Spatial effects in parasite induced marine diseases of immobile hosts

Marine infectious diseases are more prevalent in recent times due to climate
change and other anthropogenic pressures, posing a substantial threat to
marine ecosystems and the conservation of their biodiversity. An important
subset of marine organisms are sessile, for which the most common mechanism
for disease transmission is direct contact with waterborne parasites. Only
recently, some deterministic compartmental models have been proposed to
describe this kind of epidemics, being these models based on non-spatial
descriptions where space is homogenised and parasite mobility is not explicitly
accounted for. However, in realistic situations, epidemic transmission is
conditioned by the spatial distribution of hosts and the parasites mobility
patterns. Thus, the interplay between these factors is expected to have a
crucial effect in the evolution of the epidemic, so calling for a explicit
description of space. In this work we develop a spatially-explicit individual-
based model to study disease transmission by waterborne parasites in sessile
marine populations. We investigate the impact of spatial disease transmission,
performing extensive numerical simulations and analytical approximations.
Specifically, the effects of parasite mobility into the epidemic threshold and the
temporal evolution of the epidemic are assessed. We show that larger values
of pathogen mobility have two main implications: more severe epidemics,
as the number of infections increases, and shorter time-scales to extinction.
Moreover, an analytical expression for the basic reproduction number of the
spatial model, R̃ 0 , is derived as function of the non-spatial counterpart, R 0 ,
which characterises a transition between a disease-free and a propagation
phase, in which the disease propagates over a large fraction of the system. This
allows to determine a phase diagram for the epidemic model as function of the
parasite mobility and the basic reproduction number of the non-spatial model.

Also available from BioRxiv at https://doi.org/10.1101/2021.12.15.472766