Pharmaceutical Interventions in Epidemiological Studies: A Data-Driven Approach Through Health Informatics

Abstract

The control of diseases and their impact requires pharmaceutical intervention at the population level. Epidemiological research requires understanding the pattern of disease, identification of risk factors, and assessment of the efficacy of pharmaceutical interventions constituted by vaccines and treatments. However, there are deficiencies in the classical epidemiological approach due to inconsistent data, and incompleteness, and none is in real-time. This article points out where health informatics and data analytics could be overtly applied to augment pharmaceutical interventions within epidemiological investigations. We discuss the integration of heterogeneous data sources, deep machine learning models, and real-time monitoring to enhance drug safety, forecast the course of a disease, and optimize the strategy of intervention. Further, we underline some challenges in data quality, privacy, interpretability, and bias when implementing data-driven approaches and propose some strategies to address these issues for more effective and personalized pharmaceutical interventions.