DENGUE HEMORRHAGIC FEVER (DHF) DISTRIBUTION MAP MODELLING IN BANDUNG CITY

• Main Author: Taufiq Nur Rahman, Ahmad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/74666
• Institution: Institut Teknologi Bandung

Dengue Hemorrhagic Fever (DHF) is a disease caused by dengue virus infection transmitted through the bite of Aedes aegypti and Aedes albocpictus mosquitoes. DHF poses a serious threat to tropical countries, including Indonesia. Bandung City as one of the cities with the highest level of DHF disease in 2022, needs special attention in handling and prevention efforts so that the spread of this disease does not expand and the incidence of DHF cases does not increase. The purpose of this study is to review the distribution map of DHF cases, the growth rate of DHF disease, and construct the best model for DHF disease with climate information in Bandung City for each sub-district. In this study, data on dengue cases from Dinas Kesehatan of Bandung City and climate data from Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) of Bandung City including rainfall, relative humidity, and temperature from January 2009 to October 2022 were used. Visual analysis was conducted by reviewing the DHF case distribution map derived from the cumulative amount of data for each year of DHF cases for each sub-district in Bandung City. Cluster grouping using Hierarchial Clustering aims to determine the group of sub-districts in a cluster. Coblong sub-district was one of the sub-districts included in the cluster group with the highest DHF cases. Richard curve matching was used to obtain information on the growth rate of DHF disease in each specific time period. Every period, there was an outbreak of DHF cases so that Richard's curve was stepped according to the cumulative number of DHF cases. The average growth rate of DHF in Bandung City from the Richard curve matching results was 0.0005308. The best model construction was obtained by creating a Generalized Linear Model (GLM) started with Autoregressive (AR), Spearman Rank Correlation, Poisson regression, and Negative Binomial regression. The combination regression model 7 became a model of Negative Binomial regression based on the selection of the smallest AIC value of 1985.236102.