Pathogen Growth in Insect Hosts: Inferring the Importance of Different Mechanisms Using Stochastic Models and Response-Time Data

Abstract

Pathogen population dynamics within individual hosts can alter disease epidemics and pathogen evolution, but our understanding of the mechanisms driving within-host dynamics is weak. Mathematical models have provided useful insights, but existing models have only rarely been subjected to rigorous tests, and their reliability is therefore open to question. Most models assume that initial pathogen population sizes are so large that stochastic effects due to small population sizes, so-called demographic stochasticity, are negligible, but whether this assumption is reasonable is unknown. Most models also assume that the dynamic effects of a host's immune system strongly affect pathogen incubation times or "response times," but whether such effects are important in real host-pathogen interactions is likewise unknown. Here we use data for a baculovirus of the gypsy moth to test models of within-host pathogen growth. By using Bayesian statistical techniques and formal model-selection procedures, we are able to show that the response time of the gypsy moth virus is strongly affected by both demographic stochasticity and a dynamic response of the host immune system. Our results imply that not all response-time variability can be explained by host and pathogen variability, and that immune system responses to infection may have important effects on population-level disease dynamics.

Modeling Marek's disease virus transmission: A framework for evaluating the impact of farming practices and evolution

“…To account for the fact that bird population sizes are finite, and for computational convenience, the above differential equations were effectively replaced with their corresponding probabilistic transition equations (Kennedy et al, 2014, 2015a), where time was discretized to units of one day. Bird numbers were discretized to integer values, and transitions between classes were determined by binomial probabilities.…”

Effects of pathogen exposure on life‐history variation in the gypsy moth (Lymantria dispar)

“…We therefore used fourth instars in our experiments. Following virus infection under laboratory conditions, larvae die within 7 to 25 days (Kennedy et al, 2014). At death, the insect’s integument is digested by viral proteases and new occlusion bodies are released into the environment (Washburn et al, 1996).…”

Effects of multiple sources of genetic drift on pathogen variation within hosts

“…New infections are typically initiated by small pathogen population sizes within hosts (Gutiérrez et al 2012), leading to bottlenecks at the time of transmission that may drive genetic drift. Pathogen population sizes within hosts can also remain small for substantial periods following exposure (Kennedy et al 2014). In small populations, chance events such as the timing of reproduction can strongly influence population growth (Kennedy et al 2014), an effect known as "demographic stochasticity" (Kot 2001).…”

“…Pathogen population sizes within hosts can also remain small for substantial periods following exposure (Kennedy et al 2014). In small populations, chance events such as the timing of reproduction can strongly influence population growth (Kennedy et al 2014), an effect known as "demographic stochasticity" (Kot 2001). When the effects of demographic stochasticity are strong, chance may allow some virus strains to replicate and survive while others go extinct, providing a second source of genetic drift that we refer to as "replicative drift".…”

“…For baculovirus diseases of insects, pathogen population processes have been intensively studied at both the host population level (Elderd 2013), and at the individual host level (Kennedy et al 2014). Baculoviruses cause severe epizootics (= epidemics in animals) in many insects (Moreau and Lucarotti 2007), including economically important pest species such as the gypsy moth (Lymantria dispar) that we study here (Woods and Elkinton 1987).…”