A mathematical framework for predicting lifestyles of viral pathogens

TL;DR

This paper introduces a mathematical framework that models the evolutionary strategies and lifestyles of viral pathogens by analyzing key parameters like contact rate, infectiousness, and immunity, covering diverse virus types.

Contribution

It extends previous models by explicitly connecting intra- and inter-host dynamics into a unified framework for various viral pathogens.

Findings

Identifies local maxima of pathogen fitness landscapes.

Models include differential equations, agent-based simulations, and network analysis.

Provides insights into viral evolution and transmission strategies.

Abstract

Despite being similar in structure, functioning, and size viral pathogens enjoy very different mostly well-defined ways of life. They occupy their hosts for a few days (influenza), for a few weeks (measles), or even lifelong (HCV), which manifests in acute or chronic infections. The various transmission routes (airborne, via direct contact, etc.), degrees of infectiousness (referring to the load required for transmission), antigenic variation/immune escape and virulence define further pathogenic lifestyles. To survive pathogens must infect new hosts; the success determines their fitness. Infection happens with a certain likelihood during contact of hosts, where contact can also be mediated by vectors. Besides structural aspects of the host-contact network, three parameters/concepts appear to be key: the contact rate and the infectiousness during contact, which encode the mode of transmission, and third the immunity of susceptible hosts. From here, what can be concluded about the evolutionary strategies of viral pathogens? This is the biological question addressed in this paper. The answer extends earlier results (Lange & Ferguson 2009, PLoS Comput Biol 5 (10): e1000536) and makes explicit connection to another basic work on the evolution of pathogens (Grenfell et al. 2004, Science 303: 327-332). A mathematical framework is presented that models intra- and inter-host dynamics in a minimalistic but unified fashion covering a broad spectrum of viral pathogens, including those that cause flu-like infections, childhood diseases, and sexually transmitted infections. These pathogens turn out as local maxima of the fitness landscape. The models involve differential- and integral equations, agent-based simulation, networks, and probability.