Modeling Dengue Hotspot with Bipartite Network Approach

Abstract

Dengue poses a large economic burden in Malaysia includes among other endemic countries. In order to detect the likely hotspots that breeds mosquito vectors, this study aims to formulate a contact network model of dengue transmission where the research scenario is characterised by spatial data that is complex and difficult to be modelled. The bipartite network modeling approach can address the homogenous limitation seen in deterministic models by projecting the research scenario into two sets of node: human hosts and locations visited by the human. The data of human movements are collected and aggregated from Sarawak State Health Department while the environmental data are obtained from Kuching Meteorological Department. All data are pre-processed and formulated into a targeted model which consists of eight human nodes and nineteen location nodes and a test model which consists of three human nodes and eight new incoming location nodes. The link weight between two sets of node is quantified using summation rule which combines the environmental predictors for instance temperature, precipitation, humidity, human and vector characteristics. The location nodes in targeted and validated models are ranked using a web-based search algorithm according to the respective ranking values. As a result, the ranking values between the targeted and validated model shows strong ranking similarity with good Spearman rank correlation coefficient (ρ > 0.80; p < 0.001). The ranked locations can help public health authorities to prioritize the locations for vector control to remove the hotspot which results in the reduction of the spread of dengue disease.