Utilizing Bayesian Networks to Augment Decision-Making for Dengue Outbreak Response in Public Health Systems

Dengue has evolved into a worldwide concern, affecting countries across the globe. It is an epidemic disease transmitted by mosquitoes and has experienced rapid expansion in tropical and subtropical regions across the globe. The prior alerts about dengue outbreaks can effectively mitigate the impact of disease and facilitate Public Health Systems (PHS). In this paper, we introduce a Bayesian network (BN) based model designed to assess the susceptibility of a specific region to dengue outbreaks. This model aids public health services in determining the necessity of emergency medical team deployments. The primary objective of the proposed research work is to enhance the capabilities of healthcare teams in diagnosing diseases more effectively. In particular, this paper assigns weight to each dengue symptom using the Analytic Hierarchy Process (AHP) method based on data collected in a rural area of West Bengal, India. Ultimately, the Bayesian Network model computes the susceptibility of dengue occurrence within a specific geographical region. The versatility of the proposed model allows for its application in diverse regions, consistently yielding superior predictions of dengue case outcomes. The suggested model not only provides advance warnings about dengue outbreaks but also proves valuable for the public health system, enabling proactive visits to the affected locations.