Spatio-temporal forecasting of dengue in the Americas -A data set

This dataset accompanies the systematic review and meta-analysis entitled “Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis.” It provides a comprehensive synthesis of dengue modeling efforts across the Americas from 2016 to 2025, integrating mechanistic, statistical, machine learning, and phylogenetic approaches.

The files included present structured evidence on dengue dynamics, model structures, and analytical frameworks:

Detailed Overview of Sub-model Structures: Comparative representation of vector–host and within-host modeling frameworks.

Table 1. Data Extraction Matrix: Summary of key variables, parameters, and outcomes derived from the 19 included studies.

Table 2. Comparative Summary: Model calibration, validation techniques, and analytical frameworks applied across studies.

Table 3. Model Characteristics: Overview of dengue transmission model types, associated datasets, and contextual applications.

Figure 1. PRISMA Flow Diagram: Study identification and selection process for the review.

Figure 2. Geographical and Temporal Distribution: Mapping of dengue modeling studies across the Americas (2016–2025).

Figure 3. Heatmap of Risk of Bias: Horizontal representation of quality assessment across the 19 studies.

Figure 4. Forest Plot of Model Complexity: Comparative evaluation of mechanistic, statistical, ML/niche, and phylogenetic approaches.

Figure 5. Compartmental Variables and Interventions: Forest plot synthesis of transmission model compartments and intervention strategies.

Figure 6. Temperature Effects: Quantitative impact of temperature on dengue transmission risk in the Americas.

Figure 7. Model Performance and Predictive Accuracy: ROC curve analysis across different modeling frameworks.

Figure 8. General Compartmental Model: Conceptual diagram of dengue transmission in host–vector populations.

Together, these materials support transparency, reproducibility, and meta-analytical comparison of dengue modeling strategies, offering a valuable reference for researchers, public health practitioners, and policymakers addressing vector-borne disease forecasting in the Americas.