Modelling parasite-produced marine diseases: The case of the mass mortality event of Pinna nobilis

Giménez-Romero, Àlex; Grau, Amàlia; Hendriks, Iris E.; Matías, Manuel A.

doi:10.1016/j.ecolmodel.2021.109705

Cited by 8 publications

(38 citation statements)

References 38 publications

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“…Furthermore, we consider that dead hosts are not a source of parasites in the medium. See [20] for a detailed presentation of the non-spatial SIRP model, including these biological modelling considerations.…”

Section: The Sirp Spatial Modelmentioning

confidence: 99%

“…oysters), infectious diseases in bivalve populations have been deeply studied [14][15][16][17]. Recently, deterministic compartmental models have been used to describe parasite transmitted diseases in marine sessile bivalves [18][19][20], showing to be able to accurately predict disease transmission in some circumstances. The main limitation of these compartmental models is the assumption of a non-spatial description of the system under study.…”

Section: Introductionmentioning

confidence: 99%

“…A non-spatial description assumes well mixed populations, which implies that the mean distance among hosts is smaller than the typical mixing length of parasites. This assumption can be quite realistic in some situations, as it is in [20] where the hosts were kept in tanks with water renovation. However, a non-spatial model is not expected to yield a good description of spatially extended hosts in a natural setting.…”

Section: Introductionmentioning

confidence: 99%

“…The model is applied to the case of diffusing parasites and uniformly distributed hosts. The system under study is an extension of the compartmental model presented in [20] and, to our best knowledge, is the first stochastic spatially-explicit study for this type of epidemics. As a main result, we find that the occurrence of an outbreak will depend on the balance between the intrinsic characteristics of the pathosystem, well represented by the above described non-spatial basic reproductive number, R 0 , and features that characterise parasite mobility.…”

Section: Introductionmentioning

confidence: 99%

“…The paper is organised as follows: in Section 2, we introduce some biological considerations for bivalve epidemics, discussed in more detailed in Ref. [20], and build the spatially-explicit model. In Section 3, we present analytical results that are discussed and supported by numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

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Spatial effects in parasite induced marine diseases of immobile hosts

Giménez-Romero

Vázquez

López

et al. 2021

Preprint

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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, , is derived as function of the non-spatial counterpart, R0, 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.

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Section: The Sirp Spatial Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial effects in parasite induced marine diseases of immobile hosts

Giménez-Romero

Vázquez

López

et al. 2021

Preprint

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Fractional order modeling of parasite-produced marine diseases with memory effect

Alla Hamou,

Azroul,

L’kima

2024

Model. Earth Syst. Environ.

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Spatial effects in parasite-induced marine diseases of immobile hosts

et al. 2022

Self Cite

View full text Add to dashboard Cite

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenized and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites' mobility patterns, calling for an 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 through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter timescales to extinction. An analytical expression for the basic reproduction number of the spatial model, R ~ 0 , is derived as a function of the non-spatial counterpart, R 0 , which characterizes a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system.

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Modelling parasite-produced marine diseases: The case of the mass mortality event of Pinna nobilis

Cited by 8 publications

References 38 publications

Spatial effects in parasite induced marine diseases of immobile hosts

Spatial effects in parasite induced marine diseases of immobile hosts

Fractional order modeling of parasite-produced marine diseases with memory effect

Spatial effects in parasite-induced marine diseases of immobile hosts

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