A Bi-LSTM Prediction Model Integrated with GIS for Spatiotemporal Malaria Endemicity Mapping and Early Warning in Indonesia

Wellie Sulistijanti, Safaat Yulianto, Abdul Syukur, Ngatimin Ngatimin

Abstract


Malaria continues to be a major public health challenge in Indonesia, particularly in eastern provinces where transmission patterns are influenced by climatic variability, geographical heterogeneity, and historical incidence trends. This study proposes an integrated spatio-temporal malaria forecasting and early warning framework by combining Bidirectional Long Short-Term Memory (Bi-LSTM), Geographic Information Systems (GIS), and SHapley Additive exPlanations (SHAP). Monthly malaria incidence, climate variables, population data, and provincial spatial data from 12 endemic provinces in Indonesia during 2014–2025 were used. The data were preprocessed through incidence-rate conversion, outlier handling, log transformation, Min-Max normalization, and six-month sliding window segmentation. The proposed Bi-LSTM model was assesed using RMSE, sMAPE, and R², and compared againts Naive Forecasting, SARIMA, and Simple LSTM baselines. The model attained optimol global performance, with an RMSE of 0.0522, sMAPE of 18.39%, and R² of 0.9553. The provincial analysis shows good performance throughout most regions, including high-burden areas like Papua and West Papua, however a decline in relative accuracy was observed in West Nusa Tenggara due to near-zero incidence rates. SHAP analysis revealed that historical malaria incidence was the primary predictor, whereas rainfall emerged as the most significant climatic variable. GIS-based forecasting showed spatial patterns aligned with malaria epidemiology in Indonesia, with Papua exhibiting the gratest predicted incidence in December 2025. These findings demonstrate that the Bi-LSTM–GIS–SHAP framework can support malaria endemicity mapping, interpretable forecasting, and province-level early warning for targeted public health interventions.


Keywords


Bi-LSTM; Geographic Information Systems; Malaria Prediction; SHAP; Spatiotemporal Analysis

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References


WHO, World Malaria Report. 2024. [Online]. Available: https://www.wipo.int/amc/en/mediation/%0Ahttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023

W. H. Organization, World malaria report 2025: Addressing the threat of antimalarial drug resistance ISBN. Imperial College London, 2025.

W. Report, World Malaria Report 2021, vol. 13, no. April. 2021.

Y. S. Kitawa and Z. G. Asfaw, “Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia,” Malar. J., vol. 22, no. 1, pp. 1–13, 2023, doi: 10.1186/s12936-023-04742-9.

G. J. Gbaguidi, N. Topanou, W. L. Filho, and G. K. Ketoh, “Potential impact of climate change on the transmission of malaria in Northern Benin, West Africa,” Theor. Appl. Climatol., vol. 155, no. 5, pp. 3525–3539, 2024, doi: 10.1007/s00704-023-04818-1.

M. Z. Alam, S. M. Niaz Arifin, H. M. Al-Amin, M. S. Alam, and M. S. Rahman, “A spatial agent-based model of Anopheles vagus for malaria epidemiology: Examining the impact of vector control interventions,” Malar. J., vol. 16, no. 1, pp. 1–20, 2017, doi: 10.1186/s12936-017-2075-6.

E. Naroum et al., “Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon’s Adamaoua region,” Intell. Med., vol. 11, no. February, p. 100220, 2025, doi: 10.1016/j.ibmed.2025.100220.

L. Demoze, F. Gubena, E. Akalewold, H. Brhan, T. Kifle, and G. Yitageasu, “Spatial, temporal, and spatiotemporal cluster detection of malaria incidence in Southwest Ethiopia,” Front. Public Heal., vol. 12, no. January, 2024, doi: 10.3389/fpubh.2024.1466610.

I. Diouf et al., “Impact of future climate change on malaria in West Africa,” Theor. Appl. Climatol., vol. 147, no. 3–4, pp. 853–865, 2022, doi: 10.1007/s00704-021-03807-6.

Ministry of Health of The Republic of Indonesia, “Malaria Cases in Indonesia,” Ministry of Health of The Republic of Indonesia. [Online]. Available: https://malaria.kemkes.go.id/case

W. Sulistijanti, L. Khikmah, E. A. Rahmasari, C. A. P. Sangnandha, I. M. Yusuf, and D. A. Azizah, “Predicting Malaria Incidence Using LSTM and Environmental Variables Corresponding,” vol. 9, no. 2, pp. 466–476, 2025, doi: 10.30829/zero.v9i2.26043.

BPS-Statistics Indonesia, Stastical Yearbook of Indonesia 2025. BPS-Statistics Indonesia, 2025.

Ministry of Health of The Republic of Indonesia, “National Action Plan for Acceleration of Malaria Elimination 2020-2026 (Revision),” vol. 2026, 2023.

R. Komura and M. Matsuoka, “High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data,” ISPRS Int. J. Geo-Information, vol. 12, no. 12, 2023, doi: 10.3390/ijgi12120489.

X. Liu, C. Song, Z. Ren, and S. Wang, “Predicting the Geographical Distribution of Malaria-Associated Anopheles dirus in the South-East Asia and Western Pacific Regions Under Climate Change Scenarios,” Front. Environ. Sci., vol. 10, no. June, pp. 1–13, 2022, doi: 10.3389/fenvs.2022.841966.

D. M. Menda et al., “Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014-2020 Routine Data,” Adv. Public Heal., vol. 2021, 2021, doi: 10.1155/2021/6522352.

N. Mahdizadeh Gharakhanlou, M. S. Mesgari, and N. Hooshangi, “Developing an agent-based model for simulating the dynamic spread of Plasmodium vivax malaria: A case study of Sarbaz, Iran,” Ecol. Inform., vol. 54, no. September, p. 101006, 2019, doi: 10.1016/j.ecoinf.2019.101006.

A. Gaouar, S. Hamza, A. Rahmoun, M. El, and H. Daho, “Informatics in Medicine Unlocked Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms,” Informatics Med. Unlocked, vol. 57, no. March, p. 101667, 2025, doi: 10.1016/j.imu.2025.101667.

W. Sulistijanti, L. Khikmah, and A. Rahmasari, Erisa, “Predicting Malaria Incidence Using LSTM and Environmental Variables Corresponding Author :,” Zero J. Sains, Mat. dan Terap., vol. 9, no. 2, pp. 466–476, 2025, doi: 10.30829/zero.v9i2.26043.

L. Muflikhah, N. Widodo, N. Yudistira, and A. Ridok, “Drug Resistant Prediction Based on Plasmodium Falciparum DNA-Barcoding using Bidirectional Long Short Term Memory Method,” vol. 14, no. 7, pp. 433–440, 2023, doi: 10.14569/IJACSA.2023.0140747.

T. Fisher, S. Rojas-galeano, and D. Fernandez-reyes, “Specialist hybrid models with asymmetric training for malaria prevalence prediction,” Front. Public Heal., vol. 11, no. 1207624, 2023, doi: 10.3389/fpubh.2023.1207624.

C. Wang et al., “MalariaSED : a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites,” Genome Biol., pp. 1–20, 2023, doi: 10.1186/s13059-023-03063-z.

H. Alizadegan, B. Rashidi Malki, A. Radmehr, H. Karimi, and M. A. Ilani, “Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction,” Energy Explor. Exploit., 2024, doi: 10.1177/01445987241269496.

D. Diriba, S. Karuppannan, T. Regasa, and M. Kasahun, “Spatial analysis and mapping of malaria risk areas using geospatial technology in the case of Nekemte City, western Ethiopia,” Int. J. Health Geogr., vol. 23, no. 1, 2024, doi: 10.1186/s12942-024-00386-3.

D. S. S. Rejeki, S. Nurlaela, D. Octaviana, B. Wijayanto, and S. Solikhah, “Clusters of malaria cases at sub-district level in endemic area in Java Island, Indonesia,” Geospat. Health, vol. 17, no. 1, 2022, doi: 10.4081/gh.2022.1048.

S. P. Mwangungulu, D. Dorothea, Z. R. Ngereja, and E. W. Kaindoa, “Geospatial based model for malaria risk prediction in Kilombero valley, South-eastern, Tanzania,” PLoS One, vol. 18, no. 10 OCTOBER, pp. 1–23, 2023, doi: 10.1371/journal.pone.0293201.

I. Byrne et al., “Characterizing the spatial distribution of multiple malaria diagnostic endpoints in a low-transmission setting in Lao PDR,” Front. Med., vol. 9, 2022, doi: 10.3389/fmed.2022.929366.

A. Salahi-Moghaddam, H. Turki, M. Yeryan, and M. V. Fuentes, “Spatio-temporal Prediction of the Malaria Transmission Risk in Minab District (Hormozgan Province, Southern Iran),” Acta Parasitol., vol. 67, no. 4, pp. 1500–1513, 2022, doi: 10.1007/s11686-022-00598-2.

M. Javaid, M. S. Sarfraz, M. U. Aftab, Q. uz Zaman, H. T. Rauf, and K. A. Alnowibet, “WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases,” Int. J. Environ. Res. Public Health, vol. 20, no. 4, 2023, doi: 10.3390/ijerph20043740.

W. Sulistijanti and N. Khotimah, “Comparing Time Series Predictions of COVID-19 Deaths Using SARIMAX , Neural Network , and XGBoost,” vol. 3, no. 12, pp. 2751–2758, 2024, [Online]. Available: https://doi.org/10.46799/ajesh.v3i12.481

R. Alkhanbouli, H. Matar Abdulla Almadhaani, F. Alhosani, and M. C. E. Simsekler, “The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.,” BMC Med. Inform. Decis. Mak., vol. 25, no. 1, p. 110, Mar. 2025, doi: 10.1186/s12911-025-02944-6.

S. K. Ghosh and A. H. Khandoker, “Investigation on explainable machine learning models to predict chronic kidney diseases,” Sci. Rep., vol. 14, no. 1, pp. 1–15, 2024, doi: 10.1038/s41598-024-54375-4.

W. Haileselassie et al., “International border malaria transmission in the Ethiopian district of Lare, Gambella region: implications for malaria spread into South Sudan,” Malar. J., vol. 22, no. 1, pp. 1–10, 2023, doi: 10.1186/s12936-023-04479-5.

A. Fayyazbakhsh, T. Kienberger, and J. Vopava-wrienz, “Comparative Analysis of Load Profile Forecasting: LSTM, SVR, and Ensemble Approaches for Singular and Cumulative Load Categories,” Smart Cities, vol. 8, no. 65, 2025, [Online]. Available: https://doi.org/10.3390/smartcities8020065

Y. Xu, B. Guo, Y. Chen, and X. Liu, “Multiscale Nonlinear Forecasting of Government Bond Yields and Volatility via a Hybrid VMD – LSTM Framework,” Mathematics, vol. 14, no. 1297, pp. 1–22, 2026, doi: https://doi.org/10.3390/math14081297.

William H. Greene, Econometric Analysis. Pearson, 2020.




DOI: https://doi.org/10.47738/jads.v7i3.1483

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