Predicting potential areas at risk of the Dengue Hemorrhagic Fever in Jakarta, Indonesia—analyzing the accuracy of predictive hot spot analysis in the absence of small geographical area data

ABSTRACT

Dengue Hemorrhagic Fever (DHF), a more severe form of dengue, is one of the most rapidly spreading mosquito-borne diseases in the world. This study is motivated by the rising DHF incidence in Jakarta, the capital city of Indonesia. We mainly utilized hot spot analysis, which employs spatial statistics to find at-risk areas for DHF outbreaks in Jakarta’s five municipalities. However, producing informative results from hot spot analysis requires a complete set of data on each of Jakarta’s 42 districts, which is not available. We thus propose the idea of using small area estimation (SAE) and machine learning to make up for the lack of data. To evaluate whether this proposed method is effective, we compare the hot spot results from the estimation with the actual data of each district. The results show that the estimated hot spot map is similar to the hot spot map from the actual data. This implies that it is possible to find potential at-risk areas of dengue fever without a complete dataset in every small geographic area. We expect that this research can increase the performance of DHF control measures at the district level, even in the absence of small area data.

KEYWORDS:

• Getis-Ord Gi
• hot spot analysis
• machine learning
• small area estimation
• support vector regression

Acknowledgments

We would like to thank Indonesia’s Meteorology, Climatology, and Geophysical Agency and the Jakarta Health Department for their datasets and general support. Additionally, we wish to thank Cindy and Cynthia for their help in the data preprocessing stage.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Universitas Indonesia, PUTI 2020 Grant Scheme Number: NKB-1011/UN2.RST/HKP.05.00/2020.