Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation – The CovidLP Project

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Contributors
I. Introduction
	1. Overview of the book
		1.1. Objective of the book
			1.1.1. Data-driven vs model-driven
			1.1.2. Real-time prediction
			1.1.3. Building platforms
		1.2. Outline of the book
			1.2.1. How to read this book
			1.2.2. Notation
	2. Pandemic data
		2.1. Basic definitions
		2.2. Occurrence and notification times
		2.3. Other relevant pandemic data
		2.4. Data reconstruction
II. Modelling
	3. Basic epidemiological features
		3.1. Introduction and main ideas
		3.2. Model extensions
		3.3. Properties of epidemiological models
		3.4. Are these models appropriate?
	4. Data distributions
		4.1. Introduction
		4.2. The Poisson distribution
		4.3. Overdispersion
			4.3.1. Negative Binomial: Mean-dependent overdispersion
			4.3.2. Negative Binomial: Mean-independent overdispersion
			4.3.3. Other models for the overdispersion
		4.4. Discussion
			4.4.1. Daily new cases vs cumulative cases
			4.4.2. Parameter truncation
	5. Modelling specific data features
		5.1. Introduction
		5.2. Heterogeneity across sub-regions
		5.3. Seasonality
		5.4. Multiple waves
	6. Review of Bayesian inference
		6.1. Inference
			6.1.1. Bayesian inference
			6.1.2. Prior distribution
			6.1.3. Estimation
			6.1.4. Prediction
		6.2. Operationalisation
			6.2.1. The quadrature technique
			6.2.2. Markov Chain Monte Carlo simulation methods
III. Further Modelling
	7. Modelling misreported data
		7.1. Issues with the reporting of epidemiological data
		7.2. Modelling reporting delays
			7.2.1. Weekly cases nowcast
			7.2.2. Illustration: SARI notifications in Brazil provided by InfoGripe
		7.3. Prevalence estimation from imperfect tests
			7.3.1. Preliminaries: Imperfect classifiers
			7.3.2. Prevalence from a single imperfect test
			7.3.3. Re-testing positives
			7.3.4. Estimating underreporting from prevalence surveys
			7.3.5. Illustration: COVID-19 prevalence in Rio de Janeiro
			7.3.6. Model extensions
			7.3.7. Open problems
	8. Hierarchical modelling
		8.1. Introduction
		8.2. Regression
		8.3. Dynamic models
		8.4. Hierarchical models
			8.4.1. Unstructured component
		8.5. Spatial models
			8.5.1. Spatial component
IV. Implementation
	9. Data extraction/ETL
		9.1. Data sources
		9.2. Data preparation
		9.3. Additional reading
	10. Automating modelling and inference
		10.1. Introduction
		10.2. Implementing country models
		10.3. Implementing Brazilian models
	11. Building an interactive app with Shiny
		11.1. Getting started
		11.2. Shiny basics
		11.3. Beyond Shiny basics
		11.4. Code organisation
		11.5. Design of the user interface
			11.5.1. The CovidLP app structure
		11.6. Creating interactive plots
			11.6.1. plotly basics
			11.6.2. The CovidLP plots
		11.7. Deploy and publish
		11.8. Monitoring usage
V. Monitoring
	12. Daily evaluation of the updated data
		12.1. The importance of monitoring the data
		12.2. Atypical observations
		12.3. Detecting multiple waves
		12.4. Seasonality
	13. Investigating inference results
		13.1. Monitoring inference issues
		13.2. Monitoring and learning
		13.3. Evaluation metrics
		13.4. Practical situations
			13.4.1. Overall comparison
			13.4.2. Seasonality
			13.4.3. Multiple waves
	14. Comparing predictions
		14.1. The structure of the proposed comparison
		14.2. Analysis for Brazilian states
		14.3. Analysis for countries
		14.4. Improvements from a two-waves modelling
VI. Software
	15. PandemicLP package: Basic functionalities
		15.1. Introduction
		15.2. Installation
			15.2.1. Installing from the GitHub repository
		15.3. Functionalities
			15.3.1. COVID-19 data extraction and loading: load_covid
			15.3.2. Visualising the data: plot.pandemicData
			15.3.3. Model fitting: pandemic_model
			15.3.4. Predictive distribution: posterior_predict.pandemicEstimated
			15.3.5. Calculating relevant statistics: pandemic_stats
			15.3.6. Plotting the results: plot.pandemicPredicted
		15.4. Modelling with the PandemicLP
			15.4.1. Generalised logistic model
			15.4.2. Generalised logistic model with seasonal effect
			15.4.3. Two-wave model
		15.5. Sum of regions
		15.6. Working with user data
	16. Advanced settings: The pandemic_model function
		16.1. Introduction
		16.2. Solving sampling issues
		16.3. Sampling diagnostics
		16.4. Truncation of the total number of cases
VII. Conclusion
	17. Future directions
		17.1. Introduction
		17.2. Modelling
			17.2.1. Overdispersion
			17.2.2. Relation between cases and deaths
			17.2.3. Automated identification of wave changes
		17.3. Implementation
		17.4. Monitoring
		17.5. Software
Index