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Estimating the Delay between Host Infection and Disease (Incubation Period) and Assessing Its Significance to the Epidemiology of Plant Diseases

Overview of attention for article published in PLOS ONE, January 2014
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Title
Estimating the Delay between Host Infection and Disease (Incubation Period) and Assessing Its Significance to the Epidemiology of Plant Diseases
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0086568
Pubmed ID
Authors

Melen Leclerc, Thierry Doré, Christopher A. Gilligan, Philippe Lucas, João A. N. Filipe

Abstract

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.

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Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Germany 1 <1%
Unknown 101 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 26%
Researcher 21 20%
Student > Master 14 13%
Student > Bachelor 5 5%
Student > Doctoral Student 4 4%
Other 15 14%
Unknown 18 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 47%
Mathematics 9 9%
Environmental Science 6 6%
Biochemistry, Genetics and Molecular Biology 4 4%
Computer Science 4 4%
Other 13 13%
Unknown 19 18%