Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis

Abstract

Abstract Background Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Methods Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Results Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. Conclusions The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL’s versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.