Our research addresses the Ecology and Evolution of infectious diseases. 

Host-pathogen systems are paradigmatic and fascinating examples of complex adaptive systems. They combine the challenges of nonlinear dynamics, large number of interactions between diverse components, and changing conditions because of evolution or environmental changes. Questions on how to model these systems and at what scales, how to make inferences from large but incomplete data sets, how to predict and alter the course of their dynamics, are central at this time of increased contact between natural and built-in environments, increased human movement, and rapid environmental changes.

Our work relies on a variety of extensive data sets, from long time series of disease incidence that span decades, to large ecological networks, to molecular (sequence) data on pathogens. The disease work involves international collaborations with public health and research partners around the world. On the theoretical front, we use mathematical models together with computational and statistical approaches to bridge the gap between data and models.

Current research in the lab addresses the following areas:

The transmission dynamics of infectious diseases whose population dynamics are environmentally driven. We investigate for example the impact of climate variability and climate change on water-borne and vector-borne infections such as cholera and malaria. We combine mathematical models and computational statistical methods to analyze the temporal and spatial variability of infectious diseases and to assess their predictability, based on extensive but incomplete and noisy data.
Example papers: Martinez et al. PNAS 2016; Siraj et al. Science 2014; Laneri et al. PLoS Comp. Biol. 2010.

Rotavirus

The spatio-temporal dynamics of infectious diseases in large urban environments of the developing world, in South Asia and South America.  We are asking questions about the interaction of environmental and socio-economic drivers, including responses to climate forcing. We are also interested in the relevant spatial scales at which to study these dynamics and confront stochastic transmission models to surveillance data for the purposes of informing intervention.  We are collaborating on the development of models at the intersection of ecology, economics and health.
Example papers: Baeza-Castro et al. Nature E&E 2016; Santos-Vega et al. PloS NTD 2015Reiner et al., PNAS 2012.

Seasonal prediction of infectious diseases whose outbreaks recur every year but whose pathogens evolves to escape the immune system, such as seasonal influenza.  We are developing transmission models that are simple enough to be fitted to surveillance data and yet incorporate information on the evolution of the virus.

The interplay of ecological and evolutionary dynamics in generating and structuring the remarkable genetic and antigenic diversity of pathogen populations. In turn, the influence of diversity’s structure on population dynamics and therefore, on intervention efforts and resilience to elimination. We have previously contributed to develop an understanding of the ‘phylodynamics’ of influenza (at the intersection of phylogenetic patterns and transmission dynamics); we are now working on the ‘strain theory’ for Plasmodium falciparum malaria and rotavirus (the virus responsible for the majority of diarrheal cases and deaths in children worldwide), and analyzing molecular (sequence) data to address strain structure in the field. In these two systems, large genetic diversity is generated by recombining genetic material (technically the term is ‘reassorting’ in rotavirus), and therefore, by the combinatorics of mixing modular components. We can ask whether strain structure exists despite this mixing and how this structure modifies our basic understanding of transmission dynamics.  From a more general perspective, there are fascinating analogies between questions on the coexistence of a diverse ensemble of strains within pathogen populations, and those on the coexistence of a large number of species within ecological communities such as tropical rainforests or coral reefs.
Example papers: Koelle et al. Science 2006; Zinder et al., PloS Pathogens 2013; Bedford et al. BMC 2012; Artzy et al. eLife 2012.

The structure of large networks of ecological interactions between species in ecosystems. We have worked on computational approaches to explore patterns in the structure of food webs, as well as on models that can generate these structures and help us understand their dynamical consequences (‘stability’ to perturbations). At the moment, our research on networks falls mostly at the intersection with the study of pathogen diversity.  We are specifically using networks to describe and analyze the similarity structure of coexisting strains when phylogenetic trees are no longer applicable (e.g. under recombination).
Example papers: Pilosof et al. Nature E&E 2017; Golubski et al. TREE 2016; Baskerville et al. PloS CB 2011;  Allesina, Alonso and Pascual, Science 2008.

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