Multi-event capture–recapture modeling of host–pathogen dynamics among European rabbit populations exposed to myxoma and Rabbit Hemorrhagic Disease Viruses: common and heterogeneous patterns

Santoro, Simone; Pacios, Isabel; Moreno, Sacramento; Bertó-Morán, Alejandro; Rouco, Carlos

doi:10.1186/1297-9716-45-39

Cited by 14 publications

(24 citation statements)

References 60 publications

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“…This illustrates for instance the case of morbilliviruses such as CDV (15), measles, or rinderpest (36), classical swine fever (11) or rabbit haemorrhagic disease (25). For such types of diseases, the final data sets should comprise far more individuals in the R state—a “final” state, than in the S or the I states.…”

Section: Discussionmentioning

confidence: 99%

“…If the duration of the study extends well beyond the duration of the epidemic [as in e.g., (15)], then this may leave enough time for the pool of S to rebuild, and this pattern may be attenuated. In contrast, this pattern of a lower bias and higher precision for parameter estimates linked to the R state may be particularly apparent when studies are based on just a few years of data collection during a disease outbreak [e.g., (11, 25)].…”

Section: Discussionmentioning

confidence: 99%

“…When infection states are assigned based on the outcome of one diagnostic test such as “seropositive” and “seronegative”, as for free-ranging rabbits exposed to rabbit haemorrhagic disease (25) or bisons exposed to brucellosis (3), we expect heterogeneity in the assignment of infection states to be minimized as compared to cases where more than one diagnostic method is employed [e.g., (11, 15)]. This is because the assignment of infection states in such cases depends on the performance of only one type of diagnostic test (e.g., ELISAs to measure antibody titres) rather than several, which should minimize bias introduced by the diagnostic approach itself.…”

Section: Discussionmentioning

confidence: 99%

“…We set the number of occasions, corresponding for instance to years of observation, equal to five to mimic the conditions of most eco-epidemiological CMR studies, which are typically based on a few years of data collection during a disease outbreak [e.g. (11, 25)]. For each level of uncertainty, we simulated 1000 data sets.…”

Section: Methodsmentioning

confidence: 99%

“…First, they account for “imperfect detection” of individuals, which can occur when some individuals in the population are not observed during a field visit. Second, they account for “infection state uncertainty”, also termed “partial observation”, which can occur when individuals in the population are observed but their infection state could not be determined (12, 15, 18, 23–25). Finally, they also account for “infection state misclassification”, which can occur when some individuals are observed as alive and their infection state could only be assigned with some uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

et al. 2018

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Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically “imperfect”. Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be “undetected” or “detected” at each time step. Detected animals can be assigned an infection state, such as “susceptible” (S), “infected” (I), or “recovered” (R), or an “unknown” (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

et al. 2018

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Modelling pest dynamics under uncertainty in pest detection: the case of the red palm weevil

et al. 2020

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A common complication in invasive pest management is that the infectious state of the host can be wrongly assessed, leading to biases in the estimation of the prevalence of the pest and on the efficacy of mitigation actions. We designed a multievent model that accommodates uncertainty on host state to investigate the dynamics of the infestation of Phoenix canariensis by the invasive Rhynchophorus ferrugineus. Since 2011, the council of Palma city (Mallorca Island, Spain) applied preventive, curative and destructive treatments to public Phoenix palms. A year later awareness campaigns focused on every palm owner in an attempt to control the plague. We estimated the probability of infestation and assessed the efficiency of mitigation measures, awareness campaigns and palm-dependent covariates. Our results show that infestation probabilities were higher for palms infested in the previous year than for healthy palms and it decreased substantially over time as a results of mitigation measures and awareness campaigns. Palms surrounded by treated palms had a lower probability of being infested than palms surrounded by untreated palms, i.e. private palms before awareness campaigns implementation. Our results highlight the key role of awareness campaigns and public participation for invasive species control.

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Optimization and accuracy of faecal pellet count estimates of population size: The case of European rabbits in extensive breeding nuclei

Rouco

Santoro

Delibes‐Mateos

et al. 2016

Ecological Indicators

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Multi-event capture–recapture modeling of host–pathogen dynamics among European rabbit populations exposed to myxoma and Rabbit Hemorrhagic Disease Viruses: common and heterogeneous patterns

Cited by 14 publications

References 60 publications

Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

Modelling pest dynamics under uncertainty in pest detection: the case of the red palm weevil

Optimization and accuracy of faecal pellet count estimates of population size: The case of European rabbits in extensive breeding nuclei

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