Estimating the delay between host infection and disease (incubation period) and assessing its significance to the epidemiology of plant diseases.
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Leclerc, M., Doré, T., Gilligan, C., Lucas, P., & Filipe, J. (2014). Estimating the delay between host infection and disease (incubation period) and assessing its significance to the epidemiology of plant diseases.. PLoS One, 9 (1. e86568)https://doi.org/10.1371/journal.pone.0086568
Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss.
Algorithms, Epidemics, Host-Pathogen Interactions, Infectious Disease Incubation Period, Models, Theoretical, Plant Diseases
Is supplemented by: https://doi.org/10.1371/journal.pone.0086568.s001
ML thanks the Institut Technique français de la Betterave industrielle (ITB) for funding this project. CAG and JANF were funded by the UK’s Biotechnology and Biological Sciences Research Council (BBSRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
External DOI: https://doi.org/10.1371/journal.pone.0086568
This record's URL: https://www.repository.cam.ac.uk/handle/1810/261349
Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International
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