Pages that link to "Item:Q2283902"
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The following pages link to Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model (Q2283902):
Displaying 19 items.
- Studying the recovery procedure for the time-dependent transmission rate(s) in epidemic models (Q365698) (← links)
- Parameter identification in epidemic models (Q630993) (← links)
- Reconstruction of disease transmission rates: applications to measles, dengue, and influenza (Q738642) (← links)
- Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (Q1647581) (← links)
- Randomized machine learning of nonlinear models with application to forecasting the development of an epidemic process (Q1982849) (← links)
- COVID-19 pandemic and chaos theory (Q1998297) (← links)
- On stable parameter estimation and short-term forecasting with quantified uncertainty with application to COVID-19 transmission (Q2101114) (← links)
- Nonparametric comparison of epidemic time trends: the case of COVID-19 (Q2106394) (← links)
- Iteratively regularized Gauss-Newton type methods for approximating quasi-solutions of irregular nonlinear operator equations in Hilbert space with an application to COVID-19 epidemic dynamics (Q2152711) (← links)
- Modeling the effect of the vaccination campaign on the COVID-19 pandemic (Q2170320) (← links)
- Parameter identification for a stochastic \textit{SEIRS} epidemic model: case study influenza (Q2313964) (← links)
- Estimation and outbreak detection with interval observers for uncertain discrete-time SEIR epidemic models (Q3386567) (← links)
- Exact Forecasting for COVID-19 Data: Case Study for Turkey (Q5072081) (← links)
- Introduction to the special issue on Data Science for COVID-19 (Q5102527) (← links)
- On iteratively regularized predictor–corrector algorithm for parameter identification <sup>*</sup> (Q5148411) (← links)
- A Review of Multi‐Compartment Infectious Disease Models (Q6064367) (← links)
- SPADE4: sparsity and delay embedding based forecasting of epidemics (Q6168035) (← links)
- Real-time mechanistic Bayesian forecasts of COVID-19 mortality (Q6179088) (← links)
- Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission (Q6671210) (← links)
