Back

COVID AMP: An Open Access Database of COVID-19 Response Policies

Katz, R.; Toole, K.; Robertson, H.; Case, A.; Kerr, J.; Robinson-Marshall, S.; Schermerhorn, J.; Orsborn, S.; Van Maele, M.; Zimmerman, R.; Stevens, T.; COVID AMP Coding Team, ; Phelan, A.; Carlson, C. J.; Graeden, E.

2023-05-02 health policy
10.1101/2023.05.01.23289163 medRxiv
Show abstract

As the COVID-19 pandemic unfolded in the spring of 2020, governments around the world began to implement policies to mitigate and manage the outbreak. Significant research efforts were deployed to track and analyse these policies in real-time to better inform the response. While much of the policy analysis focused narrowly on social distancing measures designed to slow the spread of disease, here, we present a dataset focused on capturing the breadth of policy types implemented by jurisdictions globally across the whole-of-government. COVID Analysis and Mapping of Policies (COVID AMP) includes nearly 50,000 policy measures from 150 countries, 124 intermediate areas, and 235 local areas between January 2020 and June 2022. With up to 40 structured and unstructured characteristics encoded per policy, as well as the original source and policy text, this dataset provides a uniquely broad capture of the governance strategies for pandemic response, serving as a critical data source for future work in legal epidemiology and political science.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.