Introduction
Mosquito-borne viral diseases such as dengue, Zika, chikungunya, and yellow fever continue to pose a serious and expanding global public health challenge, driven by urbanization, climate change, population mobility, and vector adaptation. Traditional surveillance systems often struggle to provide timely and accurate early warnings, particularly in low- and middle-income settings. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance disease forecasting and outbreak preparedness. By integrating epidemiological, climatic, environmental, and socio-demographic data, AI/ML models aim to anticipate disease trends before health systems are overwhelmed, supporting proactive interventions rather than reactive responses.
Landscape of AI/ML Models in Arboviral Forecasting
The current research landscape reveals a rapidly growing body of studies applying diverse AI/ML approaches to forecast mosquito-borne viral diseases. These include tree-ensemble models (random forests, gradient boosting), classical machine learning algorithms (support vector machines, k-nearest neighbors), deep learning architectures (LSTM, CNN), and hybrid statistical–ML frameworks. Most studies focus on dengue, reflecting both its global burden and data availability. Forecasting targets range from incidence prediction and outbreak classification to temporal trend estimation across varying spatial (city to national) and temporal (weekly to annual) scales, highlighting substantial methodological diversity.
Comparative Predictive Performance Across Model Families
Comparative evidence indicates that tree-ensemble models consistently achieve strong predictive performance, particularly in short-term classification tasks, often exceeding accuracy thresholds of 0.85. Their robustness to nonlinearity, variable interactions, and noisy data likely explains this consistency. In contrast, classical ML and deep learning models demonstrate wider performance variability, with outcomes highly dependent on data volume, feature engineering, and tuning strategies. For regression-based forecasts, predictive errors tend to escalate as temporal horizons lengthen and spatial aggregation increases, underscoring inherent challenges in long-range, large-scale disease prediction.
Impact of Spatial and Temporal Scale on Forecast Reliability
Spatial and temporal resolution emerges as a critical determinant of forecasting reliability. Fine-scale, short-term models—such as weekly city-level predictions—generally yield lower absolute errors and more stable performance, making them better suited for operational decision-making. Conversely, national-level or long-horizon forecasts often suffer from inflated errors and instability, reflecting compounded uncertainties in climate drivers, human behavior, and reporting practices. These findings suggest that AI/ML models are currently most effective when tailored to localized, near-term public health questions.
Methodological Quality, Bias, and Validation Gaps
Assessment using PROBAST reveals that a majority of published studies carry a high risk of bias, frequently due to limited sample sizes, inadequate handling of missing data, lack of transparent reporting, and insufficient external validation. Only a minority of models undergo rigorous testing beyond their development datasets, raising concerns about generalizability and real-world applicability. This methodological fragility limits confidence in reported performance metrics and highlights the need for standardized reporting, open data practices, and independent validation across diverse epidemiological settings.
Operational Readiness and Future Research Priorities
Despite encouraging performance in controlled research settings, most AI/ML forecasting models for mosquito-borne viral diseases remain far from routine operational deployment. Bridging this gap requires three key research priorities: standardized performance reporting to enable meaningful comparisons, robust external and prospective validation to ensure generalizability, and context-specific calibration aligned with local surveillance systems and decision-making needs. Advancing these priorities will be essential for translating AI/ML innovations into reliable early-warning tools that can meaningfully strengthen global responses to mosquito-borne viral threats.
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#MosquitoBorneDiseases, #ArtificialIntelligence, #MachineLearning, #DiseaseForecasting, #DengueResearch, #PublicHealthResearch, #Epidemiology, #AIinHealthcare, #PredictiveModeling, #GlobalHealth, #Arboviruses, #EarlyWarningSystems, #HealthDataScience, #ClimateAndHealth, #VectorBorneDiseases, #ResearchReview, #PRISMA, #PROBAST, #OutbreakPrediction, #HealthSystems,
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