are the key take-home messages of this paper? There are five, as far as I see it: (1) plague is enzootic in prairie dogs and there is no need to posit an alternate reservoir, (2) this said, the transition from enzootic to epizootic infection in prairie dogs is mediated by grasshopper mice, (3) understanding disease ecology – including species interactions – is a key to understanding (and predicting) dynamics, (4) behavior matters for disease dynamics, and (5) epidemiological surveillance is essential for controlling infectious disease – just because you don’t see a disease, doesn’t mean it’s not there!

I’m sure I’ll have more to say about this.  I did want to note that the publication of this paper coincides with a personnel transition here in our group at Stanford. Marcel has moved on to a faculty position, joining the spectacular Center for Infectious Disease Dynamics at Penn State.  Peter Hudson and his crew have assembled an amazing and eclectic group of scientists in Happy Valley and kudos to them for landing Marcel.  I frequently think that only a total fool would pass up an offer to join this exciting and productive group, but that’s another story. I expect Marcel to do great things there and look forward to continued collaborations.

New Paper: Dynamics and Control of Diseases in Networks with Community Structure

Marcel Salathé and I have a brand new paper out in today’s issue of the Public Library of Science, Computational Biology. There is also a news piece by Adam Gorlick in the Stanford Report this morning. This is an idea I’ve been bouncing around for a few years now and I was very fortunate to have Marcel – and his programming wizardry – show up with an interest in the very same topic just at the right time. It’s not every day that one of the most talented young theoretical biologists in the world shows up at your office wanting to collaborate. If it ever happens to you, I suggest you act!

The fundamental question is: Does social structure affect that course of epidemics? The answer seems obvious, particularly for infectious diseases that are transmitted by direct person-to-person contact. However, specific work demonstrating the effects of social structure on epidemics can be hard to find. Part of the problem, of course, is that you can hardly do experiments in which you change social structure and then subject populations to an infectious disease. To overcome this ethical and practical barrier to research, epidemiologists, biologists, and social scientists interested in disease and human behavior use mathematical and computational models to study how changes in host behavior affect the outcome of simulated epidemics.

Two specific topics that clearly have some bearing on social structure have been investigated extensively: individual heterogeneity in contact number and individual assortativeness. Epidemic behavior in all but the simplest models has been seen as being driven by heterogeneity. When there is a lot of variance in the number of potentially infectious contacts that individuals in a population have, epidemics are more likely, they infect large segments of the population more quickly, and ultimately infect a larger fraction of the total population. Consider the extreme case where all members of a population have one contact except for one person, who has a contact with everyone else. If we were to draw a picture of such a contact network, it would resemble a star or a wheel with a central hub and spokes:

star

Infect any random individual on this star and everyone else is at risk for infection. At the opposite extreme, if everyone has exactly one contact, then a randomly infected person can infect, at most, one other individual.

couples

Assortativeness, the tendency for individuals to associate with others like themselves, can either aid or hinder the spread of infections. People in contemporary nation states like the United States show an incredible capacity to form associations with like individuals. We form social relationships, particularly intimate relationships, with people who are similar to us in age, socioeconomic status, sexual orientation, ethnicity, education, religion, forms of deviance behavior such as drug use or criminal activity, etc. Frequently, this assortativeness has the effect of localizing and concentrating epidemiologically important contacts. When this happens, individuals who act as bridges between different communities take on central epidemiological importance. For example, married men who visit commercial sex workers can serve as a critical bridge connecting high-risk populations of sex workers and injection drug users with the general population. Similarly, health care workers can bridge hospital populations with the general population, a phenomenon important for the emergence of SARS in 2002. (Note that for epidemiological applications, we call such individuals “bridges” but in other applications we might call them “brokers” or “entrepreneurs,” highlighting the general importance of such ideas for understanding society.) The existence of such social bridges highlights the fact that people can also assort on characteristics that are not visible attributes and this type of assortative behavior can increase connectivity. In particular, if people with few contacts tend to be connected to people with many contacts (as in the case of the star), then such disassortativeness can increase the epidemic potential in a population.

The aggregate effects of individual behavioral decisions can have a profound effect on the shape and composition of human populations, but there is more to human populations than simply individual behavior. For one thing, human populations are characterized by a hierarchical structure: individuals typically belong to households and households are aggregated into communities, which are, in turn, aggregated in towns, states, nations, etc. Naturally, there are cross-cutting ties in such hierarchical organization (much like bridges in individual contact networks). Freudian fantasies of primitive hordes aside, even the largely egalitarian societies of hunter-gatherers are characterized by a hierarchical structuring of families, bands, and tribes. Hierarchical structuring is clearly important for understanding social process in human societies.

So what effect does such community structure have on epidemics? To address this question, Marcel and I combined the formalisms of social network analysis and computational models of epidemics. We already know that heterogeneity in contact number can have profound effects on the outcomes of epidemics and that such heterogeneity can change aggregate social structure in complex ways. To avoid such complications, we generated networks where every individual had the exact same number of contacts. The only thing that varied in these toy networks was the likelihood that any randomly chosen connection between two individuals would be either within or between more or less cohesive subgroups (a.k.a., “communities”). Using metrics derived from Graph Theory, the branch of discrete mathematics that provides the basic tools for Social Network Analysis, we were able to characterize the degree of community structure and relate this to the outcome of epidemics simulated on the resulting networks.

It turns out that community structure has an enormous effect on epidemic outcome. In particular, we found that there is a remarkably abrupt transition from small outbreaks to very large outbreaks as we moved from the most structured populations to more moderately structured ones. Populations characterized by extreme community structure have smaller outbreaks because the infection has a hard time getting out of a community before dying out. As more connections to other communities are made – i.e., the community structure is lessened – there are more opportunities for the infection to escape and affect a larger fraction of the total population. While the result sounds intuitively satisfying after the fact, there was little precedent for expecting such an outcome in the mathematical theory of epidemics. This is because none of the standard metrics of an infectious disease – the basic reproduction ratio, in particular – changed as the populations’ community structure changed.

When we investigated the further structural network correlates of epidemic size, we found that one measure in particular predicted epidemic behavior quite well. This measure, known as “betweenness centrality,” harkens back to previous epidemiological interest in bridging individuals. A person with high betweenness lies on many of the shortest paths that connect all individuals in a network. When a person bridges two distinct subpopulations, he or she typically has high betweenness because all paths from individuals in one cluster have to pass through this person to get to the other cluster, and vice-versa. As a population moves from a condition of very high community structure to a more moderate level, the number of people with high betweenness increases. This highlights a particularly interesting contrast with previous models: epidemics are more likely and larger in populations with highly unequal distributions of contacts on the one hand, but also in populations with more equal betweenness.

With the information that betweenness predicts the extent of epidemic spread in populations with community structure, we sought a means to use such information to design intelligent control measures. How do you find people who have high betweenness? As abstract as the concept of betweenness may seem, it turns out to not be that difficult. We start with an infected person and do standard contact tracing. That is, we ask the index case about his or her contacts. Contact tracing is one of the most important tools in the toolkit of the gumshoe epidemiologist. From the index case’s contacts, we pick a random individual and trace his or her contacts. Picking a random individual from this second generation of contact traces, we simply ask “do you know the index case?” If so, we keep going: trace the contacts of a random contact, ask again if this person knows the index case. When we come to an individual who does not know the index case, we have found our bridge. It is the penultimate person in the chain – the person who links the index case to someone he or she doesn’t know. Basically, we do a “random walk” on the social network looking for people who link otherwise unconnected individuals. When we find the bridge, we vaccinate all of his/her contacts. We call our vaccination algorithm the “Community Bridge Finder” (CBF).

When we vaccinate according to this algorithm, we reduce the final size of the epidemic far more than randomly vaccinating the same fraction of people. More interestingly, CBF also does better than the other vaccination algorithm that uses only local network information typically available to epidemiological investigators. This algorithm, known as the “Acquaintance Method,” vaccinates a randomly selected contact of an index case. The idea behind the acquaintance method is that the contacts of a case are more likely than chance to be highly connected individuals themselves in a population with heterogeneous contacts. That is, given that you have a contact, you’re on average more likely to be connected to a hub than to someone with few connections because hubs simply have more connections.

Of course, the way that we constructed our contact networks, we stacked the deck against the acquaintance method. Remember, everyone has the same number of contacts; what varies is how many contacts are within versus between communities. One of the great limiting factors for progress in social network analysis – and network epidemiology in particular – is the paucity of detailed network data from well-defined human populations. A domain that has garnered a lot of interest recently is the analysis of networks created by social media such as Facebook and Twitter. We used data from Facebook when its use was still restricted to particular college campuses to provide networks on which infections could pass. Facebook users typically have many contacts, probably way more than people have in epidemiologically relevant networks. However, because the data come from college acquaintance networks, we were able to prune the networks down toward something hopefully more epidemiologically appropriate. We kept contacts in the networks only if two individuals shared one a several key attributes such as shared dorm or major. What this yielded were a series of networks with heterogeneous contact structure and quite a bit of community structure (the measure of community structure hovered near the values where epidemics transitioned from small to large in our simulated networks). Once again, CBF outperformed the acquaintance method. This provided very strong evidence that community structure really matters for epidemic behavior and that exploiting information on community structure allows us to better control outbreaks of infectious disease.

Latest Swine Flu Epidemic Curve for the United States

It’s been a while since I last posted about swine flu.  Alas, it is still with us. The most recent data from CDC show that swine flu is still with us and that we should steel ourselves for a heckuva flu season this autumn and winter.  The curve peaks around the middle of June, but this is well past a typical flu season.  The influenza virus apparently does not survive well when the absolute humidity rises as temperatures rise and the air can hold more moisture.  When the weather gets cold again in the northern hemisphere and the absolute humidity drops, the virus will better survive outside of its infected host and transmission will increase.

Here is the epidemic curve as it currently stands:

CDC reported confirmed influenza cases for 2008-2009
CDC reported confirmed influenza cases for 2008-2009

It reassuringly appears to be tailing off, but in reality, it is just experiencing a summer lull (remember, also, that there is quite a bit of under-reporting at this point).  It should start to pick up in October or so when the bars representing the incident cases will almost certainly dwarf the current ones.  We’re working on a number of flu-related projects, including the very precise measurement of within-school contact networks (recently funded by NSF!) as well as a project on perceptions of vaccination and (we hope) the measurement of vaccine opinion clustering.  My collaborator on this project, Marcel Salathé, has a terrific paper with Sebastian Bonhoeffer at ETH on the impact of opinion clustering on infectious disease eradication through vaccination. Their work shows that the standard estimates of necessary vaccination coverage required to protect the population through herd immunity are overly optimistic if people who share anti-vaccination beliefs, and therefore do not vaccinate themselves or their families, cluster in a population.  I will try to update, but I fear it will prove to be a very busy Autumn for me…

New Publication: Chimpanzee "AIDS"

keele_etal2009-first-pageA long-anticipated paper (by me anyway!) has finally been published in this week’s issue of Nature.  In this paper, we show that wild chimpanzees living in the Gombe National Park in western Tanzania on the shores of Lake Tanganyika appear to die from AIDS-like illness when infected with the Simian Immunodeficiency Virus (SIV).  Many African primates harbor their own species-specific strain of SIV and chimpanzees are no exception.  The host species for a particular SIV strain is indicated by a three letter abbreviation (all in lower-case) following the all-caps SIV. So, for chimpanzees, the strain is called SIVcpz. It turns out that there are two distinct HIVs, known as HIV-1 and HIV-2. HIV-1 is the virus that causes the majority of the world’s deaths.  It is what we call the “pandemic strain.” HIV-2 is less pathogenic and has a distinct geographic focus in West Africa.  The HIVs and the various SIVs belong to a larger group of viruses that infect a wide range of mammals known as the lentiviruses (lenti– meaning slow, referring to the slow time course of the pathology typically caused by these viruses). Collectively, we call the SIVs and HIVs “primate lentiviruses.”  Both HIV-1 and HIV-2 have well-documented origins in nonhuman primate reservoirs.  HIV-2 is most closely related to SIVsmm, a virus that infects sooty mangebeys (a type of West-African monkey).  HIV-1, on the other hand, is most closely related to SIVcpz, the virus that infects central and east African chimpanzees.  We believe that both HIV-1 and HIV-2 entered humans hosts when hunters were contaminated with the blood of infected monkeys (HIV-2) or chimpanzees (HIV-1). Note that this means that our terminology for the primate lentiviruses is polyphyletic.  SIVsmm and HIV-2 are sister species, while SIVcpz and HIV-1 are sister species.  Yet we call all the viruses that infect nonhuman primates simian and all the viruses that infect humans human immunodeficiency viruses.  It seems to me the best way to fix this would be to call the viruses that infect humans SIVhum1 and SIVhum2.  Of course, that will never happen, but I do think that it’s important to clarify the evolutionary history of these viruses.

The conventional wisdom regarding primate lentiviruses is that, with the exception of HIV, they are not pathogenic in their natural host.  The reasoning for why HIV causes the devastating pathology that characterizes AIDS goes that HIV-1 is a relatively new infection of humans, having just spilled over into the human population recently.  Pathogens that have recently crossed species boundaries are frequently highly pathogenic because neither the new host nor the pathogen has a history of coevolution with its new partner.  While it is a pernicious myth (that just won’t seem to die) that pathogens necessarily evolve toward a benign state, it is true that they frequently evolve a more intermediate level of virulence from their initial spillover virulence.  There are a number of problems with the idea that HIV causes AIDS because it is poorly adapted to human physiology.

The first of these is that HIV-1 is not that recent an infection of humans.  Sure, we didn’t notice it until 1983 but careful molecular evolutionary analysis by Bette Korber of the Santa Fe Institute and my collaborator Beatrice Hahn and her group at the University of Alabama Birmingham puts the most likely date for the emergence of HIV-1 in humans to be 1931.  That means that HIV-1 was being transmitted from human-to-human for over fifty years before it was ever noticed by western science. Fifty years, while certainly brief in evolutionary terms, is still long enough to lead to some reduction in virulence or host evolution.

The real nail in the coffin, however, is our new result.  Specifically, we show that SIVcpz causes AIDS-like pathology in the Gombe chimpanzees. This result is surprising because (1) given it’s pathogenicity, one would expect someone to have noticed it before, and (2) chimpanzees infected in captivity do not show obvious AIDS-like illness. I have been collaborating with Anne Pusey, Mike Wilson and their colleagues at the University of Minnesota’s Jane Goodall Institute Center for Primate Studies on the the analysis of the demography of the Gombe chimps for a number of years now. Anne and Mike have, in turn, been collaborating with Beatrice Hahn with her project on monitoring natural SIV infection in wild chimpanzees across Africa. Given my background in HIV epidemiology and statistics, it was only natural that we all join forces to look at the demographic implications of SIV infection among the Gombe chimps.  Jane Goodall famously started chimpanzee research at Gombe in 1960 and since 1964, researchers at Gombe have collected detailed demographic information, documenting all births, deaths, and migration events in the central community and eventually expanding to the peripheral ones in later years. As a result, we have an unmatched level of demographic detail (not to mention behavioral and ecological information) against which to assess the impact of SIV infection.  Using statistical methods known collectively as event-history analysis, we were able to show that the hazard ratio between SIV-infected and SIV-negative chimps is on the order of 10-16.  This essentially means that SIV+ chimps have mortality rates that are 10-16 times higher than uninfected chimps.  The analysis controls for the clear potentially confounding effects of age and sex on overall mortality. The reason why no one ever noticed this heightened mortality rate is really because no one has ever looked for it. Even when a mortality rate is 10 times higher for some segment of a population, when that segment is small and when mortality rates quite low (chimps who survive infancy can live in excess of 40 years) it can be hard to detect even a seemingly large difference.  This is why we do science: because things that seem obvious once we know they are there can be remarkably subtle when we don’t know they’re there.  Science gives us the framework and the tools for studying nature’s subtleties.

This project was absurdly interdisciplinary.  The paper has 22 co-authors, each contributing his or her own particular analytical expertise or providing access to crucial data necessary for the larger narrative.  There are papers in the literature in which people are made co-authors for pretty thin contributions.  This paper has none of that.  It was an extremely complicated story to tell and it really required the collaboration of this large team. Such work is not easy to manage and it’s not at all easy to do well.  I think that Beatrice should be commended for orchestrating all the various major contributions, keeping us in line and on schedule (more or less). It’s really gratifying to see the excellent blog piece by Carl Zimmer in which he notes the virtues — and the difficulty — of combining various scientific styles in pursuit of an important question. The title of Carl’s piece is “AIDS and the Virtues of Slow-Cooked Science.” In addition, there is a nice companion piece in this week’s Nature written by Robin Weiss and Jonathan Heeney.  They too note the strength of the interdisciplinary approach to this problem.

The paper isn’t even officially published until tomorrow and it has already been covered on Carl Zimmer’s blog for Discover Magazine, The New ScientistThe GuardianThe ScientistThe New York Times and MSNBC. Wow.  Weiss & Heeney note a number of questions that are raised by our analysis.  Specifically, they ask “why was the progression to AIDS-like illness not more apparent in chimpanzees in captivity?” My co-author Paul Sharp notes “We need to know much more about whether there are any genetic differences among the chimpanzees, or differences in co-infections with other viruses, bacteria or parasites, which influence whether or not SIV infection leads to illness or death. This presents a unique opportunity to compare and contrast the disease-causing mechanisms of two closely related viruses in two closely related hosts.”  Then, of course, there are the conservation questions that this paper raises.  Chimpanzees in the wild have birth rates that are very nearly balanced out by their death rates.  This difference, called the intrinsic rate of increase, largely determines the probability of extinction of a small population.  When the rate of increase of a population is negative, it is certain to go extinct (assuming the rate remains negative).  However, even if the intrinsic rate of increase is greater than zero, the randomness that besets small populations still means that a population can go extinct.  So, because their average birth and death rates are so close, individual chimp populations are certainly in potential jeopardy of going extinct, and Gombe is no exception to this rule. Now we add to a population something that increases mortality rates 10-16 times.  This is bound to have negative consequences for the persistence of affected chimp populations.  This is a topic that we are exploring even as I write…

Under-Reporting of Swine Flu

A very interesting epidemiological analysis of the first cases of novel A(H1N1) flu in China was posted on ProMED-mail this morning by Dr. Ji-Ming Chen, Head of the Laboratory of Animal Epidemiological Surveillance, China Animal Health and Epidemiology Center, Qingdao. Dr. Chen notes that all 12 of the cases in China were imported via air travel.  He writes, “if the prevalence of the A (H1N1) infection among the international airplane passengers is comparable to that in the departure countries, there should be many more cases in USA and Canada than the official records (more than millions?).”

How can this be?  There is more evidence in Chen’s epidemiological analysis.  Of the twelve imported cases, only two were identified as possible cases using airport temperature scanners.  These two individuals were the only patients to complain of discomfort (i.e., flu-like symptoms) on their flights.  It seems quite likely that this particular strain of influenza produces very mild, sub-clinical symptoms in many of its victims.  The implication of this inference is that infection could become very widespread without being noticed by public health officials or the public at-large.

Daily Flu Counts

The bad news is that cases of novel 2009 influenza A(H1N1) continue to increase. Data from WHO Epidemic and Pandemic Alert and Response (EPR), Influenza A(H1N1) – update 43 — 23 May 2009:

The good news is that the spread appears to be sub-exponential at this point.  Exponential growth will appear linear on semi-logarithmic axes.  Here I plot the natural logarithms of these same case-count data against the date. We can see a distinct (negative) concavity, indicating that the growth in confirmed cases is sub-exponential.  The usual caveats about under-reporting and the lag between infection and reporting dates apply, but this is a modicum of good news.

The austral flu season will be heating up (as it were) soon enough. Once again, it seems only prudent to me that the richer nations of the north help poorer nations, who are about to get hit, with efforts to contain the spread of novel A(H1N1).  Given the relative genetic homogeneity of this novel strain, choice of a strain to include in a vaccine is straightforward (if a little late for the beginning of the northern flu season).  If we can minimize the intensity of antigenic drift (despite the name which might imply random change, this is directional selection away the ancestral antigenic type in the presence of multiple circulating strains) by minimizing the number of cases in the south during their flu season, perhaps we can dodge the bullet of an extremely high-mortality pandemic.