A state-space SIR model with time-varying quarantine protocols
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This talk discussed a data analytic toolbox developed by Song Lab at UM, which enables public health workers to analyze and evaluate time-course infection dynamics of the novel coronavirus disease (COVID-19) using the public available data from the China CDC. This toolbox provides forecast of some key turning points. The data analytics are built upon a state-space SIR model, a hierarchical modeling framework in which the two-dimensional observed time series of daily incidences of infected and removed cases are emitted from the underlying infection system governed by the classical SIR infectious disease mechanism.  We extend the SIR model to incorporate different types of time-varying quarantine protocols, including government-level macro isolation policies and community-level micro inspection measures. Part of the output includes forecas' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["COVID-19, Coronavirus, SIR Model"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This talk discussed a data analytic toolbox developed by Song Lab at UM, which enables public health workers to analyze and evaluate time-course infection dynamics of the novel coronavirus disease (COVID-19) using the public available data from the China CDC. This toolbox provides forecast of some key turning points. The data analytics are built upon a state-space SIR model, a hierarchical modeling framework in which the two-dimensional observed time series of daily incidences of infected and removed cases are emitted from the underlying infection system governed by the classical SIR infectious disease mechanism.  We extend the SIR model to incorporate different types of time-varying quarantine protocols, including government-level macro isolation policies and community-level micro inspection measures. Part of the output includes forecas' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This talk discussed a data analytic toolbox developed by Song Lab at UM, which enables public health workers to analyze and evaluate time-course infection dynamics of the novel coronavirus disease (COVID-19) using the public available data from the China CDC. This toolbox provides forecast of some key turning points. The data analytics are built upon a state-space SIR model, a hierarchical modeling framework in which the two-dimensional observed time series of daily incidences of infected and removed cases are emitted from the underlying infection system governed by the classical SIR infectious disease mechanism.  We extend the SIR model to incorporate different types of time-varying quarantine protocols, including government-level macro isolation policies and community-level micro inspection measures. Part of the output includes forecas
COVID-19, Coronavirus, SIR Model