Ask the faculty: how to be successful in academia?

Interested in an academic career path? We hear a lot about the widening gap in the number of PhDs awarded and the number of academic jobs available. How can we improve our prospects for landing one of those coveted tenure track faculty positions? The best people to ask are the people that have already landed one of these positions: our own CIDD faculty. Continue reading

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CIDD Graduate Lunch- Dr. Samuel Scarpino

Please RSVP for CIDD lunch (12pm) on 2/9/2017  with Dr. Samuel Scarpino after his talk.

Great opportunity to talk to the speaker in a casual group setting.

Lunch is provided if you RSVP.


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Andrew Read’s talk about talking

Giving talks can be hard. Giving talks about giving talks might be even harder. Andrew Read pulled it off this morning when he spoke at this month’s CGSA workshop on “How to give a great talk.”  Continue reading

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Friday Links (2/3/2017)

Capture.PNGHappy Friday! This week’s theme: vintage posters of infectious diseases. A bit short for this Friday (I procrastinated), but hopefully you’ll find something interesting.

Continue reading

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CIDD Graduate Lunch- Barbara Han

Barbara Han will be coming tomorrow to give her talk and meet with grad student after!

This is a great opportunity to meet with her in a group setting.

Please RSVP for the lunch right after the seminar.

Lunch is provided if you RSVP.


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Go get lost! (in thought, that is)

I was struck by something a professor said to my Classical Ecology class in the fall of 2016: “I took my biology undergraduate students outside for class one day. We sat down in the lawn near a tree and I asked them what species the tree was. Guess what? None of them could answer! Not one! This was an upper-level biology course mostly with biology majors!” He was outraged.

Research highlighting the important connections between people and nature span decades (WHO 1946, Kaplan 1995, Clay 2001, Diaz et al. 2006, Cardinale et al. 2012, Sandifer et al. 2014, and many more), yet today we seem more disconnected than ever (e.g. biology majors cannot identify a common tree species that he/she walks by everyday). I’m sure we are all aware of the benefits a simple walk outside may provide, ranging from psychological to physical, but what does this have to do with CIDD?

Here at CIDD, lab work is a major underpinning of research. However, context outside of a microscope may be equally as important. CIDD is exceptional in that students, post-docs, and professors all manage to get out to their study sites (whether it be across the globe or at a farm across town). They collaborate with local people and local governments to address the needs of the people and areas their research affects most. These connections are imperative; you cannot learn from a book what you can learn from the field. If you are wondering why your creativity is at a lull, consider visiting your field site or taking a stroll outside. This is how the Legends began their thinking, and the most acclaimed and seasoned scientists at CIDD will tell you the same (at least in my experience).

So, how do we start to connect our passions and our research to the actual study system? It might not be as hard as you think! ALL of our research actually takes place in a natural system, yet sometimes it is easy to forget that when you are looking at computer screens and equipment all day. You are doing what you’re doing because that disease impacts humans, animals, plants, insects, or all of the above! I am lucky enough to do field work on a regular basis; I hike around and watch animals for extended periods of time and ponder why they do what they do. Not everyone can visit his or her study system regularly (or maybe even at all!), but you can take a break from the lab, clear your mind, and let thoughts flow more freely. Who knows, if you happen to be outside a mosquito may bite you, giving you the malaria-related epiphany you have been waiting for!

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Why go to journal club?

Journal club at the CIDD has had a sporadic history. In theory, we like the idea. In practice, we disagree on which papers to read, we sometimes don’t read the papers and we struggle with numbers. This year we’ve decided to join forces with biology grad students who started a symbiosis journal club last fall to fill the journal club niche that has been open in the CIDD community.

The symbiosis journal club meets once per month, usually on the first Tuesday. Symbioses include parasite-host relationships but also extend to mutualisms. CIDD grads might not spend much time thinking about mutualism, but these relationships often impact diasease outcomes, as has been shown nicely in mutualistic human gut microbes preventing colonization of pathogenic bacteria and salamanders receiving protection from chitrid fungus by associating with a mutualist skin bacteria. Learning more about mutualism might be useful for us disease folk.

Why else should we go to journal club? Here are 5 reasons to attend symbiosis journal club next month.

  1. It’s fun. We drink beer and talk about science.
  2. Learn something new. This month I learned that dinoflagellates can acquire new genes through horizontal gene transfer. They’re eukaryotes. I didn’t know eukaryotes did this! They can basically collect DNA from things they eat and take it into their nucleus. Crazy crazy.
  3. Practice for candidacy. If you’re a first or second year, journal club is a great venue to practice reviewing papers from our field before taking your candidacy exam.
  4. Get outside your comfort zone. Whenever I see a paper that was written by a geneticist, I get a little bit scared. Journal club is an opportunity to read papers that are written in a different “language”. Grad students from different lab groups and research interests have the chance to teach each other concepts that would be difficult to get through alone.
  5. Meet friends and collaborators. We are our own future colleagues. Talking about science in a more casual setting than work, and with scientists we don’t usually work with is a great chance for making interdisciplinary connections and broadening social horizons.

February’s journal club is on Tuesday, February 7 at 6 p.m. at Local Whiskey downtown. The paper to be discussed is on a symbiosis between a fruit fly and cactus. Read the paper here and see the write-up in Discover magazine to get an idea of how the media communicated the science.

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Friday Links (1/27/2017)



This Friday, it’s mainly book suggestions. It’s like Oprah’s Book Club- except I’m running it. It’s Treat Yo Self 2017 and you deserve some great science books.


Disease related news



Rstats/Data Visualization

The Genetic Map Comparator, a user friendly bioinformatics application  (Shiny) to display and compare genetic maps. (Useful for: Bioinformatics students)

R for beginners: basic graphics code to produce informative graphseveryone can code in R. Here is a tutorial for creating a 3D scatterplot. I suggest following the blog as it has a lot of tutorials (Useful for: Just starting out R)

The Annual Rstudio Conference happened last week and here are some tips and tricks written by some of the attendees.(Useful for: Anyone interested in seeing what new things are happening in the R world)

Visual Resume {VisualResume}, a new R package (and Shiny App)  for creating a really great looking resume. From meth-making chemistry teachers to new PhD holders, this might be great for a personal website.


Science Arts/Science Communication

Jen Burgess is a science illustrator that I have been following on Twitter. She collaborates with researchers all over the world to draw some very beautiful artworks. I can’t praise her work enough so I’ll let her art do the talking. Follow her on Twitter as well!

What can the anti-vaccination movement teach us about improving the public’s understanding of science? An interesting opinion piece by Jeanne Garbarino (PhD, Director of Science Outreach at Rockfeller University) on why anti-vax groups have such a hold of our nation.

Such sites [anti-vaccination sites] skillfully cultivate feelings of trust and credibility by aiming their message to hit the more human side of things. These sites get human behavior, while pro-objective evidence sites often do not.

The sound of climate change from the Amazon to Arctic, a string quartet with each instrument representing different latitudes of the world. They play together to demonstrate how rapidly temperatures have been fluctuating.

We often think of the sciences and the arts as completely separate — almost like opposites, but using music to share these data is just as scientifically valid as plotting lines on a graph,” he says. “Listening to the violin climb almost the entire range of the instrument is incredibly effective at illustrating the magnitude of change — particularly in the Arctic which has warmed more than any other part of the planet.

Book suggestions


It’s the best day of the year!

Calculus in Context, a free introductory calculus book for anyone looking to brush up on their calculus. Highly, highly recommend for life-science people because it focuses more on the context rather than pure theory or pure application. Also Spencer highly approved of it (and he does a lot of mathematics!)

Data Analysis with R, there was a free giveaway and I nabbed one. Looks like it has great reviews (I skimmed through it and it seems very substantial!). So if anyone wants to add this to their Ebook collection, go ahead and download from my Google Drive. I would recommend for any beginner/intermediate users of R.

Weapon of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, “Models are opinions embedded in mathematics,”author Cathy O’Neil declares as she pinpoints the dangers of mathematical models that run our lives. From the lack of transparency to the significant problems that arise with fitting models from past data, O’Neil takes a stand for ethics in our data-driven world.

Theory-Based Ecology: A Darwinian ApproachI think this should be the standard textbook for all ecology students. All ecological concepts are explained with an Darwinian insight and combines genetics, ecology, evolution, and mathematics into this amazing textbook. Celebrates both theoretical frameworks and empirical works equally. If you’re also interested in learning mathematical concepts used in ecology, this is quite a wonderful resource!

The general principles behind formalized ecological theories correspond to principles regularly recurring in Darwin’s texts (the rule of geometric increase, the doctrine of Malthus, and the inevitable connection of similarity and the strength of competition that lead to the principle of natural selection, and the principle of character divergence)


Mathematical explanation example

Dear Data: Two information designers send each other postcards… but of the data they collected each week. A really fascinating project because they redefine what “data” is to them. They say data was not only a dataset during this project, but a “souvenir, a love letter, and a self-portrait.”

Each week, and for a year, we collected and measured a particular type of data about our lives, used this data to make a drawing on a postcard-sized sheet of paper, and then dropped the postcard in an English “postbox” (Stefanie) or an American “mailbox” (Giorgia)!



Now you can make s-CAT-terplots. Nothing says my research is important then a plot with cats on them. 11 types of cats. 11! CatterPlots





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Multi-scale models in infectious disease epidemiology

This post may be subject to revision, contingent upon on-going discussion. The current date of last modification is March 24, 2016.

Models that link within- and between-host processes are potentially important tools in disease ecology, but the disease research community remains somewhat divided about when and how they should be used. CIDD members discussed cross-scale modeling at a recent CIDD lunch. While the discussion was fairly free-wheeling and ad hoc, a few consistent themes emerged. This post draws on those themes, as well as a few of my own observations on the role – realized or potential – that multi-scale modeling plays in modern infectious disease epidemiology.

My background is in empirically-driven between-host modeling, and that baseline partially determines the challenges I identify here. This perspective is incomplete, and I hope this post will serve as a jump-off point for a broader discussion among researchers with more varied backgrounds.

The post consists of the following:

  1. Introduction
  2. What impedes multi-scale model development?
  3. When is a multi-scale model worth the effort?
  4. Parting thought

I’ve included a few references at the end, along with some comments from experts with other perspectives.

Introduction (next) (to top)

Multi-scale models use within-host dynamics to drive between-host epidemiological (or evolutionary) processes. This is implemented by treating state-transition rates in the between-host process (for example, transmission and disease-induced mortality rates in a classic SIR model) as functions of dynamics within the host. In their 2008 TREE paper, Mideo, Alizon, and Day note that multi-scale models are only essential in cases where reciprocal feedbacks link within- and between-host processes (for example, this occurs when SIR state-transitions depend on within-host dynamics, and also within-host dynamics depend on conditions at the population level). Mideo, Alizon, and Day observed that at the time they wrote, these reciprocal feedbacks were not actually included in the majority of publications on multi-scale models for pathogen evolution; in these cases, the resulting inferences could have emerged from examination of a single scale.

Yet in many situations, within- and between-host processes do have reciprocal feedbacks, whether our models account for them or not. For example, reciprocal dependencies exist in systems where within-host dynamics depend on initial dose, and where individual infectiousness or morbidity depend on both immune and pathogen dynamics. Since both of these dependencies are pervasive among infectious diseases, there is reason to think that multi-scale models may have broad applicability.

The apparent biological plausibility of multi-scale models raises two questions. 1) Which systems stand to benefit the most from multi-scale studies approaches? 2) If multi-scale models have so much utility, why aren’t we using them more frequently?

What impedes multi-scale model development? (next) (to top)

I see three clear barriers inhibiting multi-scale model development, each of which I describe below.

  1. Language and style
  2. Articulating model objectives
  3. Gathering and incorporating data

1. Language and style (next) (to top)

Within- and between-host modeling cultures are fairly distinct from one another, and for the inexperienced practitioner this poses a challenge for model design. Researchers trained in between-host dynamics come from a culture in which the vast majority of models derive from a single basic structure, Susceptible-Infected-Recovered (SIR). A rich knowledge of SIR-like models is crucial for disease ecologists, but comes with two important limitations.

First, SIR-focused modelers may not be particularly well-versed in other kinds of consumer-resource interactions, like mutualisms, competition, or predator-prey dynamics (for a good synthesis of consumer-resource models, see Laffery et al. Science 2015). This limits the set of model structures we draw from when considering the various co-stimulatory relationships governing interactions between pathogen and host immune responses.

Second, population-level disease modelers are used to working on models with relatively well-defined states. S, I, and R are good jump-off points for almost every between-host disease model. Although one state, pathogen population size, is an obvious fixture of most within-host models, constraining the host immune response to a limited number of appropriate compartments is really difficult. This issue is somewhat compounded by the immunology community’s emphasis on specificity: binning different groups of immune markers into single entities is counter to current research in that academic culture. I suspect that the juxtaposition of population-level disease modeling’s tendency to bin and immunology’s tendency to split states stymies many multi-scale modeling efforts before they even begin.

2. Articulating model objectives (next) (to top)

Disease researchers trained on between-host processes (or at least, the one writing this post) sometimes have a tendency to model first, and ask questions later. Because the equilibrium conditions of the SIR model and its various derivatives have been so extensively studied, just constructing and parameterizing an SIR-like model can offer a number of useful insights for a given system. Selecting which insight is most important from the outset of the modeling endeavor isn’t always imperative.

The ability to choose model goals after-the-fact is less available for within-host – and consequently, multi-scale – models. Modeling first and asking questions later isn’t feasible, since the set of possible model states and configurations in-host is so broad. Instead, within-host modelers tend to emphasize developing a specific question from the outset of an investigation, and selecting model states aimed at addressing that particular question.

3. Gathering and incorporating data (next) (to top)

There is a huge volume of published data on within-host dynamics for all kinds of pathogen-host combinations (I can attest that this is true from Mycoplasma ovipneumoniae, which very few people care about, so I’m willing to postulate that it’s probably true for whatever agent you’re studying right now). Making sense of these studies, however, isn’t trivial, and this is especially true for an outsider with limited microbiological and immunological literacy. Here’s why: unlike many between-host datasets, which are subject to relatively few (well-studied) assumptions with respect to data collection (e.g., sightability, population closure, etc.), the assumptions underlying many within-host datasets – which often directly manipulate the host’s health through gene knock-outs, particular nutritional regimens, and chronic stress – are daunting. Understanding and adjusting for potential biases also requires a reasonable working knowledge of a wide range of immunological and microbiological methods and their idiosyncrasies (how accurate IS that ELISA, anyway?), a good understanding of the study conditions, and an appreciation for how those conditions differ from conditions in nature. Furthermore, longitudinal datasets, or other data that capture temporal dynamics within hosts are relatively rare in immunology. For ecological modelers whose data gold standard is rich, replicated timeseries, a lack of analogous data within the host seems acutely problematic.

However, this need not be the case. Other data structures (especially multi-sectional datasets) do contain relevant (albeit sometimes less) information, and replication under slightly varied conditions is exactly the pretext for Bayesian inference. The empirical challenge – largely solved through partially observed Markov process modeling, approximate Bayesian computation and other approaches, still rarely implemented – is to figure out ways to appropriately leverage these less-than-ideal datasets. Conflicting attitudes about when particular datasets can and should be used may impede this process, but the statistical infrastructure for these models already exists.

When is a multi-scale model worth the effort? (next) (to top)

Despite the challenges, I am convinced that multi-scale models are crucial to advancing infectious disease epidemiology. Here’s why:

We’re currently very good at modeling epidemics for which Infected is Infected is Infected. Despite the clamour surrounding Ebola modeling, I think between-host models generally perform very well for acutely immunizing infectious, in which the preponderance of heterogeneity is attributable to host behavior.


With the notable exception of HIV (perhaps a special case, given that so much heterogeneity in secreted load can be explained by infection age), we do not have good population-level models of most chronic pathogens. Population-level epidemiology of pathogens like Herpes Simplex Virus, cryptosporidium, or tuberculosis that are typically latent but occasionally “flare up” (apparently at random) within particular hosts is not well-characterized*. Understanding transmission dynamics of these pathogens requires understanding the within-host processes that allow for re-emergence, and the population-level consequences of increased individual infectiousness. In short, what allows sporadic epidemic transmission of a pathogen that was locally endemic the whole time. The answer is sometimes pathogen evolution, but I posit that other factors may also contribute. Within-host complexities that lead to sustained infectious periods and pathogen re-invigoration has been well-reviewed (for example, see the excellent review by Virgin et al. 2009), but I have yet to see these ideas extensively translated into population-level predictions.

I suspect that multi-scale modeling may be most useful at a different point in a research program than between-host models. While between-host models often occupy the ends of epidemiological projects (e.g., though assessing R_0’s sensitivity to particular aspects of the disease process), and lead directly to management recommendations, multi-scale models might be best used to generate hypotheses, identify key pieces of missing information, and constrain the relevant space of unknown parameters for future experimental investigation. As a consequence, model outputs might shift from estimation-focused between-host models to a focus on mathematical sensitivity. This transition will require some flexibility on the part of practitioners; however, although the benefits are not entirely clear from the outset, multi-scale approaches hold a lot of promise, especially for pathogens that continue to defy clear population-level modeling.

Parting thought (next) (to top)

Perhaps the real challenge, then, is that multi-scale models might be most useful in situations that defy standard assumptions both within and between-hosts. I think that the following kinds of infections fall into this group:

  1. infections with relevant spatial compartmentalization within the host (e.g., HepC, HSV)
  2. epithelial infections (e.g., infections of the respiratory, GI, or reproductive tracts)
  3. infections that stimulate autoimmune elements of the immune system
  4. infections in which immune regulation misfires (either by over- or under-response)

References (next) (to top)

Mideo N, Alizon S, Day T. 2008. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends in Ecology and Evolution 23(9); 511-517.

Handel A, Rohani P. 2015. Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge. Philosophical Transactions of the Royal Society B 370; 20140302

Lafferty KD, DeLeo G, Briggs CJ, Dobson AP, Gross T, Karis AM. 2015. A general consumer-resource population model. Science 349(6250); 854-857.

Virgin HW, Wherry EH, Ahmed R. 2009. Redefining chronic viral infections. Cell 138(1); 30-50.

Comments and discussion (to top)

Jessica Conway made the following comment, which I’ll quote directly:

[You claim that] “Understanding transmission dynamics of these pathogens requires understanding the within-host processes that allow for re-emergence, and the population-level consequences of increased individual infectiousness.” It doesn’t really, does it? Take HSV. To understand population-level spread, you need outbreak pattern data, length, duration, and frequency. Do you need to know what drives them? Probably not to understand spread. Maybe yes, to determine interventions that minimize spread, but for that you pretty much want to stop flare-ups, and the between-host scale is not helpful. To understand evolution however likely requires both scales.

This is a really good point. In-host models are probably sufficient, so long as initial dose isn’t a critical determinant of infection outcome in the host. If dose is critical, then I think there is an opportunity for feedback across scales that could make multi-scale models helpful.

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CIDD GSA Journal Club 2-9-16

Just a reminder that we will be having our third journal club of the semester this coming Tuesday, February 9th from 9-10 am in W-201 of MSC. This week’s paper is from Britt Glausinger‘s lab and can be found here.

In the future, the schedule and papers chosen for each journal club can be found as PDFs in this folder (open to anyone).

Hope to see you all there!

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