Replication Data for: How to Stop Contagion: Applying Network Science to Evaluate the Effectiveness of Covid-19 Vaccine Distribution Plans
<|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:'President Trump's haphazard decision to delegate Covid-19 vaccine distribution to US states set up conditions for evaluating state-level vaccine prioritization policies using a quasi-experimental design. Despite agreement on the goal, state-formulated vaccine distribution plans diverged beyond initial priority groups: some prioritized based on mortality risks only (i.e., age), while others also included several high-exposure risk groups. After establishing that this divergence was driven by stochastic rather than systematic factors, I leverage it as an identification strategy to test a key insight from network theory: reducing contagion requires disabling the transmission potential of the most connected actors. Based on this, I argue that early prioritization of high-exposure risk groups, especially public-facing essential workers, led t' . 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" : ["causal inference","Covid-19 vaccine","network analysis","network contagion","vaccine prioritization"]} <|eot_id|>
 Here is the Description of a dataset. Description:'President Trump's haphazard decision to delegate Covid-19 vaccine distribution to US states set up conditions for evaluating state-level vaccine prioritization policies using a quasi-experimental design. Despite agreement on the goal, state-formulated vaccine distribution plans diverged beyond initial priority groups: some prioritized based on mortality risks only (i.e., age), while others also included several high-exposure risk groups. After establishing that this divergence was driven by stochastic rather than systematic factors, I leverage it as an identification strategy to test a key insight from network theory: reducing contagion requires disabling the transmission potential of the most connected actors. Based on this, I argue that early prioritization of high-exposure risk groups, especially public-facing essential workers, led t' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
President Trump's haphazard decision to delegate Covid-19 vaccine distribution to US states set up conditions for evaluating state-level vaccine prioritization policies using a quasi-experimental design. Despite agreement on the goal, state-formulated vaccine distribution plans diverged beyond initial priority groups: some prioritized based on mortality risks only (i.e., age), while others also included several high-exposure risk groups. After establishing that this divergence was driven by stochastic rather than systematic factors, I leverage it as an identification strategy to test a key insight from network theory: reducing contagion requires disabling the transmission potential of the most connected actors. Based on this, I argue that early prioritization of high-exposure risk groups, especially public-facing essential workers, led t
causal inference; Covid-19 vaccine; network analysis; network contagion; vaccine prioritization