Choice of non-linear models to determine the growth curve of meat-type quail / Escolha de modelos não-linearespara determinar a curva de crescimento de codornas de corte

ABSTRACT: The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards' was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.

RESUMO: Objetivou-se, neste estudo, comparar modelos não lineares ajustados às curvas de crescimento de codornas para determinar qual modelo que melhor descreve o crescimento de codornas e verificar a similaridade dos modelos analisando as estimativas dos parâmetros. Para as análises foram utilizados os dados peso e idade de codornas européias de corte (Coturnix coturnix coturnix) proveniente de três linhagens, em um esquema fatorial 2x4, instalado em um delineamento inteiramente casualizado, com dois níveis de energia metabolizável e quatro níveis de proteína bruta, com seis repetições. Os modelos não lineares utilizados foram: Brody, Von Bertalanffy, Richards, Logístico e Gompertz. Para a escolha do melhor modelo utilizou-se o Coeficiente de Determinação Ajustado, o Percentual de Convergência, o Quadrado Médio do Resíduo, o Teste de Durbin-Watson, o Critério de informação Akaike e o Critério de informação Bayesiano como avaliadores da qualidade do ajuste. Utilizou-se a análise de agrupamento para verificar, baseado nas estimativas médias dos parâmetros, a similaridades entre os modelos. Entre os modelos estudados, o Richard foi o mais adequado para descrever as curvas de crescimento. Os modelos Logístico e Richards foram considerados similares nas análises sem distinção de linhagem, bem como nas análises das Linhagem 1, 2 e 3.

Fitting prediction of transmissibility and incidence trend of novel coronavirus pneumonia in Ningxia / 公共卫生与预防医学

Objective To analyze the change in transmissibility of novel coronavirus pneumonia and predict the trend of the incidence, and to provide a reference for the government to better respond to the novel coronavirus pneumonia epidemic. Methods The EpiEstimof R language software was used to estimate the change of effective basic reproduction number, and the Richards model was run by Matlab7.0 software to fit the cumulative number of confirmed cases and the number of suspected cases. The coefficient of determination and root mean squared error were used to evaluate the fitting effect of the model. Results A total of 75 confirmed cases and 107 suspected cases were reported in Ningxia. The strict implementation of various prevention and control measures gradually reduced the effective basic reproduction number from 3.82 to less than 1, indicating that the epidemic was under control. The Richards model was used to fit the cumulative confirmed cases and suspected cases, which revealed that the natural growth rates were 0.16 and 0.23, and the coefficients of determination were 0.991 and 0.998, respectively. Conclusion Combined with the effective basic reproduction number, the Richards model fitted the trend of novel coronavirus pneumonia, which can be used to predict the trend of incidence of new coronavirus pneumonia.

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Abstract Mathematical models are often used to predict microbial growth in food products. An important class of these models involves the adaptation of classical sigmoid functions, such as the Gompertz and logistic functions. This study aimed to validate the use of the modified Richards model in various situations, which have not previously been tested. The model was obtained through solving a system of two differential equations and could be applied to both isothermal and non-isothermal environments. To test and validate this model, we used published datasets containing data for the growth of Pseudomonas spp. in fish products. The results obtained after fitting the model showed that it could be effectively used to describe and predict the Pseudomonas growth curves under various temperature regimens. However, the influence of the shape parameter on the growth curve is an issue that needs further evaluation.