PSI-15 Understanding seasonal trends in anemia risk: A multi-scale analysis of fecal egg count, PCV, and FAMACHA scores.

Abstract Parasitic infections in small ruminants, especially gastrointestinal nematodes, pose major risks to animal health, economic growth and productivity. This study examines Fecal Egg Count (FEC), Packed Cell Volume (PCV), and FAMACHA scores at various time intervals to evaluate the incidence of anemia and its correlation with environmental factors like temperature, humidity, and precipitation. Conventional statistical techniques, predictive modeling, and machine learning approaches explored trends, correlations, and forecasting potential. The traditional analysis included descriptive statistics, where the mean FEC was 950 epg (±1120), indicating high variability in parasite loads, while mean PCV was 26.8% (±5.4%), with values as low as 14% in anemic goats. There was a high negative connection (-0.72) between PCV and FEC, and a positive correlation (0.67) between FAMACHA scores and FEC, proving that FEC is reliable source for detecting anemia. Seasonal tendencies were revealed by time series analysis, with warm and humid months exhibiting the highest FEC levels (over 2000 epg). ANOVA results (p < 0.001) showed significant differences in PCV and FEC across FAMACHA score categories, with goats scoring 4 or 5 having an average PCV of 19.6%, significantly lower than those scoring 1 (average PCV: 30.8%). Multiple regression models were developed to predict anemia risk. Linear regression models predicted PCV with an R² of 0.58, considering FEC and environmental factors. Logistic regression classified anemia severity with an 80.2% accuracy, distinguishing between low-risk (FAMACHA 1 & 2) and high-risk (FAMACHA 4 & 5) categories. Advanced machine learning models were implemented to classify FAMACHA scores based on physiological and environmental predictors. Random Forest models achieved 85.4% accuracy, outperforming other classifiers. SHAP analysis revealed that humidity (feature importance: 27%), temperature (22%), and FEC (18%) were the top predictors of anemia risk. A 52-week forecast for PCV, FEC, and FAMACHA scores using ARIMA and Prophet models predicted an increase in anemia risk during weeks 24–38, with expected PCV dropping by 3.5% on average in high-risk months. Finally, clustering analysis (K-means, Hierarchical Clustering) grouped goats into low-risk, moderate-risk, and high-risk clusters, while Kaplan-Meier survival analysis showed that goats with initial PCV below 22% had a 75% probability of developing severe anemia within 6 weeks. This study shows how climate can affect the likelihood of anemia and how decision support systems powered by machine learning can help with sustainable management of small ruminants. This work provides a data-driven paradigm for proactive health monitoring and parasite management in resource-limited situations using prediction models and clustering approaches.