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Activity Number:
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503
- Causal Inference for Spatiotemporal Data
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Type:
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Invited
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Date/Time:
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Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #319194
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Title:
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Spatio-Temporal Bayesian Nowcasting for Detecting the Effects of Nonpharmaceutical Interventions
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Author(s):
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Andrew Booth Lawson*
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Companies:
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Medical University of South Carolina
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Keywords:
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Bayesian;
spatio-temporal;
nowcasting;
counterfactual;
NPI;
Covid-19
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Abstract:
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The spatio-temporal course of an epidemic (such as Covis-19) can be significantly affected by non-pharmaceutical interventions (NPIs), such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious disease (STIF) (such as Covid-19). In causal inference it is classically of interest to investigate counterfactuals. In the context of STIF it is possible to use nowcasting to assess the possible counterfactual realization of disease in incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models will be discussed and the importance of the ST component in nowcasting will be assessed. The real example of lockdowns for Covid-19 in a state of the US during 2020 and 2021 will be provided.
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Authors who are presenting talks have a * after their name.
