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Autor: search.filters.author.Alves, Kaique dos SantosData inicial: 2019Data final: 2019Assunto: search.filters.subject.Fitopatologia

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    Estimation of the time-varying apparent infection rate from plant disease progress curves: a particle filter approach
    (Universidade Federal de Viçosa, 2019-08-12) Alves, Kaique dos Santos; Del Ponte, Emerson Medeiros; http://lattes.cnpq.br/3166163630863998
    The parameters of the simplest (two-parameter) epidemiological models that best fit plant disease progress curve (DPC) data are biologically meaningful: one is the surrogate for initial inoculum (𝑦 0 ) and the other is the (constant) apparent infection rate (𝑟), both being useful for understanding, predicting and comparing epidemics. The assumption that 𝑟 is constant is not reasonable and fluctuations are expected due to systematic changes in factors affecting infection (e.g. weather, host susceptibility, etc.), thus leading to a time-varying 𝑟, or 𝑟 𝑘 , being 𝑘 = 1,2, . . . , 𝑁 and 𝑁the final epidemic time. A rearrangement in formulation of these models (e.g. logistic, monomolecular, etc.) can be used to obtain 𝑟 between two time points, given the disease (𝑦) data are available. We evaluated one of the several data assimilation techniques, the Particle Filter (PF), as an alternative method for estimating 𝑟 𝑘 . Synthetic DPC data for hypothetical polycyclic epidemics were simulated using the logistic differential equation for scenarios that combined five patterns of 𝑟 𝑘 (constant, increasing, decreasing, random or sinusoidal); five increasing time assessment interval (𝛥𝑡 = 1, 3, 5, 7 or 9 time units - t.u.); and two levels of noise (0.1 or 0.25) assigned to 𝑦 𝑘 . The analyses of 50 simulated 60-t.u. DPCs showed that the errors of PF-derived𝑟̂ 𝑘 were lower (RMSE < 0.05) for 𝛥𝑡 < 5 t.u. and less affected by the presence of noise in the measure compared with the logit-derived 𝑟 𝑘 . The ability to more accurately estimate 𝑟 𝑘 may be useful to increase knowledge of field epidemics and identify within-season drivers of 𝑟 𝑘 behaviour. Keywords: Data assimilation. Inverse problems. Sequential Monte Carlo.