Predictive Modeling of Citrus Huanglongbing Temporal Dynamics in Meizhou, Guangdong

Objective This study aimed to analyze the epidemic trend of citrus Huanglongbing (HLB) in Meizhou production area, construct and screen temporal dynamic prediction models, identify key epidemic periods, and provide a theoretical basis and technical support for precise HLB management in this region.

Method Plant disease epidemiology methods were combined with real-time quantitative PCR (qPCR) to systematically investigate and monitor the occurrence and progression of HLB in Meizhou area. Disease incidence and severity index were statistically analyzed. Five mathematical models including Logistic, Linear, Cubic, Gompertz, and Exponential were employed to fit the field observation data, and the optimal temporal dynamic prediction model was selected to identify the epidemic phases.

Result During the monitoring period, the disease index rose from 12.5 to 68.5, indicating a rapid increase trend of HLB at the monitored site. The Logistic model (R² = 0.987) and the Cubic model (R² = 0.994) provided the best fit for the disease progression data. The inflection point of the Logistic model (73.8 d) coincided with the peak period of psyllid dispersal in the field, demonstrating clear biological relevance. Based on a significant negative correlation (r =-0.82) between qPCR-derived Ct values and the disease index, classification thresholds for disease severity were proposed: Level 1 (Ct=30.4-31.5), Level 3 (Ct=26.8-29.0), Level 5 (Ct=22.9-24.6), and Level 7 (Ct=20.0-21.6).

Conclusion The Logistic model and the Cubic model are suitable for predicting the temporal dynamics of HLB in Meizhou production area. The Logistic model is recommended as the core forecasting tool, supplemented by the Cubic model for short-term trend analysis. The disease severity classification standard established based on the correlation between qPCR detection and the disease index provides a quantitative basis for early disease diagnosis.