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 | Acesso ao texto completo restrito à biblioteca da Embrapa Mandioca e Fruticultura. Para informações adicionais entre em contato com cnpmf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
15/12/2010 |
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Data da última atualização: |
19/01/2011 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
DESIMONE, E. R.; LARANJEIRA, F. F.; NERI, F. M.; CUNNIFFE, N. J.; GILLIGAN, C. A. |
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Afiliação: |
Erik R. DeSimone, UCLES; FRANCISCO FERRAZ LARANJEIRA BARBOSA, CNPMF; Franco Maria Neri, UCLES; Nik J. Cunniffe, UCLES; Chris A. Gilligan, UCLES. |
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Título: |
A flexible stochastic model to test hypotheses concerning vector-transmissible citrus diseases. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: CONFERENCE INTERNATIONAL ORGANIZATION CITRUS VIROLOGISTS, 18., Campinas, SP, 2010. Proceedings... Campinas: IOCV, 2010. 1 CD-ROM. |
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Idioma: |
Inglês |
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Notas: |
035 PSO.
Publicado também em: Citrus Research & Technology, Cordeirópolis, v. 31, Suplemento, 2010 |
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Conteúdo: |
Taken as a group, vector transmissible citrus diseases are biologically diverse, with different pathogens (xylem limited - Xylella fastidiosa; phloem limited Candidatus Liberibacter sp.; localized virus - Citrus Leprosis Virus; unknown - Bahia Bark Scaling and Citrus (Blight) and different groups of vectors. As a consequence, a number of key epidemiological quantities differ between diseases, including transmission rates, incubation periods and latency periods, both in the vector and in the plant. This makes epidemiological and/or management experiments timeconsuming and difficult to carry out, as prior knowledge is lacking at the design stage, and it is impossible to reason by analogy with other ostensibly similar pathogens. Mathematical models provide a useful support to integrate knowledge about the epidemiology of a particular pathosystem, to identify gaps and to use that knowledge to design more informative experiments. Models can also be used to help screen potential control strategies, allowing only the most promising to incur the costs associated with field testing. The dominant theoretical paradigm in mathematical epidemiology is the compartmental model (e.g. SIR model with Susceptible, Infected and Removed categories) in which differential equations link the numbers of hosts in each of these classes. A SEIDR model is an extension of the SIR model in which the Infected class is subdivided into Exposed (latent), Infectious (cryptically infectious), Detectable (symptomatically infectious), and Removed classes, allowing a cryptic infectious period to be represented. Here we present a flexible suite of spatially-explicit stochastic SEIDR models to simulate the progress and spread of plant diseases in lattice crops. The model considers a rectangular landscape of variable size comprising a target grove and four neighbouring groves. The landscape is a lattice in which is possible to set the number of planting rows and the number of plants in each row for both target and neighbours. Moreover, spacing across and within rows is allowed to vary in order to capture more realistic situations. The intensity of initial inoculum (proportion of infected plants) is also changeable and can be located in the target groves or in any of the neighbours. By doing so, the model incorporates both events originating from the neighbours (i,e, primary infection), and those which originate from within the target grove (i.e. secondary infection). Furthermore, by controlling which neighbours export inoculum, the inherent directionality of an invading pathogen can be represented. Infection eventsi are explicitly dependent on the distance between infectious and susceptible plants; for each pair of plants the probability of infection in a given time interval is a function of a distance based dispersal kernel (kind and scale is chosen by user) and infection rate (also adjustable). The transition rates between compartments can also be selected by choosing proper latency, incubation and infectious periods. The concept was proved by simulating epidemics of Bahia Bark Scaling of Citrus and Huanglongbing, and comparing the results of simulation with those from real experiments. For BBS, the parameters were estimated using the only published data set on this disease, and the model was used to attempt to replicate this experiment. Results show good agreement between real and simulated data in terms of both the disease progress curve and the spatial pattern of affected plants . For HLB, only the real and simulated spatial patterns were compared, as a proper progress dataset without eradication could not be found in Brazil. HLB epidemics were simulated considering a target grove of ~9.4ha (40 rows with 96 plants each, 7.0m x 3.5m spacing). This orchard was surrounded by four neighbours, each of at least 2ha and with 5% of plants infected. The infection rate was chosen to replicate the Fundecitrus estimated HLB curves in Brazil; average latency and incubation periods were set to 1 and 6 months respectively. The resulting maps were analyzed using Quadpy software. The parameter b of the binary power law for both 2x2 and 4x4 quadrat sizes was not significantly different for simulated and published data. While the log (A) parameter was different, this can be attributed to the initial conditions of the datasets. The excellent match between real and simulated data proves the utility of our underlying approach. MenosTaken as a group, vector transmissible citrus diseases are biologically diverse, with different pathogens (xylem limited - Xylella fastidiosa; phloem limited Candidatus Liberibacter sp.; localized virus - Citrus Leprosis Virus; unknown - Bahia Bark Scaling and Citrus (Blight) and different groups of vectors. As a consequence, a number of key epidemiological quantities differ between diseases, including transmission rates, incubation periods and latency periods, both in the vector and in the plant. This makes epidemiological and/or management experiments timeconsuming and difficult to carry out, as prior knowledge is lacking at the design stage, and it is impossible to reason by analogy with other ostensibly similar pathogens. Mathematical models provide a useful support to integrate knowledge about the epidemiology of a particular pathosystem, to identify gaps and to use that knowledge to design more informative experiments. Models can also be used to help screen potential control strategies, allowing only the most promising to incur the costs associated with field testing. The dominant theoretical paradigm in mathematical epidemiology is the compartmental model (e.g. SIR model with Susceptible, Infected and Removed categories) in which differential equations link the numbers of hosts in each of these classes. A SEIDR model is an extension of the SIR model in which the Infected class is subdivided into Exposed (latent), Infectious (cryptically infectious), Detectable (sympto... Mostrar Tudo |
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Palavras-Chave: |
Bahia Bark Scaling; Plant disease; Vector. |
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Thesagro: |
Doença de Planta; Fruta Cítrica; Xylella Fastidiosa. |
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Thesaurus Nal: |
Candidatus Liberibacter. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 05360nam a2200253 a 4500
001 1869886
005 2011-01-19
008 2010 bl uuuu u00u1 u #d
100 1 $aDESIMONE, E. R.
245 $aA flexible stochastic model to test hypotheses concerning vector-transmissible citrus diseases.
260 $aIn: CONFERENCE INTERNATIONAL ORGANIZATION CITRUS VIROLOGISTS, 18., Campinas, SP, 2010. Proceedings... Campinas: IOCV, 2010. 1 CD-ROM.$c2010
500 $a035 PSO. Publicado também em: Citrus Research & Technology, Cordeirópolis, v. 31, Suplemento, 2010
520 $aTaken as a group, vector transmissible citrus diseases are biologically diverse, with different pathogens (xylem limited - Xylella fastidiosa; phloem limited Candidatus Liberibacter sp.; localized virus - Citrus Leprosis Virus; unknown - Bahia Bark Scaling and Citrus (Blight) and different groups of vectors. As a consequence, a number of key epidemiological quantities differ between diseases, including transmission rates, incubation periods and latency periods, both in the vector and in the plant. This makes epidemiological and/or management experiments timeconsuming and difficult to carry out, as prior knowledge is lacking at the design stage, and it is impossible to reason by analogy with other ostensibly similar pathogens. Mathematical models provide a useful support to integrate knowledge about the epidemiology of a particular pathosystem, to identify gaps and to use that knowledge to design more informative experiments. Models can also be used to help screen potential control strategies, allowing only the most promising to incur the costs associated with field testing. The dominant theoretical paradigm in mathematical epidemiology is the compartmental model (e.g. SIR model with Susceptible, Infected and Removed categories) in which differential equations link the numbers of hosts in each of these classes. A SEIDR model is an extension of the SIR model in which the Infected class is subdivided into Exposed (latent), Infectious (cryptically infectious), Detectable (symptomatically infectious), and Removed classes, allowing a cryptic infectious period to be represented. Here we present a flexible suite of spatially-explicit stochastic SEIDR models to simulate the progress and spread of plant diseases in lattice crops. The model considers a rectangular landscape of variable size comprising a target grove and four neighbouring groves. The landscape is a lattice in which is possible to set the number of planting rows and the number of plants in each row for both target and neighbours. Moreover, spacing across and within rows is allowed to vary in order to capture more realistic situations. The intensity of initial inoculum (proportion of infected plants) is also changeable and can be located in the target groves or in any of the neighbours. By doing so, the model incorporates both events originating from the neighbours (i,e, primary infection), and those which originate from within the target grove (i.e. secondary infection). Furthermore, by controlling which neighbours export inoculum, the inherent directionality of an invading pathogen can be represented. Infection eventsi are explicitly dependent on the distance between infectious and susceptible plants; for each pair of plants the probability of infection in a given time interval is a function of a distance based dispersal kernel (kind and scale is chosen by user) and infection rate (also adjustable). The transition rates between compartments can also be selected by choosing proper latency, incubation and infectious periods. The concept was proved by simulating epidemics of Bahia Bark Scaling of Citrus and Huanglongbing, and comparing the results of simulation with those from real experiments. For BBS, the parameters were estimated using the only published data set on this disease, and the model was used to attempt to replicate this experiment. Results show good agreement between real and simulated data in terms of both the disease progress curve and the spatial pattern of affected plants . For HLB, only the real and simulated spatial patterns were compared, as a proper progress dataset without eradication could not be found in Brazil. HLB epidemics were simulated considering a target grove of ~9.4ha (40 rows with 96 plants each, 7.0m x 3.5m spacing). This orchard was surrounded by four neighbours, each of at least 2ha and with 5% of plants infected. The infection rate was chosen to replicate the Fundecitrus estimated HLB curves in Brazil; average latency and incubation periods were set to 1 and 6 months respectively. The resulting maps were analyzed using Quadpy software. The parameter b of the binary power law for both 2x2 and 4x4 quadrat sizes was not significantly different for simulated and published data. While the log (A) parameter was different, this can be attributed to the initial conditions of the datasets. The excellent match between real and simulated data proves the utility of our underlying approach.
650 $aCandidatus Liberibacter
650 $aDoença de Planta
650 $aFruta Cítrica
650 $aXylella Fastidiosa
653 $aBahia Bark Scaling
653 $aPlant disease
653 $aVector
700 1 $aLARANJEIRA, F. F.
700 1 $aNERI, F. M.
700 1 $aCUNNIFFE, N. J.
700 1 $aGILLIGAN, C. A.
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