A Predictive Map of Larvae Presence Risk Based on Modeling Algorithm in Urban Settings of Endemic Area

¹ Health Promotion Department, Health Faculty, Sari Mulia University, Indonesia
² Department of Medical Chemistry/Biochemistry, Faculty of Medicine and Health Science, Lambung Mangkurat University, Indonesia
³ Information System Department, Science and Technology Faculty, Sari Mulia University, Indonesia
⁴ Information Technology Department, Science and Technology Faculty, Sari Mulia University, Indonesia
⁵ Medical Record Department, Health Science School of Kesdam VI/Tanjungpura, Indonesia

^* Corresponding author: nurulhidayah@unism.ac.id

Abstract

Dengue is an infectious disease that is still a concern and requires severe treatment. One of the prevention efforts is health promotion activities regarding Dengue prevention in risk areas. Preparing a health promotion strategy will be effective and efficient if it is based on target area study data, which can be done by identifying risks and creating area mapping based on larval presence data. Jorong District has the highest incidence rate in Tanah Laut Regency, divided into 11 villages. This research used map methods and design. The research population was 10,003 households, and the sample size was 100 households, which was taken using simple random sampling. The larvae risk data were analyzed univariately and presented as a risk percentage. The research results showed that Jorong Village had the highest risk (62.66%) and Alur Village had the lowest risk (41.28%). There are five villages with a high category, namely Sabuhur Village (50.65%), Jorong (62.66%), Asam Jaya (59.93%), Asri Mulya (56.72%), and Batalang (55.03%). About 84% of high-risk villages had larvae, and 80% of low-risk villages had no larvae. It was concluded that risk mapping was proven to have 82% accuracy (good) in predicting the presence of larvae.