Exploring Bipartite Network Approach in Hand, Foot and Mouth Disease Hotspot Identification

• Main Authors: Chin, Ying Liew, Nor Shamira, Sabri, Boon, Hao Hong, Jane, Labadin
• Format: Article
• Language: English
• Published: Penerbit UiTM 2023
• Subjects: QA Mathematics
• Online Access: http://ir.unimas.my/id/eprint/42180/2/Exploring%20Bipartite%20-%20Copy.pdf; http://ir.unimas.my/id/eprint/42180/; https://jsst.uitm.edu.my/index.php/jsst/article/view/39
• Institution: Universiti Malaysia Sarawak

Mathematical modeling of hand, foot, and mouth disease (HFMD) mainly focuses on compartmental modeling approaches. It classifies human population into compartments and assumes homogeneity that regards every human has equal chance of contacting other individuals in the population. However, the transmission of HFMD is complicated and dynamic with the interactions of the intertwined biomed disease transmission dynamic that involves high-dimensional space is mathematically challenging. The graph theoretic bipartite network modeling (BNM) approach has the potential to handle this challenge by abstracting the real-world disease transmission system and incorporating the individual features of the bipartite nodes. This study aims to seize the advantages portrayed by the BNM approach in capturing the heterogeneous features of the entities within a disease transmission system. It intends to explore adopting the BNM approach in modeling the transmission of HFMD at Kuching, Malaysia and identify the hotspot by employing the BNM approach comprising a four-stage methodology adapted from the BNM methodology framework. The bipartite HFMD contact (BHC) network is formulated with the basic building block consisting of the location and human nodes. The individual parameters of the location and human node are incorporated. The resulting BHC network formulated comprises 10 human nodes, 20 location nodes, and 23 edges. Then, six top-ranked location nodes were identified and agreed with the chosen benchmark system. The potential HFMD hotspots are thus identified by determining the location nodes ranking. The result from this study has enabled timely and effective measures and policies to be customized accordingly by the public health authorities and related policymakers.