Tag Archives: publication

New Ebola Paper

OK, not exactly new new, but certainly newish. This post is part of my new attempt to update my blog more with stories about science, research, and education in an attempt to avoid the vicious cycle of anxiety and depression that comes from spending too much time reading the news and engaging in social media.

Our paper on the prevalence of asymptomatic Ebola cases in Sierra Leone was published in PLoS Neglected Tropical Diseases on 15 November. In it, we show that nearly 10% of a sample of people from an Ebola hotspot in Kono District, Sierra Leone, tested positive for Ebola virus antibodies despite having reported no symptoms of Ebola Virus Disease.

The West African Ebola epidemic of 2014-2015 was the biggest outbreak of the disease ever recorded, with over 28,000 reported cases.  Our results suggest that the total number of cases may have been quite a bit more than this. They also suggest that Ebola is, as we suspected, like other pathogens and causes a wide variety of clinical manifestations.

The paper received quite a bit of media attention from outlets such as NPR, The Wall Street Journal, The LA Times, and Gizmodo.

This work was led by my rock-star Ph.D. student, Gene Richardson and involved a great many collaborators.  It was a great honor to be able to publish with such luminaries as George Rutherford, Megan Murray, and Paul Farmer. With several papers in the works or already submitted and ongoing research, I’m really looking forward to more results in the near future!

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:


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.


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.

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…