Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance

Gilian Therese B. Madrid; Maria Regina Justina E. Estuar; Mark Anthony C. Tolentino; Timothy Robin Y. Teng; Elvira P. De Lara-Tuprio; Angelica Garcia; Rolando V. Cruz; Christian E. Pulmano; Raphael B. Alampay

doi:10.1145/3761712.3761781

Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance

Gilian Therese B. Madrid
, Maria Regina Justina E. Estuar
, Mark Anthony C. Tolentino
, Timothy Robin Y. Teng
, Elvira P. De Lara-Tuprio
, Angelica Garcia
, Rolando V. Cruz
, Christian E. Pulmano
, Raphael B. Alampay

Ateneo de Manila University
Ateneo Center for Computing Competency and Research
Local Government Epidemiology and Surveillance Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics
Publisher	Association for Computing Machinery, Inc
Pages	312-316
Number of pages	5
ISBN (Electronic)	9798400715143
DOIs	https://doi.org/10.1145/3761712.3761781
State	Published - Dec 30 2025
Event	2025 9th International Conference on Medical and Health Informatics, ICMHI 2025 - Kyoto, Japan Duration: May 16 2025 → May 18 2025

Publication series

Name	ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics

Conference

Conference	2025 9th International Conference on Medical and Health Informatics, ICMHI 2025
Country/Territory	Japan
City	Kyoto
Period	5/16/25 → 5/18/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus Subject Areas

Developmental and Educational Psychology
Clinical Psychology
Artificial Intelligence
Cardiology and Cardiovascular Medicine

Keywords

Bayesian Methods
Dengue
Disease Surveillance
Epidemiological Models
Spatiotemporal Analysis

Access to Document

10.1145/3761712.3761781

Cite this

Madrid, G. T. B., Estuar, M. R. J. E., Tolentino, M. A. C., Teng, T. R. Y., De Lara-Tuprio, E. P., Garcia, A., Cruz, R. V., Pulmano, C. E., & Alampay, R. B. (2025). Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance. In ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics (pp. 312-316). (ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/3761712.3761781

Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance. / Madrid, Gilian Therese B.; Estuar, Maria Regina Justina E.; Tolentino, Mark Anthony C. et al.
ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics. Association for Computing Machinery, Inc, 2025. p. 312-316 (ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Madrid, GTB, Estuar, MRJE , Tolentino, MAC , Teng, TRY , De Lara-Tuprio, EP, Garcia, A, Cruz, RV, Pulmano, CE & Alampay, RB 2025, Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance. in ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics. ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics, Association for Computing Machinery, Inc, pp. 312-316, 2025 9th International Conference on Medical and Health Informatics, ICMHI 2025, Kyoto, Japan, 5/16/25. https://doi.org/10.1145/3761712.3761781

Madrid GTB, Estuar MRJE , Tolentino MAC , Teng TRY , De Lara-Tuprio EP, Garcia A et al. Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance. In ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics. Association for Computing Machinery, Inc. 2025. p. 312-316. (ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics). doi: 10.1145/3761712.3761781

Madrid, Gilian Therese B. ; Estuar, Maria Regina Justina E. ; Tolentino, Mark Anthony C. et al. / Citywide District-Level Weekly Dengue Forecasting : Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance. ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics. Association for Computing Machinery, Inc, 2025. pp. 312-316 (ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics).

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title = "Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance",

keywords = "Bayesian Methods, Dengue, Disease Surveillance, Epidemiological Models, Spatiotemporal Analysis",

author = "Madrid, \{Gilian Therese B.\} and Estuar, \{Maria Regina Justina E.\} and Tolentino, \{Mark Anthony C.\} and Teng, \{Timothy Robin Y.\} and \{De Lara-Tuprio\}, \{Elvira P.\} and Angelica Garcia and Cruz, \{Rolando V.\} and Pulmano, \{Christian E.\} and Alampay, \{Raphael B.\}",

note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 2025 9th International Conference on Medical and Health Informatics, ICMHI 2025 ; Conference date: 16-05-2025 Through 18-05-2025",

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AU - Madrid, Gilian Therese B.

AU - Estuar, Maria Regina Justina E.

AU - Tolentino, Mark Anthony C.

AU - Teng, Timothy Robin Y.

AU - De Lara-Tuprio, Elvira P.

AU - Garcia, Angelica

AU - Cruz, Rolando V.

AU - Pulmano, Christian E.

AU - Alampay, Raphael B.

PY - 2025/12/30

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KW - Bayesian Methods

KW - Dengue

KW - Disease Surveillance

KW - Epidemiological Models

KW - Spatiotemporal Analysis

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DO - 10.1145/3761712.3761781

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BT - ICMHI 2025 - 2025 9th International Conference on Medical and Health Informatics

PB - Association for Computing Machinery, Inc

Y2 - 16 May 2025 through 18 May 2025

ER -

Citywide District-Level Weekly Dengue Forecasting: Evaluation of Bayesian Spatiotemporal Models for Routine Surveillance

Publication series

Conference

UN SDGs

ASJC Scopus Subject Areas

Keywords

Access to Document

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