Pages that link to "Item:Q5881941"
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The following pages link to Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data (Q5881941):
Displaying 12 items.
- A study of disproportionately affected populations by race/ethnicity during the SARS-CoV-2 pandemic using multi-population SEIR modeling and ensemble data assimilation (Q2072653) (← links)
- Regressive class modelling for predicting trajectories of COVID-19 fatalities using statistical and machine learning models (Q2089358) (← links)
- Being a public health statistician during a global pandemic (Q2143954) (← links)
- Lessons learned from the COVID-19 pandemic: a statistician's reflection (Q2143955) (← links)
- Regression model for the reported infected during emerging pandemics under the stochastic SEIR (Q2688143) (← links)
- Statistical Models for COVID-19 Incidence, Cumulative Prevalence, and <i>R</i> t (Q5881942) (← links)
- Discussion of “Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data” (Q5881944) (← links)
- Extended Bayesian endemic–epidemic models to incorporate mobility data into COVID‐19 forecasting (Q6059392) (← links)
- Estimating COVID-19 vaccine protection rates via dynamic epidemiological models -- a study of 10 countries (Q6138644) (← links)
- A modified SEIR model with a jump in the transmission parameter applied to COVID-19 data on Wuhan (Q6544008) (← links)
- Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model (Q6589238) (← links)
- Prevalence estimation methods for time-dependent antibody kinetics of infected and vaccinated individuals: a Markov chain approach (Q6661680) (← links)
