Building a Platform for Data-Driven Pandemic Prediction
portes grátis
Building a Platform for Data-Driven Pandemic Prediction
From Data Modelling to Visualisation - The CovidLP Project
Gamerman, Dani; Paiva, Thais; Mayrink, Vinicius D.; Prates, Marcos O.
Taylor & Francis Ltd
09/2021
382
Mole
Inglês
9780367709976
15 a 20 dias
553
Descrição não disponível.
I Introduction
1. Overview of the book
2. Pandemic Data
II Modelling
3. Basic Epidemiological Features
4. Data Distributions
5. Modelling Specific Data Features
6. Review of Bayesian Inference
III Further Modelling
7. Modelling Misreported Data
8. Hierarchical Modelling
IV Implementation
9. Data Extraction/ETL
10. Automating Modelling and Inference
11. Building an Interactive App with Shiny
V Monitoring
12. Daily Evaluation of the Updated Data
13. Investigating Inference Results
14. Comparing Predictions
VI Software
15. PandemicLP Package: Basic Functionalities
16. Advanced Settings: The Pandemic Model Funtion
VII Conclusion
17. Future Directions
1. Overview of the book
2. Pandemic Data
II Modelling
3. Basic Epidemiological Features
4. Data Distributions
5. Modelling Specific Data Features
6. Review of Bayesian Inference
III Further Modelling
7. Modelling Misreported Data
8. Hierarchical Modelling
IV Implementation
9. Data Extraction/ETL
10. Automating Modelling and Inference
11. Building an Interactive App with Shiny
V Monitoring
12. Daily Evaluation of the Updated Data
13. Investigating Inference Results
14. Comparing Predictions
VI Software
15. PandemicLP Package: Basic Functionalities
16. Advanced Settings: The Pandemic Model Funtion
VII Conclusion
17. Future Directions
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Generalised Logistic Model;epidemiology;Posterior Distribution;bayesian;HMC;infectious diseases;Generalised Logistic Curve;covid-19;Credible Intervals;coronavirus;GitHub Repository;Markov Chain Monte Carlo Method;Notification Date;Negative Binomial Distribution;MCMC Method;Model Fitting;MCMC;Prior Distribution;Integrated Nested Laplace Approximation;Shiny App;Negative Binomial Models;Prediction Intervals;True Count;Sari;Data Frame;Minas Gerais;Predictive Distribution;Input Argument;Negative Binomial;Online Application
I Introduction
1. Overview of the book
2. Pandemic Data
II Modelling
3. Basic Epidemiological Features
4. Data Distributions
5. Modelling Specific Data Features
6. Review of Bayesian Inference
III Further Modelling
7. Modelling Misreported Data
8. Hierarchical Modelling
IV Implementation
9. Data Extraction/ETL
10. Automating Modelling and Inference
11. Building an Interactive App with Shiny
V Monitoring
12. Daily Evaluation of the Updated Data
13. Investigating Inference Results
14. Comparing Predictions
VI Software
15. PandemicLP Package: Basic Functionalities
16. Advanced Settings: The Pandemic Model Funtion
VII Conclusion
17. Future Directions
1. Overview of the book
2. Pandemic Data
II Modelling
3. Basic Epidemiological Features
4. Data Distributions
5. Modelling Specific Data Features
6. Review of Bayesian Inference
III Further Modelling
7. Modelling Misreported Data
8. Hierarchical Modelling
IV Implementation
9. Data Extraction/ETL
10. Automating Modelling and Inference
11. Building an Interactive App with Shiny
V Monitoring
12. Daily Evaluation of the Updated Data
13. Investigating Inference Results
14. Comparing Predictions
VI Software
15. PandemicLP Package: Basic Functionalities
16. Advanced Settings: The Pandemic Model Funtion
VII Conclusion
17. Future Directions
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Generalised Logistic Model;epidemiology;Posterior Distribution;bayesian;HMC;infectious diseases;Generalised Logistic Curve;covid-19;Credible Intervals;coronavirus;GitHub Repository;Markov Chain Monte Carlo Method;Notification Date;Negative Binomial Distribution;MCMC Method;Model Fitting;MCMC;Prior Distribution;Integrated Nested Laplace Approximation;Shiny App;Negative Binomial Models;Prediction Intervals;True Count;Sari;Data Frame;Minas Gerais;Predictive Distribution;Input Argument;Negative Binomial;Online Application
