Analytical tools in diseases epidemiology and surveillance: A review of literature

Abstract

In the wake of emerging and re-emerging diseases, the role of analytical tools in disease epidemiology and surveillance has never been more critical. Tools such as Geographic Information Systems (GIS), machine learning, Internet of Things (IoT), big data analytics, statistical modelling has been reckoned for their transformative impact on modern epidemiology. These tools facilitate real-time monitoring, forecast outbreak risks, detect anomalies early, provide rapid diagnoses, and enable targeted interventions during outbreaks. When these strategies are combined through collaborative efforts, they can greatly enhance the efficiency and effectiveness of disease management. Nevertheless, challenges remain, especially in resource-constrained environments, including issues related to infrastructure, costs, data integration, training needs, and privacy and ethical considerations. Through a comprehensive review of existing scholarly works, this paper evaluates the effectiveness of these tools in identifying risk factors, informing public health policies, and predicting disease outbreaks. Ultimately, this review seeks to provide an understanding of the current landscape of disease surveillance and offer insights into how technological advancements will shape future epidemiological practices.