Date : 08/03/2011

Laboratory
UMMISCO
UMI 209 IRD
32, avenue Henry Varagnat
93143 Bondy
Website : http://www.ummisco.ird.fr
Main discipline : Epidemiology
Lab director : Pierre Auger

PhD Supervisor
Dr Benjamin Roche
email : This e-mail address is being protected from spam bots, you need JavaScript enabled to view it
phone : +33.6.29.58.54.60

Subjects
1.: Epidemiological and Evolutionary dynamics
2.: Ecological and Evolutionary traps

Tools and methodologies
1.: Stochastic models
2.: Individual Based Models

Summary of lab's interests

The PhD project will be done in collaboration between two labs "Mathematical and Computational Modeling of Complex Systems" and "Ecologie & Evolution", and between these labs with Dr Benjamin Roche and Pr Bernard Cazelles. The first laboratory focuses on innovative mathematical and computational modelling of complex systems, with a strong interest for epidemiological models.The second laboratory's research interests are in evolutionary ecology, with a strong focus on feedbacks between ecological and evolutionary processes. Main research axes are eco-evolutionary modeling, epidemiology, evolutionary microbiology and immunology, evolution of sociality, integrative ecology.

Summary of project

It is increasingly recognized that the majority of infectious diseases are a full-fledged component of their ecological ecosystem. It is well known that the animal diversity plays an important role in disease emergence and propagation. This host diversity can amplify the disease transmission, as documented for some directly-transmitted diseases like rodent-borne viruses, or dilutes the pathogen dispersal, as demonstrated for many pathogens involved a vector transmission. It has been also documented that pathogens interacts between them, altering reciprocally their epidemiological and evolutionary dynamics. This competition can take place within the host through its immune system, like for many infections with HIV or in the case of helimnths and plasmodium falciparum that involve both an opposite immune reaction. These interactions can arise also between the hosts, like through a convalescence period where infected individual are unavailable for other infectious diseases, as it has been demonstrated between measles and whooping-cough. These different examples underline that epidemiological and evolutionary dynamics of pathogens are driven by the ecosystem where they are introduced. Nevertheless, the current paradigm for pathogen control strategies is mainly to focus directly of the pathogen of interest instead of targeting its ecosystem. This mandatory medical approach fail to consider a priori how these strategies will impact dynamics of other infectious diseases or how it will be impacted by the ecosystem surrounding the targeted pathogen. This omission can explain the lack of prediction of the numerous unexpected effects that have been observed, like antibiotics resistance, disease emergence consecutive to biodiversity loss, etc... The main objective of this PhD will be to explore how to use the pathogen ecosystem to provide an optimal control strategy. An interesting concept suggested several years ago in evolutionary biology, and never be explore for infectious diseases so far, is the concept of "evolutionary trap". This generalization of the "ecological trap", widely used in conservation biology to constrain the dispersal of endangered animal species within manageable areas, relies on the cues captured by an organism about the habitat choice, forcing the animal species to make the wrong one. One can suppose that an analogy could be envisioned for pathogenic organism. The presence of an animal reservoir or another pathogen, or the both, could create an ecosystem where the pathogen targeted is maladaptated, yielding a decrease of pathogen transmission. The goal of this PhD will be to explore the conditions required to observe the emergence of an evolutionary trap for infectious diseases that can help to mitigate disease transmission. The first step will be the development of an innovative theoretical model integrating: (i) large host species assemblages and (ii) numerous other pathogens interacting between themselves in different ways. This first half of the PhD will help assessing the suitability of this approach for different kind of pathogens. In a second time, according to the theoretical results, several pathogens will be targeted. The rapid evolutionary dynamics of influenza viruses is a good reason to consider this infection as a good potential candidate, moreover regarding the huge amount of epidemiological and genetic data that are freely available. The wide host range of Lyme disease, with the good documentation about the competence of each of its animal reservoir, can be helpful to test this approach with the animal diversity. Many empirical studies have published epidemiological data that can be used to test our hypotheses. Finally, the good biological background on plasmodium falciparum, including the trophic preference of its vector species and the level of its interactions with many other pathogens, makes this infection a good candidate. The existence of freely available database, like within the MAP project, can be helpful to consider realistic epidemiological situations. Collaborations are also starting to work on the data of the zoonotic form of these diseases, i.e. avian malaria, avian influenza and avian Lyme. The choice of the diseases studied will be driven by the previous theoretical results.

Interdisciplinarity of the project

This project is at the perfect interface of ecology, evolution and public health. It will require collaborations with medical doctors, ecologist, evolutionary biologist and modellers. We believe that this trans-disciplinary collaboration will be highly productive and dynamic. The main expected scientific result is to give precious insights into new control strategies of infectious diseases that can help anticipating and mitigating the explosive effects of these new plagues, especially in developing countries who cannot afford adequate early-response health systems.