Rabies Hotspot Detection Using Bipartite Network Modelling Approach

• Main Authors: Jian, Daren Bing Chia, Woon, Chee Kok, Nur Asheila, Abdul Taib, Boon, Hao Hong, Khairani, Abd Majid, Jane, Labadin
• Format: Article
• Language: English
• Published: UNIMAS Publisher 2021
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/40714/1/Rabies%20Hotspot%20Detection%20Using%20Bipartite%20Network%20Modelling%20Approach.pdf; http://ir.unimas.my/id/eprint/40714/; https://publisher.unimas.my/ojs/index.php/TUR/article/view/3012
• Institution: Universiti Malaysia Sarawak

Despite entering its fourth year, the rabies outbreak in the East Malaysian state of Sarawak has claimed another nine lives in 2020, culminating with a total of 31 laboratory-confirmed cases of human rabies as of 31st December 2020. One of the outbreak control challenges faced by the authorities within a previously rabies-free area, such as in the case of Sarawak, is the lack of information regarding possible starting sources, notably hotspot locations of the outbreak. Identification of potential high-risk areas for rabies infection is a sine qua non for effective disease interventions and control strategies. Motivated by this and in preparation for future similar incidents, this paper presented a preliminary study on rabies hotspot identification. The modelling approach adopted the bipartite network where the two disjoint sets of nodes are the Location node and Dog (Bite Cases) node. The formulation of the network followed closely the Bipartite Modeling Methodology Framework. Thorough model verification was done in an attempt to show that such problem domain can be modelled using the Bipartite Modeling approach.