User acceptance testing of the D-MOSS dengue predictive model as an early warning system in Malaysia

Lim Mei Cheng¹, Nuur Hafizah Md Iderus¹, Sumarni Mohd Ghazali¹, Lonny Chen Rong Qi Ahmad¹, Nur'ain Mohd Ghazali¹, Mohamad Nadzmi Md Nadzri¹, Asrul Anuar Zulkifli¹, Mohd Kamarulariffin Kamarudin¹, Nur Ar Rabiah Ahmad¹, Chew Cheng Hoon¹, Teh Chien Huey¹, Nur Huda Mohd Jaghfar¹, Qistina Ruslan¹, Balvinder Singh Gill¹, Chong Zhuo Lin², Wan Ming Keong³, Tee Kok Keng⁴, Lokman Hakim Sulaiman^5,6, Sarbhan Singh¹

1. Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Jalan Setia Murni U13/52, Seksyen U13, Setia Alam, 40170 Shah Alam, Selangor, Malaysia.
2. Disease Control Division, Ministry of Health Malaysia, Kompleks E, Pusat Pentadbiran Kerajaan Persekutuan, 62590 Wilayah Persekutuan Putrajaya, Malaysia.
3. Department of Medical Microbiology, Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.
4. Department of Public Health and Community Medicine, School of Medicine, IMU University, No. 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000 Kuala Lumpur, Malaysia.
5. Centre for Environment and Population on Health, Institute for Research, Development and Innovation, No. 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000 Kuala Lumpur, Malaysia.

*Corresponding author: Lim Mei Cheng. This email address is being protected from spambots. You need JavaScript enabled to view it.. Tel: +603-33626922

CITATION: Lim MC, Md Iderus NH, Mohd Ghazali S, Ahmad LCRQ, Mohd Ghazali N, Md Nadzri MN, et al. User acceptance testing of the D-MOSS dengue predictive model as an early warning system in Malaysia. International Medical Research Journal. 2025 May 30;11(1):59–76. https://doi.org/10.63719/imrj.2025.11.01.006

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ABSTRACT
Dengue remains a significant public health challenge in Malaysia with recurring outbreaks, highlighting the need for effective forecasting tools. This study aimed to evaluate user acceptance of the Dengue forecasting Model Satellite-based System (D-MOSS) in Malaysia, assessing its usability, strengths, challenges, and areas for improvement. A cross-sectional study was conducted among 32 stakeholders who were directly involved in using D-MOSS for dengue management. Data were collected during a national workshop using a structured survey adapted to the specific operational context of D-MOSS. Thematic analysis was employed to analyse open-ended responses using R for keyword-based categorisation and visualisation. Participants reported high comprehension of D-MOSS features, with a 100% understanding of the outbreak thresholds and forecast values. Common strengths included the system's forecasting capability (66%), impact on outbreak management (22%) and user-friendly interface (12%). Challenges highlighted included accuracy and credibility (54%), performance and technical issues (27%), and granularity and usability of data (19%). For improvements, 40% suggested greater customisation, such as smaller spatial resolution, 25% emphasised interface enhancements, 20% advocated for comparative analysis features, and 15% proposed integrating action planning tools. The system was perceived as a helpful tool for assisting proactive dengue management. D-MOSS demonstrated its user-friendliness and stakeholder adaptability, which support its application in dengue surveillance. Addressing identified challenges and enhancing system features could strengthen its utility and integration, contributing to improved dengue surveillance and control efforts.

KEYWORDS: dengue, early warning systems, user acceptance testing, predictive model, public health surveillance