References
- World Health Organization. Global strategy for dengue prevention and control 2012–2020. Geneva: World Health Organization. 2012.
- World Health Organization. Dengue and severe dengue. No. WHOEM/MAC/032/E. Cairo: World Health Organization. Regional Office for the Eastern Mediterranean; 2014.
- Guo P, Liu T, Zhang Q, et al. Developing a dengue forecast model using machine learning: a case study in China. PLoS negl trop dis. 2017;11(10):e0005973.
- Murphy A, Rajahram GS, Jilip J, et al. Incidence and epidemiological features of dengue in Sabah, Malaysia. PLoS negl trop dis. 2020;14(5):e0007504.
- Redondo-Bravo L, Ruiz-Huerta C, Gomez-Barroso D, et al. Imported dengue in Spain: a nationwide analysis with predictive time series analyses. J Travel Med. 2019;26(8):taz072.
- Sintayehu DW, Tassie N, De Boer WF. Present and future climatic suitability for dengue fever in Africa. Infection Ecology & Epidemiology. 2020;10(1):1782042.
- Haryanto B. Indonesia dengue fever: status, vulnerability, and challenges. Current Topics In Tropical Emerging Diseases And Travel Medicine. 2018;5:81–9.
- Ministry of Health of the Republic of Indonesia. The situation of dengue fever in Indonesia in the year 2017. Jakarta: Ministry of Health of the Republic of Indonesia. Text in Indonesian; 2018.
- Sulistyawati S, Fitriani I . Risk factor and cluster analysis to identify malaria hot spot for control strategy in Samigaluh Sub-District, Kulon Progo, Indonesia. Iran J Public Health. 2019;48(9):1647.
- Bogale GG, Gelaye KA, Degefie DT, et al. Spatial patterns of childhood diarrhea in Ethiopia: data from Ethiopian demographic and health surveys (2000, 2005, and 2011). BMC Infect Dis. 2017;17(1):426.
- Hart TC, Zandbergen PA. Effects of data quality on predictive hotspot mapping, 239861. Washington (DC): National Criminal Justice Research Service; 2012.
- Wang X, Varady DP. Using hot-spot analysis to study the clustering of Section 8 housing voucher families. Housing Studies. 2005;20(1):29–48.
- Stasny EA, Goel PK, Ramsey OJ. County estimates of wheat production. Survey Methodology. 1991;17(2):211–225.
- Christiaensen L, Lanjouw P, Luoto J, et al. Small area estimation-based prediction methods to track poverty: validation and applications. The Journal Of Economic Inequality. 2012;10(2):267–297.
- Molina I, Rao J. Small area estimation of poverty indicators. Can J Stat. 2010;38(3):369–385.
- Costa R, Ed. Predictive modeling and risk assessment. Vol. 4 New York: Springer Science & Business Media; 2008.
- Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.
- Mishra G, Sehgal D, Valadi JK. Quantitative structure activity relationship study of the anti-hepatitis peptides employing random forests and extra-trees regressors. Bioinformation. 2017;13(3):60.
- Tanawi IN, Vito V, Sarwinda D, et al. Support vector regression for predicting the number of dengue incidents in DKI Jakarta. Procedia Comput Sci. 2021;179:747–753.
- Rao JNK, Molina I. Small area estimation. John Wiley & Sons: 2015. 10.1002/9781118735855.
- Rao JN. Small-area estimation. Wiley StatsRef: Statistics Reference Online. Hoboken: John Wiley & Sons; 2014. p. 1–8.
- Kalinic M, Krisp JM (2018). Kernel Density Estimation (KDE) vs. hot-spot analysis–detecting criminal hot spots in the city of San Francisco. Proceeding of the 21st Conference on Geo-Information Science, Lund.
- Getis A, Ord JK. 2010. The analysis of spatial association by use of distance statistics. Perspectives on Spatial Data Analysis pp. 127–145. Springer. 10.1007/978-3-642-01976-0_10.
- Suryowati K, Bekti RD, Faradila A. A comparison of weights matrices on computation of dengue spatial autocorrelation. IOP Conf Ser Mater Sci Eng. 2018;335(1):012052.
- Yan Y, Hu W. Does foreign direct investment affect tropospheric SO2 emissions? A spatial analysis in Eastern China from 2011 to 2017. Sustainability. 2020;12(7):2878.
- Danades A, Pratama D, Anggraini D, et al. (2016). Comparison of accuracy level K-nearest neighbor algorithm and support vector machine algorithm in classification water quality status. 2016 6th International Conference on System Engineering and Technology (ICSET); Bandung (pp. 137–141). IEEE.
- Tamatjita EN, Mahastama AW (2016). Comparison of music genre classification using Nearest Centroid Classifier and k-Nearest Neighbours. 2016 International Conference on Information Management and Technology (ICIMTech); Bandung (pp. 118–123). IEEE.
- Brown JM (2017). Predicting math test scores using k-nearest neighbor. 2017 IEEE Integrated STEM Education Conference (ISEC); Princeton (pp. 104–106). IEEE.
- Syaliman KU, Nababan EB, Sitompul OS. Improving the accuracy of k-nearest neighbor using local mean based and distance weight. J Phys. 2018;978(1):012047. doi:10.1088/1742-6596/978/1/012047. IOP Publishing.
- Caruana R, Niculescu-Mizil A (2006). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine learning; Pittsburgh (pp. 161–168).
- Caruana R, Karampatziakis N, Yessenalina A (2008, July). An empirical evaluation of supervised learning in high dimensions. Proceedings of the 25th International Conference on Machine learning; Helsinki (pp. 96–103).
- Calhoun P, Hallett MJ, Su X, et al. Random forest with acceptance–rejection trees. Computational Statistics. 2019;35:983–999.
- Rawlings JO, Pantula SG, Dickey DA. Applied regression analysis: a research tool. New York: Springer Science & Business Media; 2001.
- Cleophas TJ, Zwinderman AH. Regression analysis in medical research: for starters and 2nd levelers. New York: Springer; 2018. 10.1007/978-3-319-71937-5
- Sungkar S, Fadli RS, Sukmaningsih A. Trend of dengue hemorrhagic fever in North Jakarta. Journal Of The Indonesian Medical Association. 2012;61(10):394–399.
- Cawood P, Van Zyl T. Evaluating state-of-the-art, forecasting ensembles and meta-learning strategies for model fusion. Forecasting. 2022;4(3):732–751.
- Mahmoud A, Mohammed A. 2021. A survey on deep learning for time-series forecasting. Machine learning and big data analytics paradigms: analysis, applications and challengespp. 365–392. Springer; Cham:10.1007/978-3-030-59338-4_19.
- Wijaya KP, Aldila D, Schäfer LE. Learning the seasonality of disease incidences from empirical data. Ecol Complexity. 2019;38:83–97.
- Nuraini N, Fauzi IS, Fakhruddin M, et al. Climate-based dengue model in Semarang, Indonesia: predictions and descriptive analysis. Infect Dis Model. 2021;6:598–611.
- Faridah L, Rinawan FR, Fauziah N, et al. Evaluation of health information system (HIS) in the surveillance of dengue in Indonesia: lessons from case in Bandung, West Java. Int J Environ Res Public Health. 2020;17(5):1795.
- Brady OJ, Gething PW, Bhatt S, et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS negl trop dis. 2012;6(8):e1760.