Rabies Hotspot Detection Using Bipartite Network Modelling Approach

Authors

  • DAREN JIAN BING CHIA Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • WOON CHEE KOK Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • NUR ASHEILA ABDUL TAIB Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • BOON HAO HONG Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • KHAIRANI ABD MAJID Department of Computer Science, Faculty of Defence Science and Technology, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia
  • JANE LABADIN Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.

DOI:

https://doi.org/10.33736/tur.3012.2021

Keywords:

Bipartite Network Modeling Framework, BRC, Dog, Location, Rabies, Ranking

Abstract

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.

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Published

2021-06-29

How to Cite

CHIA, D. J. B. ., KOK, W. C. ., ABDUL TAIB , N. A. ., HONG, B. H. ., ABD MAJID, K., & LABADIN, J. (2021). Rabies Hotspot Detection Using Bipartite Network Modelling Approach. Trends in Undergraduate Research, 4(1), c52–60. https://doi.org/10.33736/tur.3012.2021

Issue

Section

Computer Science and Information Technology