Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

Accounting for Detection Heterogeneity and Host Movements in a House Finch-Mycoplasma gallisepticum Disease System

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.During the course of my dissertation research, I made use of capture-recapture methods to investigate local house finch (Carpodacus mexicanus) demography and movements in the context of understanding seasonal Mycoplasma gallisepticum (MG) infection dynamics. Capture-recapture design, estimation, and modeling explicitly accounts for variable detectability of individuals and provides a framework for making multi-model inference, thereby incorporating inherent model selection uncertainty (via Akaike information criterion) into the inferential process. The biological focus throughout my research has generally been centered on the relationship between local spatial scale host population structure, movements, and host-pathogen dynamics. Broadly, my work illustrates the importance of accounting for animal detection probabilities when estimating epidemiological statistics and parameters. I also highlight the importance of considering different forms of animal movements (either biologically induced or as a consequence of sampling design) with respect to understanding dynamics in the finch-MG system (specifically), but also applicable to other host-pathogen systems (generally). I estimate host transient movements, completely observable within-study area movements, proportional recruitment, and temporary movements from the study area (representing partially observable movements); all of which are very important elements to consider for understanding the dynamics of highly mobile animal populations (especially in the presence of a virulent pathogen). My research, conducted at a local spatial scale in Ithaca, NY complements analyses using House Finch Disease Survey data (Dhondt et al. 1998) at a broader spatial scale, and provides a point of entry for understanding the critical linkage of scale dependent processes influencing finch-MG dynamics. Throughout this dissertation, I have sought to characterize the structure of this local finch population, and establish how both host population structure and movements lead to a better overall understanding of MG infection dynamics. As such, the complete body of work produced here represents the most comprehensive investigation of wildlife disease dynamics to date, which has incorporated and accounted for sampling and biologically driven heterogeneity in host encounter probabilities. Beyond the proximate benefits that this research contributes to understanding of the finch-MG system, my hope is that this work will in part serve as a precedent for future empirical investigations of wildlife-pathogen dynamics.Cornell Department of Natural Resources, Cornell Laboratory of Ornithology, and National Science Foundation (under Grant No. DEB-0094456

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases