Jayaraj, Vivek Jason and Ng, Chiu Wan and Hoe, Victor Chee Wai and Chong, Diane Woei-Quan and Rampal, Sanjay (2024) Rapidly scalable and low-cost public health surveillance reporting system for COVID-19. BMJ Health and Care Informatics, 31 (1). ISSN 2632-1009, DOI https://doi.org/10.1136/bmjhci-2023-100759.
Full text not available from this repository.Abstract
Objective Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. Methods A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources. Results Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5 user conversion rate, evidencing its value and adaptability. Conclusion This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios. © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Item Type: | Article |
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Funders: | UNSPECIFIED |
Uncontrolled Keywords: | Data interpretation, Statistical; Data visualization; Health communication; Information dissemination; Public health informatics |
Subjects: | R Medicine T Technology > T Technology (General) |
Divisions: | Faculty of Medicine > Social & Preventive Medicine |
Depositing User: | Ms. Juhaida Abd Rahim |
Date Deposited: | 13 May 2024 07:39 |
Last Modified: | 13 May 2024 07:39 |
URI: | http://eprints.um.edu.my/id/eprint/44924 |
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