Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission

Epidemiology (92D30) Artificial neural networks and deep learning (68T07) Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76) Stochastic difference equations (39A50)

Cites Work

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Studying the recovery procedure for the time-dependent transmission rate(s) in epidemic models
Seasonality in epidemic models: a literature review
Stochastic modelling of air pollution impacts on respiratory infection risk
Synchronization for commensurate Riemann-Liouville fractional-order memristor-based neural networks with unknown parameters
The universal approximation property. Characterization, construction, representation, and existence
A stochastic SIS model driven by random diffusion of air pollutants
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model
Optimal control by deep learning techniques and its applications on epidemic models
Extracting the time-dependent transmission rate from infection data via solution of an inverse ODE problem
Some Bayesian Inferences for a Changing Linear Model
SIRS Epidemic Model and Simulations Using Different Types of Seasonal Contact Rate
Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs
A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
Simple mathematical models with very complicated dynamics
Structural inference of networked dynamical systems with universal differential equations
Estimating time-varying reproduction number by deep learning techniques

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