Application of Mathematical Models to Evaluate Serological Monitoring in Breeders and Commercial Laying Hens

Mathematical models based on regression equations are an important tool for the interpretation of data generated by the serological monitoring of poultry flocks. The present study aimed to evaluate the serological monitoring of breeders and commercial laying hens by several poultry companies, and to develop mathematical models for seven different poultry diseases. Data from serological tests for seven diseases (chicken infectious anemia, infectious bronchitis, infectious bursal disease, Newcastle disease, avian encephalomyelitis, avian metapneumovirus, and avian reovirus) were selected for analysis in this study. The variables “age of birds at the time of blood collection” was considered as the independent variable (x), while “antibody titer” was set as the dependent variable (y). Analysis of variance and coefficient of multiple determination (R²) were used to select the model with the highest capacity to adequately describe the analyzed data. Data from serological monitoring generated 166 linear and non-linear regression equations, but only 1.2% of them yielded an R²≥0.8 and more than 25 serum collections. The low number of suitable models may be related to the lack of standardization of the sample collection. In summary, serological monitoring of breeders and commercial laying hen flocks can be performed using mathematical models, but the lack of standardization of sample collections may have led to limited results.

Keywords:
Amathematical models; monitoring; regression equations; serological tests