Validation of bipartite network model of dengue hotspot detection in Sarawak

Kok, Woon Chee and Labadin, Jane (2019) Validation of bipartite network model of dengue hotspot detection in Sarawak. In: Computational Science and Technology. Lecture Notes in Electrical Engineering, 481 (481). © Springer Nature Singapore Pte Ltd., pp. 335-345.

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Abstract

This paper presents the verification and validation processes in producing a realistic bipartite network model to detect dengue hotspot in Sarawak. Based on the result of previous published work, ranking of location nodes of possible dengue hotspot at Sarawak are used to illustrate the validation by comparing the Spearman rank correlation coefficients (SRCC) between the network models. UCINET 6 is used to generate a benchmark ranking result for model verification. A centrality measure analysis feature available in UCINET is used to determine the node centrality of a network model. The validation results show strong ranking similarity for all three groups of network models with good Spearman rank correlation coefficients values of 1.000, 0.8000 and 0.8824 (ρ>0.80; p<0.001) respectively. The top-ranked locations are seen as dengue hotspots and this study demonstrate a new approach to model dengue transmission at district-level by locating the hotspots and prioritizing the locations according to vector density. © Springer Nature Singapore Pte Ltd. 2019.

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