Dengue Forecasting Models: A Systematic Review Incorporating a Network Meta-Analysis and Comparative Analysis of Methodologies.

AbstractsO_ST_ABSBackgroundC_ST_ABSAccurate dengue forecasting is vital for public health preparedness. Despite a surge in forecasting approaches, a quantitative ranking of the relative performance and practical utility of dengue forecasting is lacking. MethodsA systematic review and Network Meta-Analysis (NMA) of studies comparing dengue forecasting methods (2014-2024) was conducted. Models were categorised into five groups: Time Series, Deep Learning (DL), Machine Learning (excluding DL), Hybrid, and Ensembles. NMA was applied to the logarithm of the most common forecast error metric to rank relative performance--an "Implementability Score" quantified analyst and data requirements, and computational costs. Results59 studies were included. NMA of Root Mean Squared Error identified k-Nearest Neighbour (k-NN) models as achieving the highest predictive accuracy, followed closely by Vector Autoregression, Kalman Filtering, Generalised Linear Model and Autoregressive Neural Network (ARNN). While DL models showed high potential, they scored lowest in implementability due to poor interpretability and high data requirements. Most studies utilised meteorological covariates, with significant gaps in the use of socio-economic and entomological predictors. ConclusionsAlthough there was some trade-off between accuracy and implementability, traditional statistical models were often comparable in accuracy to machine learning approaches, with advantages in interpretability and data needs. Under-explored areas for future research include the use of ensemble models and the use of socio-economic and entomological data. RegistrationPROSPERO CRD420251016662. Author SummaryDengue is a critical global health threat affecting the worlds population. While many forecasting models exist to help officials prepare for outbreaks, there has been no standardised way to compare their performance. This leaves health experts in resource-limited areas uncertain about which tools are truly reliable or easy to use under their specific local conditions. We conducted a network meta-analysis of studies comparing dengue forecasting methods accuracy, grouping them into five categories: Machine Learning, Deep Learning, Time Series, Ensemble, and Hybrid. Beyond ranking their accuracy, we developed an "Implementability Score" to evaluate the practical feasibility of each model, accounting for technical complexity, data requirements, and software accessibility. Our analysis identified the top-performing models. Notably, traditional statistical models often performed as well as complex Deep Learning algorithms. While advanced models show potential, they are often difficult to implement or explain to decision-makers. There is no "one-size-fits-all" solution; the best model depends on capacity and data in each setting. This study provides a roadmap for public health officials to select tools that are both accurate and feasible.