Infectious Disease Elimination

Guest blog: Multi-disciplinary interactions under the Mexican sun – reflections from the ISVEE conference

Guest blog by Tiziana Lembo and Liliana Salvador with contributions from Rowland Kao and Louise Matthews.

What is the role of scientific conference? Is it to present our research and expound upon our scientific philosophies? Is it to hear people talking about the interesting research that they are developing? Or is it to meet old friends and make new ones while also traveling to interesting places? All of these aims and more were fulfilled when a group of us left umbrellas and raincoats behind to travel to sunny and warm Mérida, Mexico, for two stimulating weeks of ISVEE 14 (International Society for Veterinary Epidemiology and Economics) conference and workshops. The sun, heat, margaritas, “Jarana” tunes and dances, and the colourful decorations of the “Día de Muertos” created an ideal atmosphere for productive and enlightening scientific interactions.

Dia de muertos

The beginning of ISVEE 14 coincided with the “Dia de Muertos” (Day of the Dead), an ancient Mexican celebration to remember ancestors, family members and friends who have died. Traditionally, altars (“ofrendas”) are built that are laden with decorations, and favourite foods and beverages of teh departed. Above an altar dedicated to the famous Mexican painter Frida Kahlo de Rivera at La Casa Azul in Mexico City, where Frida Kahlo lived and worked most of her life (photo: Tiziana Lembo)

Every three years, ISVEE provides opportunities for academics from a range of different disciplines, policy-makers and stakeholders from the private sector to come together to share their expertise in innovative research, technological developments, and policy health agendas. By blending a wide range of disciplines to address health issues of global importance, the Boyd Orr Centre has a major role to play in all these discussions. What were our contributions to the ISVEE agenda this year?


Let’s start with our research on the very topical bovine Tuberculosis (bTB), caused by Mycobacterium bovis. Rowland Kao discussed a subject that is very close to his heart – the transformative role of Whole-Genome-Sequencing (WGS) in elucidating complex transmission dynamics and disease maintenance patterns in multi-host systems. He provided examples of how the approach has been used by the Glasgow team and collaborators to expose the role of wildlife in the maintenance and transmission of bTB to cattle in different parts of the world, including Great Britain, the United States, and New Zealand. He contrasted currently available data with optimal data and listed some of the key features of an ideal dataset for WGS approaches, most importantly dense, representative sampling across all important hosts; representative samples across populations, but also the way that evolutionary analyses and model-based epidemiological approaches complement each other in interpreting these data.


Joseph Crisp and Liliana Salvador provided examples of the use of WGS to tackle bTB in New Zealand and US wildlife and cattle populations. Joe showed that the evolutionary substitution rates of M. bovis in his study populations, including cattle, possums and other wildlife are higher than previously thought and that non-cattle reservoirs were heavily involved in the maintenance of M. bovis in the sampled population. Liliana focused on bTB transmission amongst elk, deer and cattle in Michigan, US, and demonstrated that elk is the only one of these species with spatial and temporal clustering of M. bovis. In addition, for the available data, she showed that there is no evidence of transmission between elk and cattle and that cross-species transmission in Michigan is likely due to deer.


Liliana also presented her work on surveillance of bTB in Low Risk Areas (LRAs) in England. She showed that larger herds and herds that receive a high number of animals from high-risk areas are most exposed to infection. She also demonstrated that in LRAs there is no clear advantage of testing herds for bTB more frequently, since it would give no increase in the number of detected breakdowns, but the number of false positive would rise considerably. However, adopting risk-based surveillance, where herds that are at higher risk of infection are targeted, can improve the efficiency of the testing regime by increasing the number of identified cases and reducing the number of herds tested.


An entire session of the conference was dedicated to foot-and-mouth disease (FMD) with a focus on the latest research efforts in Africa, Asia and Latin America. The work of the Boyd Orr Centre on endemic FMD in Tanzania featured prominently. Findings from micro-econometric studies investigating impacts of FMD outbreaks on individual livestock-owning communities were presented by Tom Marsh, a collaborator from Washington State University. These analyses have revealed that FMD outbreaks in cattle cause reductions in milk production, traction capacity and income from livestock sales, and that households would spend more on child education if they were not affected by milk losses due to FMD. Tiziana Lembo’s talk focused on epidemiological studies investigating temporal and spatial FMD virus dynamics in East Africa to devise appropriate control strategies. She showed that four different serotypes (A, O, SAT 1 and SAT 2) are responsible for FMD outbreaks in cattle in northern Tanzania, and that there is a pattern of serotypic dominance over time across Tanzania and Kenya, which allows us to predict the timing of epidemics of specific serotypes. The implications are that livestock vaccination could target given serotypes ahead of expected outbreaks, using monovalent vaccines, which are much more readily available than polyvalent vaccines needed to cover all of the wide range of serotypes circulating in these areas.


In her talk, Louise Matthews tackled the question of whether farmers would adopt a new diagnostic test for early detection of sheep scab at the subclinical stage. The advantages of using the test are that it would allow farmers to detect and treat the disease before clinical signs, reducing production losses, and also reducing transmission to other sheep and flocks. However, the farmers would need to pay for the test and may be reluctant to do so if they believe their flock to be at low risk of infection or if their neighbour is using the test, therefore not posing a transmission risk. These advantages and disadvantages can be assessed using a game theory framework that predicts whether farmers will adopt the test and how that uptake depends on test cost. The outcome was uptake of the test when farmers are at high risk (i.e. when they had experienced clinical sheep scab in the previous year), leading in the long term to a reduction in the proportion of infected farms by around 50%.


Harriet Auty, a collaborator from Scotland’s Rural College, presented research on human African trypanosomiasis caused by Trypanosoma brucei rhodesiense in Tanzania. She talked about the relative importance of different wildlife species in the reservoir community for human trypanosomiasis in multi-host populations of the Serengeti National Park. She showed that species such as bushbuck, reedbuck and impala, which are frequently infected with T. brucei, might play an important role in the reservoir community, even though they are not regular food sources for the tsetse vector. Conversely, elephant or giraffe are frequently fed on but rarely infected, indicating they may play a role in dampening transmission, and suggesting how changes in wild species composition could impact on human disease risk.


As always, ISVEE also provided a forum for conference delegates to update and strengthen their skills in a number of methods and topics through workshops run by academic colleagues from around the world. For instance, Tiziana Lembo benefited from training and discussions in data management and analyses in R organised by the Swedish National Veterinary Institute, as well as in the use of economics for animal health decision-making coordinated by the Royal Veterinary College and collaborators.

Back to the rain and grey skies now, we have many memories and knowledge to treasure from the land of revolution, music and art.


Street mural depicting Emiliano Zapata in Tepoztlan, State of Morelos, Mexico during the Mexican Revolution. Zapata remains an iconic figure in Mexico to this day (Photo: Tiziana Lembo).










ode to life.png

An ode to life (“Viva la Vida”) by the Mexican painter Frida Kahlo de Rivera, La Casa Azul, Mexico City (Photo: Tiziana Lembo).

The research presented and our attendance were funded by: BBSRC / DFID / Scottish Government (Combating Infectious Diseases of Livestock for International Development initiative), MSD Animal Health, and Scottish Universities Life Sciences Alliance (Tiziana Lembo); BBSRC/DFID (Zoonoses in Emerging Livestock Systems) and EPIC (The Scottish Centre of Expertise in Animal Disease Outbreaks); Defra, NSF/BBSRC


Reservoir concepts: axioms and prepositions – Guest post by Rebecca Mancy

Haydon et al.’s (2002) “reservoirs framework” paper provides a structure for understanding reservoirs of infection by distinguishing between maintenance, source and target populations and clarifying the relationship between them (see Viana et al. 2014 Box 1, Figure I for an open access explanation). Reading it for the first time a few years ago, I found myself drawn into testing the structures and the relationships between them, generating new examples, verifying that the framework provided was sufficient to describe them, and checking whether they were topologically equivalent to those depicted in the figure. It felt like learning a new axiomatic system in mathematics. At the time, I paid very little attention to the terminology. The language of squares, circles and arrows seemed sufficient.

The Black Death, probably caused by Yersinia Pestis and depicted here on a panel of the Great Tapestry of Scotland, has long been one of the most iconic examples of a pandemic implicating a reservoir of infection and explanations of its mechanisms still attract considerable scientific interest. Photo:  Alex Hewitt/Trustees of the Great Tapestry of Scotland (GTOS)

The Black Death, probably caused by Yersinia Pestis and depicted here on a panel of the Great Tapestry of Scotland, has long been one of the most iconic examples of a pandemic implicating a reservoir of infection and explanations of its mechanisms still attract considerable scientific interest. Photo: Alex Hewitt/Trustees of the Great Tapestry of Scotland (GTOS)

Although our recent update (Viana et al., 2014) focused primarily on reviewing threads of evidence and discussing ways in which they might be woven into a tapestry to allow us to better identify reservoir systems, its writing has also led to new discussions about the framework and the structural relationships it encompasses. But it has also led to discussions of terminology. Most of these have focused on our use of the term ‘reservoir’. I’ve spent the last couple of hours trawling online dictionaries and etymological sources in order to better understand our use of the word. A foray into another of my favourite worlds: language. Understanding the different meanings of the word might also help us to understand some of the uncertainties that have been raised about the framework.

Most free online English language resources, such as Merriam-Webster and Online Etymology Dictionary, provide fairly limited information on the etymology of the word reservoir beyond a reference to its French origins. The Centre National de Ressources Textuelles et Lexicales, a CNRS resource centre, helps us to trace the term a little further back. According to the entry in their etymological dictionary, the first recorded use of the term dates back to 1510 when it was used to refer to a receptacle for holding a liquid. By 1547, it was being used more generally as a space fitted out for the conservation and storage of provisions, and by 1601 had adopted a figurative meaning, being used to refer to anything capable of serving as a repository. Despite these subtle changes, these uses all relate to the notion of a container employed for purposeful storage. Perhaps surprisingly, it was only in 1742 (in French; and slightly earlier in English according to the OED) that it took on the meaning of a place serving as a natural reserve of something. Yet among modern definitions, even if we exclude epidemiological meanings, we find a third use of the term as a supply of something in which the reservoir no longer refers to the container but to a resource that is contained. I suspect this is the sense in which Acheson employed it in explaining that Winston Churchill

“… still had his glorious sense of words drawn from the special reservoir from which Lincoln also drew, fed by Shakespeare and those Tudor critics who wrote the first Prayer Book of Edward VI and their Jacobean successors who translated the Bible.”

Dean Gooderham Acheson (1961) Of Winston Churchill in Sketches from Life of Men I Have Known.

Actually, what alerted me to the different meanings was not the etymology at all, but prepositions, something else that Churchill is reputed to have been sensitive to. What none of the above sources note is that the distinction between the different meanings of the word reservoir can be detected in its association with particular prepositions. When using it in the sense of a purposeful receptacle, we use the preposition for, such as when we refer to a ‘reservoir for heating oil’; in the sense of a natural reservoir, we would generally employ the preposition of, as we might if talking about a ‘subterranean reservoir of natural gas’. In the case where we want to emphasise the idea that a natural reservoir serves as a supply, we use both prepositions, but the meaning of the word for now changes. In the phrase ‘a subterranean reservoir of natural gas for the population of Scotland’, the word for refers not to the natural gas (as it did in the heating oil example) but to the population due to receive the gas.

In the epidemiological context, the equivalent of a reservoir of natural gas for a population would look something like

A reservoir of [infectious agent] for [target population].

And yet, the epidemiological literature is replete with examples of the equivalent of ‘a reservoir for natural gas’ (i.e. a receptacle into which one puts natural gas). A search in my Mendeley library brings up a list of examples: ‘a potential reservoir for Leishmania’, ‘a reservoir for a coronavirus’, ‘the reservoir for the origin of the SARS epidemic’, ‘a reservoir for emerging infectious diseases’, ‘a reservoir for rabies’ and ‘a reservoir for bovine tuberculosis’. When we write in this way, I am sure that we are simply being imprecise rather than implying a sense of human purpose in the maintenance of these reservoirs. But we really should try to use language a bit better than that.

But how does this distinction relate to the question of how we describe structures using the reservoir framework? Firstly, it explains why we choose to refer to the target in the definition of a reservoir. Basing our definition on that of Haydon et al. (2002), we explain that “A ‘reservoir of infection’ is defined with respect to a target population as ‘one or more epidemiologically connected populations or environments in which a pathogen can be permanently maintained and from which infection is transmitted to the target population’”. Thus, according to the framework, referring to a reservoir without reference to a target constitutes under-specification. Obviously, without maintenance there would be no reservoir; but equally, if there were no target population into which disease spills over then the term maintenance population would fully characterise the system and there would be no need to refer to a reservoir. For example, for a multi-host pathogen such as the virus causing foot-and-mouth disease, referring to buffalo as ‘the reservoir’ makes little sense because the system is under-specified: if we complete the definition by specifying a target, the factual accuracy of the statement “buffalo are the reservoir of FMDV for <target>” depends on the particular target we choose.

More precisely, the framework in Haydon et al. (2002) should be thought of as serving to describe not just reservoirs, but target-reservoir systems. According to the framework, populations and communities are classified in two ways. Firstly, according to their maintenance status as either capable of maintaining the pathogen in the long term or incapable of doing so; and secondly, according to their role in transmission between populations within the target-reservoir system as target, source, or neither. The simplest way to characterise the full system is then to view these dimensions as orthogonal: every population has an attribute from each of the two dimensions.

This construction helps to answer a number of questions that have arisen in discussion with colleagues. For example, it means we may still wish to refer to a reservoir even if the target population is capable of maintenance (or R0 in the target is greater than one). For example, this would be the case if some infections in the target came from other maintenance populations in the system. Furthermore, a source population can be maintenance or non-maintenance. Source populations that are not capable of maintaining a pathogen alone can form an essential or inessential part of a maintenance community, or simply assist in the transfer from the maintenance population to the target. In fact, all three possibilities might be involved in the transmission and persistence of the plague bacterium, Yersinia Pestis, in relation to different flea species and mammalian host communities (Eisen & Gage, 2009; Webb, Brooks, Gage, & Antolin, 2006). One might ask, as Ashford (2003) has, whether or not vectors that do not contribute to pathogen maintenance should be included in the reservoir. As Ashford notes, this particular point could be argued either way; nonetheless, distinguishing between types of vectors is important when designing interventions.

Fundamentally, there are two ways to protect the target: either we prevent maintenance, or we prevent transmission from the maintenance community to the target. As we explain in Viana et al. (2014), there are various ways to achieve these aims, which we refer to as press, pulse and block. However, this categorisation focuses on the implementation rather than the aim. For example, a pulse intervention may consist of culling (to prevent maintenance) or vaccination (to prevent transmission to the target); a block action may employ fences erected between non-target, non-maintenance populations (to prevent community-level maintenance) or between a maintenance community and the target (to prevent transmission to the target).

Simple reservoir-target systems showing the three kinds of vectors. T denotes the target population, V the vector source and P an additional population involved in the target-reservoir system. Arrows indicate transmission between populations, circles represent non-maintenance populations while squares are maintenance populations; maintenance communities are shown with a dashed outline.

Figure 1. Simple reservoir-target systems showing the three kinds of vectors. T denotes the target population, V the vector source and P an additional population involved in the target-reservoir system. Arrows indicate transmission between populations, circles represent non-maintenance populations while squares are maintenance populations; maintenance communities are shown with a dashed outline.

To come back to the importance of distinguishing between vectors that are involved in maintenance and those that are not, an interesting case arises. In Figure 1, although eliminating the vector is effective for different reasons in the three cases, the set of interventions is actually identical (eliminate population P, eliminate the vector population V, block transmission link a, block transmission link b). In the first and third case, eliminating V is effective because it breaks the transmission link to the target; in the second case, its elimination is also prevents maintenance in the community consisting of the vector V2 and population P2.

Ultimately, perhaps the most important questions about the definition and associated framework relate not to the word reservoir in the definition, but to how generally the framework applies. For example, should we use it for situations such as environmental persistence without pathogen reproduction? It seems fairly natural to apply it to when considering to parasites, but should it extend to organisms such as toxin-producing algae or fungi that do not require living matter in order to reproduce? These are all fascinating questions and it should be fun thinking about whether and how to best integrate them.

(With thanks to Daniel Haydon and Mafalda Viana for comments.)

[RRK – Comments can be made here, or addressed directly to rebecca.mancy A T]


Ashford, R. W. (2003). When Is a Reservoir Not a Reservoir? Emerging Infectious Diseases, 9(11), 1495–1496.

Eisen, R. J., & Gage, K. L. (2009). Review article Adaptive strategies of Yersinia pestis to persist during inter-epizootic and epizootic periods. Veterinary Research, 40(1), 1–14.

Haydon, D. T., Cleaveland, S., Taylor, L. H., & Laurenson, M. K. (2002). Identifying reservoirs of infection: a conceptual and practical challenge. Emerging Infectious Diseases, 8(12), 1468–73. Retrieved from

Viana, M., Mancy, R., Biek, R., Cleaveland, S., Cross, P. C., Lloyd-Smith, J. O., & Haydon, D. T. (2014). Assembling evidence for identifying reservoirs of infection. Trends in Ecology & Evolution, 29(5), 270–279. doi:10.1016/j.tree.2014.03.002

Webb, C. T., Brooks, C. P., Gage, K. L., & Antolin, M. F. (2006). Classic flea-borne transmission does not drive plague epizootics in prairie dogs. Proceedings of the National Academy of Sciences of the United States of America, 103(16), 6236–41. doi:10.1073/pnas.0510090103


Sherlock Holmes and the deductive paradigm of forensic epidemiology.

No blogs for ages, and then two in one week …

Deductive logic at its finest

Earlier this year, Dom Mellor was giving a talk to the epidemiology group at Glasgow, where he started by saying that, in his view, Sherlock Holmes represented the perfect example of forensic epidemiology. In a sense he was right, and at least some of you will know that it is commonly believed that the Holmesian forensic technique was based on Conan Doyle’s experiences as an Edinburgh medical student, where the medical doctor and University Professor Joseph Bell impressed the young student in his lectures. It was said that “all Edinburgh medical students remember Joseph Bell – Joe Bell – as they called him. Always alert, always up and doing, nothing ever escaped that keen eye of his. He read both patients and students like so many open books. His diagnosis was almost never at fault.” Sherlock Holmes most famous quote, taken from the Sign of the Four: “When you have eliminated the impossible, whatever remains, however improbable, must be the truth” is the iconic expression of deductive logic, and it could be said to be the ultimate goal of forensic epidemiology. I recall a colleague saying to me “Identify and eliminate the source of Infection, and you eliminate the epidemic”, apparently quoting from the highly respected veterinary epidemiologist, Prof. Mike Thrusfield at the Dick Vet School in Edinburgh, though I cannot comment on the accuracy of the quote. Of course, it is also well recognised that it would usually be impossible to be so sure as Sherlock Holmes in real life, but this nevertheless represents a sort of platonic ideal of forensics.

“Balance of probabilities, little brother” Mycroft Holmes, Hearse, Sign and Vow (from

Move forward a century and more, and the hugely popular TV series ‘Sherlock’ presents a modern updating of the old stories, an updating which, to my great surprise, I have thoroughly enjoyed. In the third series, in the episode ‘Hearse, Sign and Vow’, Sherlock and his older, more intelligent brother Mycroft are engaged in a contest to characterise a man from only his woolly hat. In this contest Sherlock queries one of Mycroft’s “deductions”, when Mycroft replies “Balance of probabilities, little brother.” Now this statement is decidedly un-Holmesian – in the world of Arthur Conan Doyle’s Sherlock Holmes, probabilities have nothing to do with it. This statement is in fact, one of inductive logic. And it could be argued that the mathematical and statistical modelling of infectious diseases lies very much more in this inductive tradition. Not so much concerned with identifying the single chain of transmission, modelling traditionally concentrates on the identification of general, population level principles of transmission, and an overall ‘balance of probabilities’ of getting the right pattern.

These two traditions – that of the forensic epidemiologist and the mathematical/statistical epidemiologist do not sit easily together, and indeed it could be argued that much of the controversy over the 2001 Foot-and-mouth disease (FMD) epidemic in Great Britain can be attributed to precisely that clash of cultures.

Phylodynamic reconstruction of a foot-and-mouth disease (FMD) epidemic. (A) Identified likelihood that a particular infected premises was the source of another infected premises based on a space–time–genetic model. Circle size is proportional to the relative likelihood of that event. (B) Spatial relationships among premises in the dataset. Reproduced from Morelli et al. PLoS Pathogens 2012.

Phylodynamic reconstruction of a cluster of cases from the 2001 FMD epidemic in Great Britain. (A) Identified likelihood that a particular infected premises was the source of another infected premises based on a space–time–genetic model. Circle size is proportional to the relative likelihood of that event. (B) Spatial relationships among premises in the dataset. Adapted from Morelli et al. PLoS Pathogens 2012.

Now however, the integration of rapid high throughout sequencing of pathogens allows us to trace to a very fine scale the movement of pathogens from place-to-place, and even from individual-to-individual. Combined with mathematical models, this can often lead to very precise identification of likely sources of infection. The figure here is taken from a paper by Marco Morelli while he was working with Dan Haydon at Glasgow, illustrating precisely that kind of analysis using data from the 2001 FMD epidemic. Of course the most likely source under one model of transmission is not necessarily proof that the relationship is the true one (e.g. what if another model gives an equally strong but different prediction?) and there are many challenges still to be addressed. Despite these issues, the future is bright and it is just possible that, through these new technologies and approaches, we can at last approach that Holmesian ideal.