MODIFIED SEIR MODEL WITH GRID AUTO-SEARCH OPTIMIZATION FOR HIGH-ACCURACY EPIDEMIC DYNAMICS FORECASTING BASED ON COVID-19

Mathematical and computational analysis of a SEIR-informed LSTM framework for epidemic dynamics with meteorological effects

An empirical evaluation of mathematical, machine learning, and hybrid modeling approaches for COVID-19 forecasting

None of the mechanistic or machine-learning approaches anticipates new epidemic waves beyond the training period, underscoring the difficulty of medium-range epidemic forecasting under genuinely held-out conditions.

Bayesian calibration of adaptive-behavior SIR models for multi-wave COVID-19 incidence in New York City

Epidemic incidence reflects both transmission dynamics and adaptive human behavior, yet these mechanisms may be difficult to distinguish from aggregate case data alone. We calibrated four susceptible--infected--recovered (SIR) specifications to weekly confirmed COVID-19 incidence in New York City from June to December...

Genetic algorithm–optimized SEIR-V: robust parameter estimation for wastewater-based infectious disease modeling across multiple regions

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, en...

Calibration of Time-Varying SEIR Models

Mechanistic epidemic calibration can be sensitive to short-lived reporting anomalies that give a few observations high leverage under least squares (LSQ). We develop direct forward-solver least absolute deviations (LAD) calibration for time-varying susceptible-exposed-infectious-removed models fitted to reporting-inter...

Testing structural interpretations beyond goodness-of-fit in a phenomenological epidemic model: A reproducibility-oriented case study

Simulation models are widely used to support counterfactual inference in settings where direct experimental validation is difficult or infeasible. However, for phenomenological models calibrated to observational time series, goodness-of-fit does not necessarily establish that the structural assumptions underlying such...