Abstract / Summary
Background Wastewater surveillance has emerged as a cost-effective and powerful approach for monitoring infectious diseases at the community level, as demonstrated during the COVID-19 pandemic. However, translating wastewater viral signals into reliable epidemic forecasts remains challenging because measurements are noisy, environmental conditions vary over time, and disease transmission is inherently non-linear. Mechanistic models such as the Susceptible-Exposed-Infectious-Recovered-Virus (SEIR-V) framework can bridge wastewater signals with infection dynamics, but their performance depends heavily on accurate parameter estimation, and conventional least-squares optimization is prone to convergence to local optima. Methods We developed a Genetic Algorithm-optimized SEIR-V (GA-SEIR-V) model that applies evolutionary optimization for robust multi-parameter estimation. To improve biological realism, the model incorporates a temperature-dependent viral decay rate represented by a sinusoidal function fitted to regional climate data, enabling dynamic simulation of seasonal viral persistence in wastewater. The framework was calibrated and evaluated using wastewater viral load, clinical case, and temperature data from California (September 2020-November 2022), Greater Boston (September 2020-May 2021), and Switzerland (January 2022-December 2022). Results Using a unified evaluation protocol with 30 independent genetic algorithm runs, we explicitly separated in-sample calibration from out-of-sample prediction. During calibration, GA-SEIR-V reproduced the reported model fit for California and consistently outperformed a carefully re-tuned least-squares baseline on large, heterogeneous datasets, demonstrating greater robustness and more reliable parameter estimation where gradient-based optimization became trapped in local optima. In contrast, long-term prediction beyond the calibration period remained difficult for all methods, particularly across multiple epidemic waves, resulting in limited out-of-sample forecasting accuracy. Conclusion GA-SEIR-V combines evolutionary optimization with temperature-aware viral decay modeling to improve the robustness and identifiability of SEIR-V parameter estimation, particularly for complex wastewater datasets. By explicitly distinguishing calibration from prediction, the study shows that excellent in-sample fitting does not necessarily translate into reliable long-term forecasting. The proposed framework is therefore most suitable for robust parameter estimation and short-term epidemic tracking, while providing a foundation for future improvements toward more accurate long-range prediction.