Modelling Dengue Spread as Dynamic Networks of Time and Location Changes

Setiyoutami, Arfinda; Purwitasari, Diana; Anggraeni, Wiwik; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery

doi:10.22266/ijies2021.0630.29

Cited by 2 publications

(1 citation statement)

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“…Only 2017 on this combination reached 0.413 of the silhouette score feature group combination, which was around double that of 2016 (0.29%). Previous work with dengue fever data shows that demographic location and environment are vital in determining the spread of DHF [18], [19].…”

Section: Results and Discussion Experiments On Feature Group Attributementioning

confidence: 99%

A Clustering Approach for Mapping Dengue Contingency Plan

Husna

Purwitasari²,

Sidharta³

et al. 2022

SJI

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Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.Methods: We used clustering with silhouette scoring to select features, the best cluster formation, the best clustering method, and cluster severity. Cluster severity is carried out by levelling the attributes of the average value to low, medium, high, and extreme, which are related to the plans each region sets for village type and season type.Result: In five years of data (2016-2020) ±15K cases from Semarang City, Indonesia, feature selection results show that environmental and demography group features have the biggest silhouette score. With these features, it is found that K-Means has a high silhouette score compared to DBSCAN and agglomerative with three optimum numbers of clusters. K-Means also successfully mapped the cluster severity and assigned the cluster to a suitable contingency policy.Novelty: Most of the research on DHF cases is about predicting DHF cases and measuring the risk of DHF occurrence. There are not many studies that discuss the policy recommendations for dengue control.

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Section: Results and Discussion Experiments On Feature Group Attributementioning

confidence: 99%