Neglected Tropical Diseases in the Americas: A Seasonally-Forced Compartmental Framework Calibrated and Externally Validated Against Real Publicly Archived Dengue Surveillance Data

Background. Neglected tropical diseases impose a disproportionate burden on impoverished communities throughout the Americas. Prior compartmental frameworks in this space are rarely calibrated against and externally validated on real multi-country surveillance time series with transparently reported goodness of fit.Methods. A seasonally-forced extension of a susceptible exposed infectious partially-immune recovered vector-coupled SEIZR-V model was fitted to real weekly national dengue case counts for Brazil (2016 to 2019; n=209 weeks) drawn from the OpenDengue global database which itself compiles and standardises Pan American Health Organization PAHO PLISA surveillance records. Because full Hamiltonian No-U-Turn Markov chain Monte Carlo sampling (e.g. via PyMC) was not executable in the available single-core offline computational environment parameters were instead estimated by maximum a posteriori MAP optimisation with a Laplace local quadratic approximation to posterior uncertainty a documented and standard fallback when full MCMC is computationally infeasible. The fitted transmission parameters were then transferred without re-estimation to three held-out countries (Colombia Bolivia Peru; 2021 to 2022 n=104 weeks each) to assess genuine out-of-sample generalisation re-estimating only a country-specific reporting-scale parameter.Results. The calibrated model reproduced the order of magnitude and partial seasonal timing of Brazilian weekly dengue incidence (Pearson r=0.49; MAPE=145%) reflecting the real inter-annual variability of dengue epidemics that a single annual harmonic cannot fully capture. External transfer to Bolivia and Peru showed weak to moderate positive concordance (r=0.18 and 0.57 respectively) whereas transfer to Colombia failed (r=-0.46) indicating that Brazil's fitted seasonal phase does not generalise across all Latin American transmission settings. Global variance-based Sobol sensitivity analysis of the basic reproduction number identified the mosquito biting rate as the dominant driver of R0 variance (S1=0.49) consistent with prior vector-borne disease literature.Conclusions. Real-data calibration surfaces genuine disease and country-specific transferability limits that synthetic-data exercises cannot reveal and these limits not a uniformly high validation score are the primary scientific finding of this analysis and the appropriate basis for any future translational roadmap.