Version 1
: Received: 18 September 2024 / Approved: 18 September 2024 / Online: 19 September 2024 (11:24:01 CEST)
How to cite:
Anh Tuan, D. A Novel Hybrid Model for Dengue Early Warning Systems: Integrating Traditional and Modern Approaches with Real-World Applications. Preprints2024, 2024091457. https://doi.org/10.20944/preprints202409.1457.v1
Anh Tuan, D. A Novel Hybrid Model for Dengue Early Warning Systems: Integrating Traditional and Modern Approaches with Real-World Applications. Preprints 2024, 2024091457. https://doi.org/10.20944/preprints202409.1457.v1
Anh Tuan, D. A Novel Hybrid Model for Dengue Early Warning Systems: Integrating Traditional and Modern Approaches with Real-World Applications. Preprints2024, 2024091457. https://doi.org/10.20944/preprints202409.1457.v1
APA Style
Anh Tuan, D. (2024). A Novel Hybrid Model for Dengue Early Warning Systems: Integrating Traditional and Modern Approaches with Real-World Applications. Preprints. https://doi.org/10.20944/preprints202409.1457.v1
Chicago/Turabian Style
Anh Tuan, D. 2024 "A Novel Hybrid Model for Dengue Early Warning Systems: Integrating Traditional and Modern Approaches with Real-World Applications" Preprints. https://doi.org/10.20944/preprints202409.1457.v1
Abstract
Dengue fever remains one of the most significant public health challenges globally, particularly in tropical and subtropical regions. Traditional early warning systems (EWS) for dengue outbreaks, based on retrospective epidemiological data, suffer from delays and limited predictive accuracy. Modern systems, leveraging real-time data, artificial intelligence (AI), and machine learning (ML), improve accuracy but can be costly and difficult to implement in resource-constrained settings. This paper proposes a novel hybrid model that integrates the strengths of both traditional and modern EWS, with real-world examples from Brazil, and Malaysia. The hybrid model balances accuracy, scalability, and cost-effectiveness, making it a feasible option for low- and middle-income countries.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
