Dissertation Defense: Pratha Sah
Candidate Name: Pratha Sah
Advisor: Shweta Bansal, Ph.D.
Title: Interactions Between Social Structure, Contact Network and Infectious Disease Spread in Wildlife Populations
Socially complex species that live in large groups are traditionally considered to have elevated risks of disease transmission. Beyond group size, it is being increasingly recognized that the dynamics of infection spread largely depends on the organization of contacts occurring in host populations. Consequently, network theory has emerged as a powerful tool as it provides a framework for incorporating host contact patterns into models of infectious disease spread. Comparing empirical networks has however proven to be challenging, and limited knowledge about the host-pathogen system has resulted in underutilization of network analytic approaches in wildlife epidemiology. In this dissertation, I address these challenges to answer three specific questions - (i) does social structure mitigate the risk of disease transmission?, (ii) can unique social network structures associated with different social systems predict their disease outcomes?, and (iii) can empirical networks be utilized to infer transmission pathways of infectious diseases? I answer these questions by utilizing an extensive empirical dataset of more than 40 species, and developing novel tools for network analysis. I first test the hypothesis that subdivisions in animal social networks alleviate the disease costs of group
living. My analysis of empirical and theoretical networks reveals that infectious disease spread is largely unaffected by the underlying modular subdivisions except when host populations are extremely subdivided. Next, I perform a meta-analysis of more than 600 animal social networks to investigate the disease implications of species’ social system. I find that only few features of social structure distinguish different social systems, and that the network organization in social species may not provide general protection against pathogens of various transmission potential. Finally, because the transmission pathway of wildlife infectious diseases is often unknown, I develop a tool that estimates the statistical power of empirical contact networks to predict infectious disease spread, and enables hypothesis testing between different network models. Together, the results in this dissertation offer new perspectives on the debate about the disease costs of social living, and form a framework of integrating conventional wildlife survey techniques into network modeling. The tools developed in this dissertation may also prove useful in formulating disease prevention and conservation strategies in wildlife populations.
Monday, October 16, 2017 at 2:00pm to 4:00pm
Healy Hall, 106
37th and O St., N.W., Washington