Pages that link to "Item:Q3145586"
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The following pages link to Modeling seasonality in space-time infectious disease surveillance data (Q3145586):
Displaying 14 items.
- Power-law models for infectious disease spread (Q58327) (← links)
- Calibration tests for count data (Q2342870) (← links)
- Bayesian spatio-temporal random coefficient time series (BaST-RCTS) model of infectious disease (Q2343727) (← links)
- A statistical framework for the analysis of multivariate infectious disease surveillance counts (Q3413102) (← links)
- A two-component model for counts of infectious diseases (Q3434155) (← links)
- An endemic–epidemic beta model for time series of infectious disease proportions (Q5044690) (← links)
- Power law approximations of movement network data for modeling infectious disease spread (Q5420222) (← links)
- An online spatiotemporal prediction model for dengue fever epidemic in <scp>K</scp>aohsiung (<scp>T</scp>aiwan) (Q5420230) (← links)
- Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error (Q5884456) (← links)
- Extended Bayesian endemic–epidemic models to incorporate mobility data into COVID‐19 forecasting (Q6059392) (← links)
- A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts (Q6079250) (← links)
- Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to nonrandom missingness (Q6138597) (← links)
- Forecasting overdispersed INAR(1) count time series with negative binomial marginal (Q6172610) (← links)
- Gaussian random fields on the product of spheres: theory and applications (Q6200912) (← links)
