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Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic
- Source :
- PLoS Computational Biology, Vol 17, Iss 7, p e1009210 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.<br />Author summary Interventions meant to control infectious diseases are often determined and judged using surveillance data on the number of new cases of disease. In many diseases, there are substantial delays between the time when an individual is infected or shows symptoms and when the case is actually reported to a public health authority, such as the CDC. This reported data often collects information on symptom onset dates for some individuals. In this paper, we describe a method that imputes missing onset dates for all individuals and recreates the history of the disease progression in a population according to symptom onset dates, which are the best observable proxy available for infection dates. Our method also estimates the instantaneous reproduction number and is robust to many deviations from the assumptions of the model. We show, using a COVID-19 dataset from Massachusetts that our method accurately follows the implementation of control measures in the state.
- Subjects :
- 0301 basic medicine
Viral Diseases
Distribution Curves
Databases, Factual
Nowcasting
Epidemiology
Computer science
computer.software_genre
Infographics
Line list
Bayes' theorem
0302 clinical medicine
Medical Conditions
Statistics
Pandemic
Medicine and Health Sciences
Preprocessor
030212 general & internal medicine
Imputation (statistics)
Biology (General)
Virus Testing
Data Management
Flow Rate
Ecology
Physics
Classical Mechanics
Infectious Diseases
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Data mining
Graphs
Algorithms
Research Article
Statistical Distributions
Computer and Information Sciences
Coronavirus disease 2019 (COVID-19)
QH301-705.5
Bayesian probability
Fluid Mechanics
Continuum Mechanics
Infectious Disease Epidemiology
Back calculation
03 medical and health sciences
Cellular and Molecular Neuroscience
Diagnostic Medicine
Genetics
Humans
Computer Simulation
Molecular Biology
Pandemics
Ecology, Evolution, Behavior and Systematics
Estimation
Models, Statistical
SARS-CoV-2
Data Visualization
COVID-19
Computational Biology
Bayes Theorem
Covid 19
Fluid Dynamics
Probability Theory
Missing data
030104 developmental biology
Table (database)
computer
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....fb2ca8f83e7796a65921ca3d90883ea4