SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.
The mathematics of infectious diseases.SIAM review, 42(4):599–653, 2000
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Simulations on a geo-referenced synthetic urban network show age-structured contacts produce faster and more pervasive epidemics while distance decay has negligible effects, with preliminary evidence of hierarchical spatial diffusion.
Simulation study and Ethiopian cohort data show that particle MCMC and conditional normalizing flows both deliver accurate parameter estimates and forecasts for stochastic compartmental epidemic models with intractable likelihoods.
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SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.
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Epidemics in a Synthetic Urban Population with Multiple Levels of Mixing
Simulations on a geo-referenced synthetic urban network show age-structured contacts produce faster and more pervasive epidemics while distance decay has negligible effects, with preliminary evidence of hierarchical spatial diffusion.
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models in Epidemiological Research
Simulation study and Ethiopian cohort data show that particle MCMC and conditional normalizing flows both deliver accurate parameter estimates and forecasts for stochastic compartmental epidemic models with intractable likelihoods.