Mathematical Modeling of Viral Zoonoses in Wildlife

Allen, Linda J. S.; Brown, V. L.; Jonsson, Colleen B.; Klein, Sabra L.; Laverty, Sean M.; Magwedere, Kudakwashe; Owen, Jennifer C.; Driessche, P. van den

doi:10.1111/j.1939-7445.2011.00104.x

Cited by 36 publications

(44 citation statements)

References 165 publications

(239 reference statements)

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“…Identifying host features associated with supershedding may facilitate more targeted research or disease control efforts, and this can only be accomplished with a thorough understanding of the mechanisms responsible for infection heterogeneity. The life cycle of a virus provides the basis for investigations of supershedding and includes (I) the initial exposure of a host, (II) entry into sites of virus replication within a host through barriers and receptors, (III) virus replication, and (IV) exposure of another host to the virus [37]. Differences between individuals in the transition-rate from one step of the cycle to the next likely accounts for infection heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Birds Shed RNA-Viruses According to the Pareto Principle

et al. 2013

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A major challenge in disease ecology is to understand the role of individual variation of infection load on disease transmission dynamics and how this influences the evolution of resistance or tolerance mechanisms. Such information will improve our capacity to understand, predict, and mitigate pathogen-associated disease in all organisms. In many host-pathogen systems, particularly macroparasites and sexually transmitted diseases, it has been found that approximately 20% of the population is responsible for approximately 80% of the transmission events. Although host contact rates can account for some of this pattern, pathogen transmission dynamics also depend upon host infectiousness, an area that has received relatively little attention. Therefore, we conducted a meta-analysis of pathogen shedding rates of 24 host (avian) – pathogen (RNA-virus) studies, including 17 bird species and five important zoonotic viruses. We determined that viral count data followed the Weibull distribution, the mean Gini coefficient (an index of inequality) was 0.687 (0.036 SEM), and that 22.0% (0.90 SEM) of the birds shed 80% of the virus across all studies, suggesting an adherence of viral shedding counts to the Pareto Principle. The relative position of a bird in a distribution of viral counts was affected by factors extrinsic to the host, such as exposure to corticosterone and to a lesser extent reduced food availability, but not to intrinsic host factors including age, sex, and migratory status. These data provide a quantitative view of heterogeneous virus shedding in birds that may be used to better parameterize epidemiological models and understand transmission dynamics.

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Section: Discussionmentioning

confidence: 99%

Birds Shed RNA-Viruses According to the Pareto Principle

et al. 2013

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“…The explicit incorporation of the counts of susceptible and infected hosts in the hidden compartment is a way to objectively account for the time-varying risk of infection caused by infected hidden hosts. This approach is adopted in many temporal SIR-like models that make the distinction between different types of hosts, for example target hosts and alternate hosts, including vectors (Dobson, 2004;Allen et al, 2012). Such multi-host epidemic models are often based on a system of ordinary differential equations, but can also be based on Markov processes (McCormack & Allen, 2006;Allen, 2017), as in our case.…”

Section: New Phytologistmentioning

confidence: 99%

Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent

et al. 2018

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Summary Unravelling the ecological structure of emerging plant pathogens persisting in multi‐host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space and host species, and may lead to biases of perception. The biased perception of pathogen ecology may be exacerbated by hidden fractions of the whole host population, which may act as infection reservoirs.We designed a mechanistic‐statistical approach to help understand the ecology of emerging pathogens by filtering out some biases of perception. This approach, based on SIR (Susceptible–Infected–Removed) models and a Bayesian framework, disentangles epidemiological and observational processes underlying temporal counting data.We applied our approach to French surveillance data on Xylella fastidiosa, a multi‐host pathogenic bacterium recently discovered in Corsica, France. A model selection led to two diverging scenarios: one scenario without a hidden compartment and an introduction around 2001, and the other with a hidden compartment and an introduction around 1985.Thus, Xylella fastidiosa was probably introduced into Corsica much earlier than its discovery, and its control could be arduous under the hidden compartment scenario. From a methodological perspective, our approach provides insights into the dynamics of emerging plant pathogens and, in particular, the potential existence of infection reservoirs.

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“…Despite its central role in transmission and disease control, the environment is often poorly represented in infectious disease models used to understand transmission and evaluate control strategies. Models that include environmental reservoirs often represent the environment as a homogenous compartment, and the contact rates between the environment and hosts are assumed to be random78910.…”

mentioning

confidence: 99%

Temporal-spatial heterogeneity in animal-environment contact: Implications for the exposure and transmission of pathogens

Chen

Sanderson²,

White

et al. 2013

Sci Rep

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Contact structure, a critical driver of infectious disease transmission, is not completely understood and characterized for environmentally transmitted pathogens. In this study, we assessed the effects of temporal and spatial heterogeneity in animal contact structures on the dynamics of environmentally transmitted pathogens. We used real-time animal position data to describe contact between animals and specific environmental areas used for feeding and watering calves. The generated contact structure varied across days and among animals. We integrated animal and environmental heterogeneity into an agent-based simulation model for Escherichia coli O157 environmental transmission in cattle to simulate four different scenarios with different environmental bacteria concentrations at different areas. The simulation results suggest heterogeneity in environmental contact structure among cattle influences pathogen prevalence and exposure associated with each environment. Our findings suggest that interventions that target environmental areas, even relatively small areas, with high bacterial concentration can result in effective mitigation of environmentally transmitted pathogens.

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Mathematical Modeling of Viral Zoonoses in Wildlife

Cited by 36 publications

References 165 publications

Birds Shed RNA-Viruses According to the Pareto Principle

Birds Shed RNA-Viruses According to the Pareto Principle

Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent

Temporal-spatial heterogeneity in animal-environment contact: Implications for the exposure and transmission of pathogens

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