A new Bayesian multiscale framework with cut inference jointly models heterogeneous viral load trajectories and household transmission, recovering parameters without bias on simulated data when viral sampling is frequent.
Epidemics 38, 100547
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Unbiased gradient estimators are derived for sensitivity of infections to vaccination proportion v and contact rate β in stochastic epidemic models with parameter uncertainty, revealing lower sensitivity and more conservative optimal policies than deterministic approximations.
A review of data sources, uncertainty incorporation methods, and open challenges in constructing contact matrices for infectious disease epidemiology.
citing papers explorer
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Bayesian inference for disease transmission models informed by viral dynamics
A new Bayesian multiscale framework with cut inference jointly models heterogeneous viral load trajectories and household transmission, recovering parameters without bias on simulated data when viral sampling is frequent.
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Sensitivity Analysis and Optimization of Stochastic Epidemic Models under Parameter Uncertainty
Unbiased gradient estimators are derived for sensitivity of infections to vaccination proportion v and contact rate β in stochastic epidemic models with parameter uncertainty, revealing lower sensitivity and more conservative optimal policies than deterministic approximations.
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Constructing Contact and Connectivity Matrices for Infectious Disease Modelling
A review of data sources, uncertainty incorporation methods, and open challenges in constructing contact matrices for infectious disease epidemiology.