Challenges and Strategies for the Development and Implementation of Climate‐Informed Early Warning Systems for Vector‐Borne Diseases: A Systematic Review
Cong Tuan Pham, Ha Thu Nguyen, Hong H. T. C. Le, Nu Quy Linh Tran, Do Kien Quoc, Vinh Bui, Hai Phung, Dung Phung, Cordia Chu
Abstract
BACKGROUND: Vector-borne diseases, exacerbated by climate change, present an escalating global health threat, necessitating robust surveillance and climate-informed early warning systems to predict outbreaks and enable timely interventions. This systematic review aims to synthesise the challenges and strategies involved in developing and operationalising early warning systems for vector-borne diseases. METHODS: Following PRISMA guidelines, we conducted a systematic search across multiple databases (PubMed, Web of Science, Scopus and Embase) and performed a manual search using predefined keywords up to 05 November 2024. Eleven papers were selected for the reviewing process. RESULTS: While early warning systems show significant promise in enhancing outbreak prediction and guiding timely public health interventions, several key challenges persist. Inadequate data quality and integration-characterised by fragmented epidemiological, entomological and meteorological datasets-compromise predictive accuracy. The review also highlights gaps in stakeholder engagement and capacity building. Without comprehensive training and active collaboration among public health officials, climate scientists and data analysts, the practical application and sustainability of these systems are undermined. Enhancing data harmonisation through standardised collection processes and integration protocols is crucial for improving model reliability. The adoption of scalable, cloud-based platforms can mitigate technical and infrastructural limitations by enabling real-time data processing and robust computational capabilities. Strengthening interdisciplinary collaborations-bringing together experts from diverse fields-can refine predictive models and ensure that system outputs are both accurate and actionable. Furthermore, tailored capacity-building initiatives are vital for empowering local authorities to effectively interpret and implement early warning systems' warning signals. Finally, optimising communication strategies by simplifying technical outputs and developing user-friendly interfaces can bridge the gap between complex predictive analytics and practical decision-making processes. CONCLUSION: Addressing these challenges through integrated solutions will enhance the effectiveness and sustainability of early warning systems, ultimately improving outbreak preparedness and response for vector-borne diseases in a changing climate.