Kien Quoc Do

Background: Dengue is a major vector-borne disease, with rising incidence driven by climate change, rapid urbanization, and increased international travel. Although early warning systems (EWSs) have shown promising predictive performance in endemic settings, limited evidence on implementation barriers from local end users hinders their integration into routine proactive vector control. Methods: A qualitative study with in-depth interviews was conducted with local end users, including local authorities, health practitioners, and health volunteers. A deductive approach utilizing a predefined conceptual framework was employed to code the verbatim transcripts. Inductive coding was subsequently incorporated to capture emerging concepts that were not represented in the initial framework. Thematic analysis was performed to analyse the collected data. To enhance the methodological rigor, interviews and coding were performed by multiple and diverse researchers from various academic and practi ce institutions, and the transcripts were validated through member checking with selected participants. Results: The lack of supportive policies, limited engagement from relevant stakeholders due to low familiarity with EWS concepts, and operational challenges were identified as critical barriers to implementing EWS-informed proactive source reduction. The perception that dengue risks were only during the rainy season, together with socio-economic constraints, water scarcity exacerbated by extreme weather events, and the application of inappropriate source reduction measures, further constrained household participation. The multi-level barriers collectively disrupted the translation of EWS alerts into proactive dengue prevention. Conclusions: Early warning information alone is insufficient to translate into effective and sustained proactive vector control for dengue prevention. Strengthening enabling policies, local stakeholder and community engagement, and awareness of EWS-informed risk management across multiple levels are essential to translate EWS alerts into timely and sustained vector control actions in dengue-endemic settings similar to Vietnam. Further studies across diverse settings are needed to assess the broader applicability of EWS-informed approaches.

Dengue presents a major public health challenge in Vietnam, driven by biological, behavioural, and environmental factors. Compound climate extremes, such as sequential hydrometeorological events, can influence dengue risk yet remain understudied. We evaluated the effects of compound climate extremes, socioeconomic conditions, and human mobility on dengue relative risk across 670 districts in Vietnam over 20 years, stratifying by eight subregions spanning emerging to endemic transmission. Dengue risk was greatest following dry-then-wet conditions in seven subregions. Higher temperatures increased risk across North and Central Vietnam, but had limited effect in the South. Mobility associations followed an urban-rural gradient, with increased risk in highly rural districts where residents travelled more frequently to fewer destinations, and in urban districts with more visitors and dispersed outgoing mobility. Stratifying climatic and socioeconomic effects by subregion improved predictive skill at lead times of 1 to 6 months over unstratified and baseline models, with substantial spatial variation. Accounting for compound extremes across distinct spatial contexts could strengthen disease early warning systems in Vietnam and beyond.

African swine fever (ASF) is a viral disease of domestic and wild pigs that has re-emerged as a major transboundary disease. Modelling using mechanistic, statistical, and machine learning (ML) approaches plays a key role in understanding ASF transmission and informing disease control, but the literature remains fragmented. To synthesise global ASF modelling efforts, we systematically reviewed studies applying these three approaches. We examined temporal and geographic trends, modelling objectives, explanatory variables, and model evaluation practices. A total of 151 papers published through 2024 met the inclusion criteria. Mechanistic (54.3%) and statistical (40.4%) approaches predominated, whereas ML (9.3%) was increasingly applied in recent years. Mechanistic models were primarily used to assess control strategies (48.8%) and transmission drivers (41.5%), statistical models to identify risk factors (63.9%) and spatiotemporal spread (32.8%), and ML for environmental suitability modelling (64.3%). Most were published from 2011 (99.3%) and focused on Europe (43.0%) and Asia (26.5%). Model evaluation remained inconsistent, with mechanistic papers frequently lacking model output uncertainty quantification (47.0%) and statistical papers often omitting model adequacy assessment (49.2%) and assumption checking (50.8%). Overall, ASF modelling approaches have developed complementary methodological roles, while geographic underrepresentation, limited representation of some transmission pathways, and inconsistent model evaluation remain important gaps.

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.

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue’s distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue’s expansion throughout Vietnam.