INTEGRATING EMPIRICAL RESULTS INTO EPIDEMIOLOGICAL MODELS
When considering effects of stress on infected host fitness and
infectivity, responses varied depending on stressor type. Environmental
stress decreased host survivorship and increased infection intensity,
pollution decreased host survival and pathogen prevalence, and limiting
resources decreased host reproduction and pathogen intensity.
We integrated the best-supported relationships from our meta-analysis
into mathematical models to evaluate the net impact of these
simultaneous effects of stressors on host-pathogen interactions. We
built two dynamic Susceptible-Infected (SI) models. An SI-Resource model
following the framework of Civitello et al. (2018) where key
processes (i.e., host reproduction and pathogen transmission) could
depend on resource availability (Box 1). And an SI-Environmental
gradient model following the framework of Lafferty & Holt (2003) where
key processes (i.e., host survivorship and pathogen transmission) could
depend on an abiotic environmental factor (Box 2). Because our
meta-analysis suggested no proportional difference between uninfected
and infected hosts for survival or reproduction, we incorporated this
result by including a common parameter for the strength of these effects
on both groups (Box 1 and Box 2).
We used the models to determine equilibria of disease prevalence as a
function of resource availability and environmental stress gradients,
using the numerical integration function “lsoda” in the R packagedeSolve (Soetaert et al. 2010). We examine different
scenarios in which fecundity and infectivity, or background death and
infectivity, had different sensitivities to either resource (Box 1) or
environmental stress gradients (Box 2), respectively. We simulated
epidemiological dynamics of each model across a gradient of either
resource availability or environmental stress, then plotted equilibrium
infection prevalence and host density against such gradients for each
model (Fig. 5).