Tag Archives: epidemiology

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!

Ebola Event at UCI: Planning, Not Panic

I am just back from an event at the University of California, Irvine organized by medical demographer Andrew Noymer. The event drew a big crowd, with probably 500-600 people in attendance.

There were five invited plenary speakers: Michael Buchmeier (UCI) spoke about the virology of Ebola and the Filovriuses more generally. Hearing Mike’s insights on the not one, but two vaccines for Ebola that have been shelved for a decade due to lack of interest was particularly illuminating. George Rutherford (UCSF) talked about the epidemiology of the current EVD epidemic and placed control efforts within the broader context of Global Health Initiatives. This is a guy with a ton of experience in global health and on the ground in Africa and his cool demeanor was calming for the crowd. Victoria Fan (Hawai’i) discussed the economic implications of the epidemic. Spoiler alert: they’re not good. Shruti Gohil (UCI Medical Center) talked about infection control in a hospital setting. Finally, I talked about the disease ecology, broadly construed, of Ebola. Following our talks, we got together as a panel and took questions for the audience.

Given the crazy hysteria surrounding the EVD epidemic and the arrival of a handful of cases in the United States, it was reassuring to participate in a couple hours of such sober, scientifically-informed discussion. Shruti’s insights as chief of infection control at the UCI medical center particularly struck me. She noted that Texas Health Presbyterian Hospital in Dallas, where the first American EVD case (Thomas Duncan) was treated, was clearly completely unprepared to handle an acute EVD case. Despite this, Shruti estimated that the attack rate of health care workers who attended to Duncan was about 4%. Not that horrible for an unprepared hospital. She also noted that no health care workers have become infected in the special units specifically designed to handle infectious diseases like EVD at Emory, Nebraska, and Bethesda. Planning, strict adherence to protocols, and personal protective gear work!

So, let’s summarize a bit about EVD in the US (these are the numbers as best as I can remember them, with citations where I can find them):

Number of cases of evacuated aid workers infected in Africa: 4

Number of deaths of evacuated aid workers infected in Africa: 0

Number of travel-associated cases in US: 4

Number of deaths of travel-associated cases in US: 1

Number of cases of American health care workers: 2

Number of deaths of American health care workers: 0

Note that the one death (Thomas Duncan) might have been prevented if he hadn’t been sent home from the emergency room and gotten so much sicker.

Another interesting point that Shruti made is that none of Duncan’s close personal contacts have contracted EVD and the 21-day window has now passed. The clear implication of all these data is that Ebola is not that infectious. It is controllable if we are prepared and follow protocols.

This gives me hope that we can control the EVD epidemic in West Africa if we were to decide to get serious about its control. But the international community needs to fight this epidemic where it is currently raging. This is clearly in the national interest of the United States and the collective interest of the international community. If we want to remain secure from EVD, we need to stop it where the epidemic continues to grow. World Bank President, and medical anthropologist extraordinaire, Jim Kim pulled out a great analogy in an interview on NPR on 17 October:

It’s like you’re in your room and the house is on fire, and your approach is to put wet towels under the door. That might work for a while, but unless you put the fire out, you’re still in trouble.

Let’s get over our fear, stop politicizing this crisis, stop demonizing the heroes. Let’s roll up our sleeves, get out our checkbooks, and bring a speedy end to this crisis.  Let’s put out the fire.

 

Quick and Dirty Analysis of Ebola

I've been traveling all summer while this largest Ebola Virus Disease (EVD) outbreak in recorded history has raged in the West African countries of Guinea, Sierra Leone, Liberia, and (worryingly) Nigeria. My peripatetic state has meant that I haven't been able to devote as much attention to this outbreak as I would like to. There is a great deal of concern -- some might say hysteria -- about EVD and the possibility that it may go pandemic. Tara Smith at least, on her Aetiology blog, has written something sensible, noting that EVD, while terrifying, is controllable with careful public health protective measures, as the historical record from Uganda shows. A recent post by Greg Laden got me to thinking about the numbers from the current EVD outbreak and what we might be able to learn.

EVD was the model disease for the terrible (1995) Dustin Hoffman movie, Outbreak. As we learned in the much more scientifically-accurate (2011) movie Contagion (which is based on an equally terrifying aerosolized Nipah virus), one of the key pieces of information regarding an epidemic is the basic reproduction number, R_0. The basic reproduction number tells us how many secondary infections are expected (i.e., on average) to be produced by a single, typical case at the outset of an epidemic before the pool of susceptible people has been depleted.  R_0 provides lots of information about epidemics, including: (1) the epidemic threshold (i.e., whether or not an epidemic will occur, which happens in the deterministic case when R_0 > 1), (2) the initial rate of increase of an epidemic, (3) the critical vaccination threshold (i.e., what fraction of the population you need to vaccinate to prevent an outbreak), (4) the endemic equilibrium of an infection (i.e., the fraction of the population that is infected in between outbreaks), and (5) the final size of the epidemic (i.e., the fraction of the total population that is ever infected when the epidemic is over).

Thus, for a novel outbreak, it's good to have an idea of R_0. I've been a bit out of the loop this summer and haven't seen any estimates so I figured that I would see what I could do. I fully realize that someone may have already done this and that I am not yet aware of it. I also recognize that, if someone has done this, they've probably done it better. This is a blog, not a peer-reviewed paper, and I am away from my usual resources, so please take this in the back-of-the-envelope spirit in which it is intended. I reserve the right to retract, etc. I will also post the R code that I used to make the calculations. I hope that this may prove helpful to others interested in the dynamics of outbreaks.

In their terrific (2003) paper on the SARS outbreak, Marc Lipsitch and colleagues provided a method for estimating the reproduction number from outbreak data. Note that this is a more generalized reproduction number, which we call R, than is the basic reproduction number, R_0. The key difference is that a reproduction number can be calculated at any point in an outbreak, whereas R_0 is only technically correct at the outset (the zero index in R_0 indicates the "generation" of the outbreak where "0" refers to the index case, a.k.a., "patient zero"). I've simply used the count of total cases from this week. It is straightforward to extend the calculation to previous counts. I haven't yet had a chance to do this because there is no convenient collection of data that I can find with my current access constraints.

The method involves equating R_0 for a simplified SEIR system to the observed rate of increase of the outbreak at some point in time t, using the fact that the reproduction number is approximately equivalent to the growth rate of the epidemic. See the supplementary information from Lipsitch et al. (2003) for details of the method. In brief, we calculate the dominant eigenvalue of the linearized SEIR model, for which it is straightforward to write an analytical formula, and equate this to log[Y(t)]/t, the empirical growth rate of the epidemic (where Y(t) is the cumulative number of cases at time t). Lipsitch et al. (2003) note that using the standard formula for the characteristic equation of the eigenvalues of the linearized SEIR model, we can solve for the reproduction number as:

 R = 1 + V \lambda + f(1-f) (V \lambda)^2,

where V is the serial interval (i.e., the summed duration of the incubation period, L, and the duration of the infectious period, D), \lambda is the positive root of the characteristic equation which we set equal to \log[Y(t)]/t, and f is the ratio of the infectious period of the serial interval.

I got the case data from the weekly WHO outbreak report for 11 August 2014. For this week Y(t)=1848. For the start time of the epidemic in the currently afflicted countries, I used the date of 10 March 2014, taken from this week's NEJM paper by Blaize et al. (2014). For the serial interval data, I used the values provided by the Legrand et al. (2007). Because Legrand et al. (2007) provide mean values of the relevant parameters -- and this is a different epidemic -- I used a variety of values for D and L to calculate R. It turns out that it doesn't matter all that much; the estimates of R are pretty stable.

I plot the values of R against the duration of the latent period. The different lines are for the different values of the duration of infectiousness. R increases with both. What we see is that at this point in the epidemic at least, R ranges from around 1.3 to 2.6, depending on specifics of the course of the disease. This is not all that high -- about the same as various flavors of influenza and considerably less than, say, pertussis. This is good news for potential control, if we could just rally some more international support for control of this serious infection...

Ebola-R0-plot1

 

Here is the R code for doing the calculations and creating this figure:

R:
  1. library(lubridate)
  2. # number of cases as of 11 August 2014
  3. # http://www.who.int/csr/don/2014_08_11_ebola/en/
  4. cases <- 1848
  5.  
  6. # start of epidemic in Guinea: 10 March 2014
  7. # Blaize et al. (2014), NEJM. DOI: 10.1056/NEJMoa1404505
  8. s <- dmy("10-03-14")
  9. e <- dmy("11-08-14")
  10. t <- e-s
  11. # Time difference of 154 days
  12.  
  13. ## incubation period 2-21 days
  14. ## http://www.who.int/mediacentre/factsheets/fs103/en/
  15. ## duration of infectiousness: virus detected in of lab-infected man 61 days!
  16.  
  17. ## Legrande et al. (2007) use L=7 and D=10
  18. ## doi:10.1017/S0950268806007217
  19.  
  20. lambda <- log(cases)/t
  21.  
  22. ## From Lipsitch et al. (2003)
  23. ## lambda is the dominant eigenvalue of the linearized SEIR model
  24. ## V is the serial interval V = D + L
  25. ## D is duration infectious period, L is duration of latent period
  26. ## f is the ratio of the the infectious period to the serial interval
  27. ## to solve for R set the eigenvalue equal to the observed exponential growth rate of the epidemic log(Y(t))/t
  28. Rapprox <- function(lambda,V,f) 1 + V*lambda + f*(1-f)*(V* lambda)^2
  29.  
  30. RR <- matrix(0, nr=10, nc=10)
  31. L <- seq(3,12)
  32. D <- seq(5,14)
  33. for(i in 1:length(L)){
  34. for(j in 1:length(D)){
  35. RR[i,j] <- Rapprox(lambda,L[i]+D[j],D[j]/(L[i]+D[j]))
  36. }
  37. }
  38.  
  39. cols <- topo.colors(10)
  40.  
  41. png(file="Ebola-R0-plot1.png")
  42. plot(L, RR[1,], type="n", xlab="Duration of Incubation", ylab="Reproduction Number",ylim=c(1,2.5))
  43. for(i in 1:10) lines(L, RR[i,], lwd=2, col=cols[i])
  44. dev.off()

Ecology and Evolution of Infectious Disease

I am recently back from the 2013 Ecology and Evolution of Infections Disease Conference at Penn State University. This was quite possibly the best meeting I have ever attended, not even for the science (which was nonetheless impeccable), but for the culture. I place the blame for this awesome culture firmly on the shoulders of the leaders of this field and, in particular, the primary motivating force behind the recent emergence of this field, Penn State's Peter Hudson. Since I had attended the other EEID conference at UGA earlier this Spring (another great conference), I had no intention on attending the Penn State conference this year. Then, one day in late March, Nita Bharti asked me if I was going and mentioned, "You know it's Pete's 60th birthday, right?" Well that sealed it; I really had no choice.  I simply had to go if for no other reason than to pay my due respect to this man I admire so greatly. Pete has the most relentless optimism about the future of science and a willingness to make things happen that I have ever encountered and, in this way, has provided me one of my primary role models as a university professor and mentor. He has played a role in developing so many of the brilliant people who make this field so exciting, it's amazing (just a sample that comes immediately to mind: Ottar Bjornstad, Matt Ferrari, Nita Bharti, Marcel Salathé, Isabella Cattadori, Jamie Lloyd-Smith, Shweta Bansal, Jess Metcalf...). Of course, even as I write this, I realize the joint influence of another major player in the field, Bryan Grenfell, formerly of Penn State but now at Princeton, becomes obvious. A great scientist in his own right, Pete is the master facilitator, providing the support (and institutional interference!) that allows young scholars to thrive. He is a talent-spotter extraordinaire.

The talks that made up the bulk of the scientific program were, for the most part, excellent. The average age of the speakers was about 30, maybe just a bit higher. When one attends an academic conference, one typically expects that the major addresses to the collected masses will be by geezers, er, senior scholars in the field. There was a clear play at inversion of the standard model here though. Speakers were clearly chosen because of their trajectories, not their past achievements.  That's pretty great. When I went up for tenure at Stanford, I was told that Stanford does not really care about what you have done; it cares about what you will do. Of course, the best information that the university has about your future work is the work you have already done. This conference embodied this spirit by placing the future (and, in many cases, current) leaders of the field in the key speaking roles while some of the biggest names in ecology, population biology, and epidemiology sat happily in the audience (e.g., joining Hudson and Grenfell were Andy Dobson, Andrew Read, Mick Crawley, Charles Godfray, Mike Boots, Mercedes Pascual, Les Real, Matt Thomas, ...)

The tone set by these great mentors carries through to the whole culture of the conference, where senior people attended the poster sessions, sat with students at lunches and dinners, and schmoozed at the plentiful open-bar mixers. For example, on the first full day of the conference, there was an afternoon poster session that started at 4:30 (we had been in back-to-back sessions since 8:30). This session was preceded by an hour-long poster-teaser session in which grad students and post-docs got up and presented 60-second (and, as Andrew Read noted, not one nanosecond more) teasers of their posters. Bear in mind, this session was entirely comprised of students and post-docs. It was striking that essentially every seat in the house was occupied and all the major players were present. The teasers were great – many were very funny, including a haiku apparently written by a triatomine bug and translated to us by Princeton EEB student Jennifer Peterson.

After the teasers, the conference went en masse to the fancy new Millenium Science Complex (it turns out that Pete Hudson has physical capital projects in addition to human capital ones!). There, participants milled about the 150 posters. After spending quite a bit of time doing this – and dutifully getting pictures of all my lab with their posters – I thought to check the time and realized it was nearly 6:30. The poster session had been going for two hours and nearly everyone was still there, including all the luminaries. It helped that there was free beer. I tweeted my amazement at this realization:

That is, in fact, Princeton's Bryan Grenfell moving fast in the middle of the picture, apparently making a bee-line for Michigan's Aaron King. Andrew Read is in the far background, talking to a poster-presenter (he has that posture).

Scientific highlights for me included Caroline Buckee's talk about measuring mobility in the context of malaria transmission in Kenya and Derek Cummings's talk on the Fluscape Project to measure spatial heterogeneity in influenza transmission in China. I am a long-time fan of this project and it's nice to see the great work that has come out of it. These talks were right in my wheelhouse of interest, but there were plenty other cool ones including Britt Koskella's talk on the dynamics of bacteria and phage on tree leaves.

Stanford was exceedingly well represented at this conference. My lab had no fewer than five posters. Ashley Hazel presented on her work with Carl Simon on modeling gonorrhea transmission dynamics in Kaokoland, Namibia. Whitney Bagge presented her work on remote-sensing of rodent-borne disease in Kenya. Alejandro Feged presented work on the transmission dynamics of malaria in the Colombian Amazon among the indigenous Nukak people. Laura Bloomfield presented her remote sensing and spatial analysis work from our project on the spillover of primate retroviruses in Western Uganda. I closed things out with a minimalist poster on simple graphical models for multiple attractors in vector-borne disease dynamics in multi-host ecologies. In addition to my lab group, Giulio De Leo (with whom I have been running a weekly disease ecology workshop at Woods since winter quarter) was there, helping to bridge all sorts of structural holes in our collective collaboration graphs.

The other thing that comes out of these meetings, especially more intimate ones like EEID, is some actual work on collaborative projects. I managed to find some time to sit down and discuss plans with collaborators as well as do some shameless recruitment for my planned re-submission of the Stanford Biodemography Workshops. I'm really excited about some of these collaborations, including one that brings together my two major areas of interest: biodemography and life history theory and infectious disease ecology.

Oh, and I'm convinced that there must be an interpretive dance component to the Ph.D. exam in the Grenfell lab. This is certainly the most parsimonious explanation for much of what I saw Wednesday night.

Ecology and Evolution of Infectious Disease, 2013

I am recently back from the Ecology and Evolution of Infectious Disease (EEID) Principal Investigators' Meeting hosted by the Odum School of Ecology at the University of Georgia in lovely Athens. This is a remarable event, and a remarkable field, and I can't remember ever being so energized after returning from a professional conference (which often leave me dismayed or even depressed about my field). EEID  is an innovative, highly interdisciplinary funding program jointly managed by the National Science Foundation and the National Institutes of Health. I have been lucky enough to be involved with this program for the last six years. I've served on the scientific review panel a couple times and am now a Co-PI on two projects.

We had a big turn-out for our Uganda team in Athens and team members presented no fewer than four posters. The Stanford social networks/human dimensions team (including Laura Bloomfield, Shannon Randolph and Lucie Clech) presented a poster ("Multiplex Social Relations and Retroviral Transmission Risk in Rural Western Uganda") on our preliminary analysis of the social network data. Simon Frost's student at Cambridge, James Lester, presented a poster ("Networks, Disease, and the Kibale Forest") analyzing our syndromic surveillance data. Sarah Paige from Wisconsin presented a poster on the socio-economic predictors of high-risk animal contact ("Beyond Bushmeat: Animal contact, injury, and zoonotic disease risk in western Uganda") and Maria Ruiz-López, who works with Nelson Ting at Oregon, presented a poster on their work on developing the resources to do some serious population genetics on the Kibale red colobus monkeys ("Use of RNA-seq and nextRAD for the development of red colobus monkey genomic resource").

Parviez Hosseini, from the EcoHealth Alliance, also presented a poster for our joint work on comparative spillover dynamics of avian influenza ("Comparative Spillover Dynamics of Avian Influenza in Endemic Countries"). I'm excited to get more work done on this project which is possible now that new post-doc Ashley Hazel has arrived from Michigan. Ashley will oversee the collection of relational data in Bangladesh and help us get this project into high gear.

The EEID conference has a unique take on poster presentations which make it much more enjoyable than the typical professional meeting. In general, I hate poster sessions. Now, don't get me wrong: I see lots of scientific value in them and they can be a great way for people to have extended conversations about their work. They can be an especially great forum for students to showcase their work and start the long process of forming professional networking. However, there is an awkwardness to poster sessions that can be painful for the hapless conference attender who might want, say, to walk through the room in which a poster session is being held. These rooms tend to be heavy with the smell of desperation and one has to negotiate a gauntlet of suit-clad, doe-eyed graduate students desperate to talk to anyone who will listen about their work. "Please talk to me; I'm so lonely" is what I imagine them all saying as I briskly walk through, trying to look busy and purposeful (while keeping half an eye out for something really interesting!).

The scene at EEID is much different. All posters go up at the same time and the site-fidelity of poster presenters is the lowest I have ever seen. It has to be since, if everyone stuck by their poster, there wouldn't be anyone to see any of them! What this did was allow far more mixing than I normally see at such sessions and avoid much of the inherent social awkwardness of a poster session. Posters also stayed up long past the official poster session. I continued to read posters for at least a day after the official session ended. Of course, it helps that there was all manner of great work being presented.

There were lots of great podium talks too. I was particularly impressed with the talks by Charlie King of Case Western on polyparasitism in Kenya, Maria Diuk-Wasser of Yale on the emergence of babesiosis in the Northeast, Jean Tsao (Michigan State) and Graham Hickling's (Tennessee) joint talk on Lyme disease in the Southeast, and Bethany Krebs's talk on the role of robin social behavior in West Nile Virus outbreaks. Laura Pomeroy, from Ohio State, represented one of the other few teams with a substantial anthropological component extremely well, talking about the transmission dynamics of foot-and-mouth disease in Cameroon. Probably my favorite talk of the weekend was the last talk by Penn State's Matt Thomas. They done awesome work elucidating the role of temperature variability on the transmission dynamics of malaria.

It turns out that this was the last EEID PI conference. Next year, the EEID PI conference will be combined with the other EEID conference which was originally organized at Penn State (and is there again this May). This combining of forces is, I'm sure, a good thing as it will reduce confusion and probably make it more likely that all the people I want to see have a better chance of showing up. I just hope that this new, larger conference retains the charms of the EEID PI conference.

EEID is a new, interdisciplinary field that has grown thanks to some disproportionately large contributions of a few, highly energetic people. One of the principals in this realm is definitely Sam Scheiner, the EEID program officer at NSF.  The EEID PI meeting has basically been Sam's baby for the past 10 years. Sam has done an amazing job creating a community of interdisciplinary scholars and I'm sure I speak for every researcher who has been heavily involved with EEID when I express my gratitude for all his efforts.

On The Dilution Effect

A new paper written by Dan Salkeld (formerly of Stanford), Kerry Padgett (CA Department of Public Health), and myself just came out in the journal Ecology Letters this week.

One of the most important ideas in disease ecology is a hypothesis known as the "dilution effect". The basic idea behind the dilution effect hypothesis is that biodiversity -- typically measured by species richness, or the number of different species present in a particular spatially defined locality -- is protective against infection with zoonotic pathogens (i.e., pathogens transmitted to humans through animal reservoirs). The hypothesis emerged from analysis of Lyme disease ecology in the American Northeast by Richard Ostfeld and his colleagues and students from the Cary Institute for Ecosystem Studies in Millbrook, New York. Lyme disease ecology is incredibly complicated, and there are a couple different ways that the dilution effect can come into play even in this one disease system, but I will try to render it down to something easily digestible.

Lyme disease is caused by a spirochete bacterium Borrelia burgdorferi. It is a vector-borne disease transmitted by hard-bodied ticks of the genus >Ixodes. These ticks are what is known as hemimetabolous, meaning that they experience incomplete metamorphosis involving larval and nymphal stages. Rather than a pupa, these larvae and nymphs resemble little bitty adults. An Ixodes tick takes three blood meals in its lifetime: one as a larva, once as a nymph, once as an adult. At different life-cycle stages, the ticks have different preferences for hosts. Larval ticks generally favor the white-footed mouse (Peromyscus leucopus) for their blood meal and this is where the catch is. It turns out that white-footed mice are extremely efficient reservoirs for Lyme disease. In fact, an infected mouse has as much as a 90% chance of transmitting infection to a larva feeding on it. The larvae then molt into nymphs and overwinter on the forest floor. Then, in spring or early summer a year after they first hatch from eggs, nymphs seek vertebrate hosts. If an individual tick acquired infection as a larva, it can now transmit to its next host. Nymphs are less particular about their choice of host and are happy to feed on humans (or just about any other available vertebrate host).

This is where the dilution effect comes in. The basic idea is that if there are more potential hosts such as chipmunks, shrews, squirrels, or skunks, there are more chances that an infected nymph will take a blood meal on a person. Furthermore, most of these hosts are much less efficient at transmitting the Lyme spirochete than are white-footed mice. This lowers the prevalence of infection and makes it more likely that it will go extinct locally. It's not difficult to imagine the dilution effect working at the larval stage blood-meal too: if there are more species present (and the larvae are not picky about their blood meal), the risk of initial infection is also diluted.

In the highly-fragmented landscape of northeastern temperate woodlands, when there is only one species in a forest fragment, it is quite likely that it will be a white-footed mouse. These mice are very adaptable generalists that occur in a wide range of habitats from pristine woodland to degraded forest. Therefore, species-poor habitats tend to have mice but no other species. The idea behind the dilution effect is that by adding different species to the baseline of a highly depauperate assemblage of simply white-footed mice, the prevalence of nymphal infection will decline and the risk for zoonotic infection of people will be reduced.

It is not an exaggeration to say that the dilution-effect hypothesis is one of the two or three most important ideas in disease ecology and much of the explosion of interest in disease ecology can be attributed in part to such ideas. The dilution effect is also a nice idea. Wouldn't it be great if every dollar we invested in the conservation of biodiversity potentially paid a dividend in reduced disease risk? However, its importance to the field or the beauty of the idea do not guarantee that it is actually scientifically correct.

One major issue with the dilution effect hypothesis is its problem with scale, arguably the central question in ecology. Numerous studies have shown that pathogen diversity is positively related to overall biodiversity at larger spatial scales. For example, in an analysis of global risk of emerging infectious diseases, Kate Jones and her colleagues form the London Zoological Society showed that globally, mammalian biodiversity is positively associated with the odds of an emerging disease. Work by Pete Hudson and colleagues at the Center for Infectious Disease Dynamics at Penn State showed that healthy ecosystems may actually be richer in parasite diversity than degraded ones. Given these quite robust findings, how is it that diversity at a smaller scale is protective?

We use a family of statistical tools known as "meta-analysis" to aggregate the results of a number of previous studies into a single synthetic test of the dilution-effect hypothesis. It is well known that inferences drawn from small samples generally have lower precision (i.e., the estimates carry more uncertainty) than inferences drawn from larger samples. A nice demonstration of this comes from the classical asymptotic statistics. The expected value of a sample mean is the "true mean" of a normal distribution and the standard deviation of this distribution is given by the standard error, which is defined as the standard deviation of the distribution divided by the square root of the sample size. Say that for two studies we estimate the standard deviation of our estimate of the mean to be 10. In the first study, this estimate is based on a single observation, whereas in the second, it is based on a sample of 100 observations. The estimated of the mean in the second study is 10 times more precise than the estimate based on the first because 10/\sqrt{1} = 10 while 10/\sqrt{100} = 1.

Meta-analysis allows us to pool estimates from a number of different studies to increase our sample size and, therefore, our precision. One of the primary goals of meta-analysis is to estimate the overall effect size and its corresponding uncertainty. The simplest way to think of effect size in our case is the difference in disease risk (e.g., as measured in the prevalence of infected hosts) between a species rich area and a species poor area. Unfortunately, a surprising number of studies don't publish this seemingly basic result. For such studies, we have to calculate a surrogate of effect size based on the reported test statistics of the hypothesis that the authors report. This is not completely ideal -- we would much rather calculate effect sizes directly, but to paraphrase a dubious source, you do a meta-analysis with the statistics that have been published, not with the statistics you wish had been published. On this note, one of our key recommendations is that disease ecologists do a better job reporting effect sizes to facilitate future meta-anlayses.

In addition to allowing us to estimate the mean effect size across studies and its associated uncertainty, another goal of meta-analysis is to test for the existence of publication bias. Stanford's own John Ioannidis has written on the ubiquity of publication bias in medical research. The term "bias" has a general meaning that is not quite the same as the technical meaning. By "publication bias", there is generally no implication of nefarious motives on the part of the authors. Rather, it typically arises through a process of selection at both the individual authors' level and the institutional level of the journals to which authors submit their papers. An author, who is under pressure to be productive by her home institution and funding agencies, is not going to waste her time submitting a paper that she thinks has a low chance of being accepted. This means that there is a filter at the level of the author against publishing negative results. This is known as the "file-drawer effect", referring to the hypothetical 19 studies with negative results that never make it out of the authors' desk for every one paper publishing positive results. Of course, journals, editors, and reviewers prefer papers with results to those without as well. These very sensible responses to incentives in scientific publication unfortunately aggregate into systematic biases at the level of the broader literature in a field.

We use a couple methods for detecting publication bias. The first is a graphical device known as a funnel plot. We expect studies done on large samples to have estimates of the effect size that are close to the overall mean effect because estimates based on large samples have higher precision. On the other hand, smaller studies will have effect-size estimates that are more distributed because random error can have a bigger influence in small samples. If we plot the precision (e.g., measured by the standard error) against the effect size, we would expect to see an inverted triangle shape -- or a funnel -- to the scatter plot. Note -- and this is important -- that we expect the scatter around the mean effect size to be symmetrical. Random variation that causes effect-size estimates to deviate from the mean are just as likely to push the estimates above and below the mean. However, if there is a tendency to not publish studies that fail to support the hypothesis, we should see an asymmetry to our funnel. In particular, there should be a deficit of studies that have low power and effect-size estimates that are opposite of the hypothesis. This is exactly what we found. Only studies supporting the dilution-effect hypothesis are published when they have very small samples. Here is what our funnel plot looked like.

Note that there are no points in the lower right quadrant of the plot (where species richness and disease risk would be positively related).

While the graphical approach is great and provides an intuitive feel for what is happening, it is nice to have a more formal way of evaluating the effect of publication bias on our estimates of effect size. Note that if there is publication bias, we will over-estimate our precision because the studies that are missing are far away from the mean (and on the wrong side of it). The method we use to measure the impact of publication bias on our estimate of uncertainty formalizes this idea. Known as "trim-and-fill", it uses an algorithm to find the most divergent asymmetric observations. These are removed and the precision of the mean effect size is calculated. This sub-sample is known as the "truncated" sample. Then a sample of missing values is imputed (i.e., simulated from the implied distribution) and added to the base sample. This is known as the "augmented" sample. The precision is then re-calculated. If there is no publication bias, these estimates should not be too different. In our sample, we find that estimates of precision differ quite a bit between the truncated and augmented samples. We estimate that between 4-7 studies are missing from the sample.

Most importantly, we find that the 95% confidence interval for our estimated mean effect size crosses zero. That is, while the mean effect size is slightly negative (suggesting that biodiversity is protective against disease risk), we can't confidently say that it is actually different than zero. Essentially, our large sample suggests that there is no simple relationship between disease risk and biodiversity.

On Ecological Mechanisms One of the main conclusions of our paper is that we need to move beyond simple correlations between species richness and disease risk and focus instead on ecological mechanisms. I have no doubt that there are specific cases where the negative correlation between species richness and disease risk is real (note our title says that we think this link is idiosyncratic). However, I suspect where we see a significant negative correlation, what is really happening is that some specific ecological mechanism is being aliased by species richness. For example, a forest fragment with a more intact fauna is probably more likely to contain predators and these predators may be keeping the population of efficient reservoir species in check.

I don't think that this is an especially controversial idea. In fact, some of the biggest advocates for the dilution effect hypothesis have done some seminal work advancing our understanding of the ecological mechanisms underlying biodiversity-disease risk relationships. Ostfeld and Holt (2004) note the importance of predators of rodents for regulating disease. They also make the very important point that not all predators are created equally when it comes to the suppression of disease. A hallmark of simple models of predation is the cycling of abundances of predators and prey. A specialist predator which induces boom-bust cycles in a disease reservoir probably is not optimal for infection control. Indeed, it may exacerbate disease risk if, for example, rodents become more aggressive and are more frequently infected in agonistic encounters with conspecifics during steep growth phases of their population cycle. This phenomenon has been cited in the risk of zoonotic transmission of Sin Nombre Virus in the American Southwest.

I have a lot more to write on this, so, in the interest of time, I will end this post now but with the expectation that I will write more in the near future!

 

New Publication, Emerging infectious diseases: the role of social sciences

This past week, The Lancet published a brief commentary I wrote with a group of anthropologist-collaborators. The piece, written with Craig Janes, Kitty Corbett, and Jim Trostle, arose from a workshop I attended in lovely Buenos Aires back in June of 2011. This was a pretty remarkable meeting that was orchestrated by Josh Rosenthal, acting director of the Division of International Training and Research at the Fogarty International Center at NIH, and hosted in grand fashion by Ricardo Gürtler of the University of Buenos Aires.

Our commentary is on a series of papers on zoonoses, a seemingly unlikely topic for about which a collection of anthropologists might have opinions. However, as we note in our paper, social science is essential for understanding emerging zoonoses. First, human social behavior is an essential ingredient in R_0, the basic reproduction number of an infection (The paper uses the term "basic reproductive rate," which was changed somewhere in production from the several times I changed "rate" to "number"). Second, we suggest that social scientists who participate in primary field data collection (e.g., anthropologists, geographers, sociologists) are in a strong position to understand the complex causal circumstances surrounding novel zoonotic disease spill-overs.

We note that there are some challenges to integrating the social sciences effectively into research on emerging infectious disease. Part of this is simply translational. Social scientists, natural scientists, and medical practitioners need to be able to speak to each other and this kind of transdisciplinary communication takes practice. I'm not at all certain what it takes to make researchers from different traditions mutually comprehensible, but I know that it's more likely to happen if these people talk more. My hypothesis is that this is best done away from anyone's office, in the presence of food and drink. Tentative support for this hypothesis is provided by the wide-ranging and fun conversations over lomo y malbec. These conversations have so far yielded at least one paper and laid the foundations for a larger review I am currently writing. I know that various permutations of the people in Buenos Aires for this meeting are still talking and working together, so who knows what may eventually come of it?

This is Just What Greece Needs

Greece was officially deemed malaria-free in 1974. Recent reports, however, suggest that there is ongoing autochthonous transmission of of Plasmodium vivax malaria. According to a brief report from the Mediterranean Bureau of the Italian News Agency (ANSAmed), 40 cases of P. vivax malaria have been reported in the first seven months of 2012. Of these 40, six had no history of travel to areas known to be endemic for malaria transmission. The natural inference is thus that they acquired it locally (i.e., "autochthonously") and that malaria may be back in Greece.

More detail on the malaria cases in Greece can be found on this European Centre for Disease Prevention and Control website. The actual ECDC report on autochthonous malaria transmission in Greece can be found here. A point in that report that is not mentioned in the ANSAmed newswire is that 2012 marks the third consecutive year in which autochthonous transmission has been inferred in Greece. So much for Greece being malaria-free.

Three Questions About Norms

Well, it certainly has been a while since I've written anything here. Life has gotten busy with new projects, new responsibilities, etc. Yesterday, I participated in a workshop on campus sponsored by the Woods Institute for the Environment, the Young Environmental Scholars Conference. I was asked to stand-in for a faculty member who had to cancel at the last minute. I threw together some rather hastily-written notes and figured I'd share them here (especially since I spoke quite a bit of the importance for public communication!).

The theme of the conference was "Environmental Policy, Behavior, and Norms" and we were asked to answer three questions: (1) What does doing normative research mean to you? (2) How do your own norms and values influence your research? (3) What room and role do you see for normative research in your field? So, in order, here are my answers.

What does doing normative research mean to you?

I actually don't particularly like the term "normative research" because it sounds a little too much like imposing one's values on other people. I am skeptical of the imposition of norms that have more to do with (often unrecognized) ideology and less about empirical truth – an idea that was later reinforced by a terrific concluding talk by Debra Satz. If I can define "normative" to mean with the intent to improve people’s lives, then OK.  Otherwise, I prefer to do "positive" research.

For me, normative research is about doing good science. As a biosocial scientist with broad interests, I wear a lot of hats. I have always been interested in questions about the natural world, and (deep) human history in particular. However, I find that the types of questions that really hold my interest these days are more and more engaged in the substantial challenges we face in the world with inequality and sustainability. In keeping with my deep pragmatist sympathies, I increasingly identify with Charles Sanders Pierce's idea that given the "great ocean of truth" that can potentially be uncovered by science, there is a moral burden to do things that have social value. (As an aside, I think that there is social value in understanding the natural world, so I don’t mean to imply a crude instrumentalism here.) In effect, there is a lot of cool science to be done; one may as well do something of relevance.  I personally have little patience for people who pursue racist or otherwise socially divisive agendas and cloak their work in a veil of  free scientific inquiry.  This said, I worry when advocacy interferes with intellectual fairness or an unwillingness to accept that one's position is not actually true.

I think that we are fooling ourselves if we believe that our norms somehow don't have an effect on our research.  Recognizing what these norms that shape your research – whether implicitly or explicitly – helps you manage your bias. Yes, I said manage. I'm not sure we can ever completely eliminate it. I see this as more of a management of a necessary trade-off, drawing an analogy between the practice of science and a classic problem in statistics, between bias and variance. The more biased one is, the less variance there is in the outcome of one’s investigation. The less bias, the greater the likelihood that results will differ from one’s expectations (or wishes). Recognizing how norms shape our research also deals with that murky area of pre-science: where do our ideas for what to study come from?

How do your own norms and values influence your research?

Some of the the norms that shape my own research and teaching include:

transparency: science works best when it is open. This places a premium on sharing data, methods, and communicating results in a manner that maximizes access to information. As a simple example, this norm shapes my belief that we should not train students from poor countries in the use of proprietary software (and other technologies) that they won't be able to afford when they return to their home countries when there are free or otherwise open-source alternatives.

fairness: this naturally includes a sense of social justice or people playing on an equal playing field, but it also includes fairness to different ideas, alternative hypotheses, the possibility that one is wrong. This type of fairness is essential for one's credibility as a public intellectual in science (particularly supporting policy), as noted eloquently in this interview with Dick Lewontin.

respect for people's ultimate rationality: Trying to understand the social, ecological, and economic context of people's decision-making, even if it violates our own normative – particularly market-based economic – expectations.

flexibility: solving real problems means that we need to be flexible in our approach, willing to go where the solutions lead us, learning new tools and collaborating. Flexibility also means a willingness to give up on a research program that is doing harm.

good-faith communication: I believe that there is no room for obscurantism in the academy of the 21st century. This includes public communication. There are, of course, complexities here with regard to the professional development of young scholars.  One of the key trade-offs for young scholars is the need for professional advancement (which comes from academic production) and activism, policy, and public communication. Within the elite universities, the reality is that neither public communication nor activism count much for tenure. However, as Jon Krosnick noted, tenure is a remarkable privilege and, while it may seem impossibly far away for a student just finishing a Ph.D., it’s not really. Once you prove that you have the requisite disciplinary chops, you have plenty of time to to use tenure for what it is designed for (i.e., protecting intellectual freedom) and engaging in critical public debate and communication.

humility: solving problems (in science and society) means caring more about the answer to a problem than one's own pet theory. Humility is intimately related to respect for others' rationality.  It also means recognizing the inherently collaborative nature of contemporary science: giving credit where it is due, seeking help when one is in over one’s head, etc. John DeGioia, President of Georgetown University, quoted St. Augustine in his letter of support for Georgetown Law Student, Sandra Fluke against the crude attacks by radio personality Rush Limbaugh and I think those words are quite applicable here as well.  Augustine implored his interlocutors to "lay aside arrogance" and to "let neither of us assert that he has found the truth; let us seek it as if it were unknown to both." This is not a bad description of the way that science really should work.

What room and role do you see for normative research in your field?

I believe that there is actually an enormous amount of room for normative research, if by "normative research," we mean research that has the potential to have a positive effect on people's lives. If instead we mean imposing values on people, then I am less sure of its role.

Anthropology is often criticized from outside the field, and to a lesser extent, from within it for being overly politicized. You can see this in Nicholas Wade’s critical pieces in the New York Times Science Times section following the American Anthropological Association’s executive committee excising of the word "science" from the field’s long-range planning document. Wade writes,

The decision [to remove the word 'science' from the long-range planning document] has reopened a long-simmering tension between researchers in science-based anthropological disciplines — including archaeologists, physical anthropologists and some cultural anthropologists — and members of the profession who study race, ethnicity and gender and see themselves as advocates for native peoples or human rights.

This is a common sentiment. And it is a complete misunderstanding. It suggests that scientists can't be advocates for native peoples or human rights.  It also suggests that one can't study race, ethnicity, or gender from a scientific perspective.  Both these ideas are complete nonsense.  For all the leftist rhetoric, I am not impressed with the actual political practice of what I see in contemporary anthropology. There is plenty of posturing about power asymmetries and identity politics but it is always done in such a mind-numbingly opaque language and with no apparent practical tie-in to policies that make people's lives better. And, of course, there is the outright disdain for "applied" work one sees in elite anthropology departments.

Writing specifically about Foucault, Chomsky captured my take on this whole mode of intellectual production:

The only way to understand [the mode of scholarship] is if you are a graduate student or you are attending a university and have been trained in this particular style of discourse. That's a way of guaranteeing...that intellectuals will have power, prestige and influence. If something can be said simply, say it simply, so that the carpenter next door can understand you. Anything that is at all well understood about human affairs is pretty simple.

Ultimately, the simple truths about human affairs that I find anyone can relate to are subsistence, health, and the well-being of one’s children. These are the themes at the core of my own research and I hope that the work I do ultimately can effect some good in these areas.

New Grant, Post-Doc Opportunity

Biological and Human Dimensions of Primate Retroviral Transmission
One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.
We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.
We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.
Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.
The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from "smoldering" subclinical infections to full-fledged pandemics.

I am thrilled to report that our latest EID project proposal, Biological and Human Dimensions of Primate Retroviral Transmission, has now been funded (by NIAID nonetheless!).  I will briefly describe the project here and then shamelessly tack on the full text of our advertisement for a post-doc to work as the project manager with Tony Goldberg, PI for this grant, in the College of Veterinary Medicine, University of Wisconsin, Madison.  This project will complement the ongoing work of the Kibale EcoHealth Project. The research team includes: Tony, Colin Chapman (McGill), Bill Switzer (CDC), Nelson Ting (Iowa), Mhairi Gibson (Bristol), Simon Frost (Cambridge), Jennifer Mason (Manchester), and me. This is a pretty great line-up of interdisciplinary scholars and I am honored to be included in the list.

Biological and Human Dimensions of Primate Retroviral Transmission

One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.

We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.

We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.

Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.

The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from "smoldering" subclinical infections to full-fledged pandemics.

Post Doctoral Opportunity

The Goldberg Lab at the University of Wisconsin-Madison invites applications for a post-doctoral researcher to study human social drivers of zoonotic disease in Sub-Saharan Africa.   The post-doc will be an integral member of a new, international, NIH-funded project focused on the biological and human dimensions of primate infectious disease transmission in Uganda, including social drivers of human-primate contact and zoonotic transmission.  This is a unique opportunity for a post-doctoral scholar with training in the social sciences to study human-wildlife conflict/contact and health and disease in a highly relevant ecological setting.  The following criteria apply.

  1. Candidates must have completed or be near to completing a PhD in the social sciences, in a discipline such as anthropology, geography, sociology, behavioral epidemiology, or a relevant discipline within the public health fields.
  2. Candidates must have a demonstrated interest in health and infectious disease.
  3. Candidates must have prior field experience in Sub-Saharan Africa.
  4. Candidates must be willing to relocate to Madison, Wisconsin for three years.
  5. Candidates must be willing to spend substantial time abroad, including in Sub-Saharan Africa and at partner institutions in the United Kingdom.
  6. Candidates must have experience with collection and analysis of both quantitative and qualitative data.  Familiarity with methods such as social network analysis, GIS, participatory methods, and survey design would be advantageous.

The successful candidate will help lead a dynamic international team of students and other post-docs in a multi-institutional, multidisciplinary project.  Duties involve a flexible combination of fieldwork, analyses, and project coordination, in addition to helping to mentor students from North America, Europe, and Africa.  The successful applicant will be expected to explore new research directions of her/his choosing, assisted by a strong team of collaborators.

University of Wisconsin-Madison is a top-notch institution for research and training in the social and health sciences.  Madison, WI, is a vibrant city with outstanding culture and exceptional opportunities for outdoor recreation.

Applicants should send a current CV, a statement of research interests and qualifications (be sure to address the six criteria above), and a list of three people (names, addresses, e-mails) who can serve as references.

Materials and inquiries should be sent to Dr. Tony L. Goldberg (tgoldberg@vetmed.wisc.edu).  Application materials must be received by September 12, 2011 for full consideration; the position is available starting immediately and requires a three-year commitment.