Spatial analysis and forecasting map of diarrhea incidents in Banjar District

Diarrhea is a common disease in the community and can be fatal if treatment is delayed. Banjar District has recorded the highest prevalence of diarrhea in South Kalimantan for the past few years, making it one of the causes of death in toddlers. This study aims to conduct spatial analysis using Moran's I index and local indicators of spatial association (LISA). Diarrhea case predictions are made using the multiplicative decomposition time series method. The data used in this study are diarrhea case data from 20 sub-districts in Banjar District during the period 2016-2022. Although no global autocorrelation was found in Banjar District, there were two sub-districts that showed local autocorrelation. The prediction results show a decreasing trend in diarrhea cases in most sub-districts. Health interventions can be focused on areas with high risk, such as hotspot areas and areas predicted not to experience a decrease in diarrhea cases.

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Publication Details

Journal
International Journal of Public Health Science (IJPHS)
Published
2024-09-13
DOI
https://doi.org/10.11591/ijphs.v14i1.24682
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Spatial analysis and forecasting map of diarrhea incidents in Banjar District

Eko Suhartono, Isna Syauqiah, Deni Fakhrizal, Nopi Stiyati Prihartini et al.
International Journal of Public Health Science (IJPHS)
Data Mining and Machine Learning Applications
article

Spatial analysis and forecasting map of diarrhea incidents in Banjar District

Eko Suhartono, Isna Syauqiah, Deni Fakhrizal, Nopi Stiyati Prihartini, Meitria Syahdatina Noor
article en

Abstract

Diarrhea is a common disease in the community and can be fatal if treatment is delayed. Banjar District has recorded the highest prevalence of diarrhea in South Kalimantan for the past few years, making it one of the causes of death in toddlers. This study aims to conduct spatial analysis using Moran's I index and local indicators of spatial association (LISA). Diarrhea case predictions are made using the multiplicative decomposition time series method. The data used in this study are diarrhea case data from 20 sub-districts in Banjar District during the period 2016-2022. Although no global autocorrelation was found in Banjar District, there were two sub-districts that showed local autocorrelation. The prediction results show a decreasing trend in diarrhea cases in most sub-districts. Health interventions can be focused on areas with high risk, such as hotspot areas and areas predicted not to experience a decrease in diarrhea cases.

International Journal of Public Health Science (IJPHS)Vol. 14(1)
Ministère de la Santé Publique (BI), Lambung Mangkurat University (ID)
Good health and well-being
Openalex Percentile: Top 76%
Data Mining and Machine Learning Applications
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