Emerging Concepts of Data Integration in Pathogen Phylodynamics.
Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.
Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium.
Guy
G
Baele
Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.|Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.|Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA, USA.
Marc A
MA
Suchard
Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, Edinburgh, UK.|Centre for Immunity, Infection and Evolution, University of Edinburgh, Kings Buildings, Edinburgh, UK.
Andrew
A
Rambaut
Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium.
Philippe
P
Lemey
D005075:Biological Evolution; D004813:Epidemiology; D054884:Host-Pathogen Interactions; D008954:Models, Biological; D010802:Phylogeny; D014777:Virus Diseases / Q000453:epidemiology / Q000635:transmission / Q000821:virology; D014780:Viruses / Q000145:classification / Q000235:genetics / Q000472:pathogenicity