@article{Falisse_J-2021_64500,
title = {Visualising policy responses during health emergencies. Learning from the COVID-19 policy trackers},
author = {Falisse, J. and McAteer, B.},
month = {dec},
year = {2021},
abstract = {Data visualisations are intimately connected to the emergence of public health as a discipline and policy area. Besides the mapping of cases and deaths, the COVID-19 pandemic has seen an explosion of attempts to track policy responses. They have come from actors sometimes initially unfamiliar with public and global health. In this paper, we analyse seven of the most successful tracker initiatives that have sought to map governments’ reactions to COVID-19 and reflect on our own. When not led by international organisations, the trackers primarily rely on networks of volunteer country expert contributors (who need to be incentivised in the medium term). The vertical crowdsourcing approach means that, despite good intentions, contributors have a relatively limited agency in shaping the trackers. Maps and timelines comparing countries are the most popular visualisations; they suggest that (some) policy solutions can be found abroad and rely on policy taxonomies established by the trackers’ core teams. We contend that such taxonomies, which compete with each other, constitute attempts to frame the complex issue of which policies matter in responding to COVID-19. All the projects are large and complex and often without a well-defined intended audience. We hypothesise that the popularity (in terms of backlinks) of the most successful tracker arises from the fact that it sums up COVID-19 policies in one easily visualisable indicator. We suggest that the trackers are a more helpful emergency policy tool when they provide contextual information, keep policy details or refer to them (rather than only reduce them to categories), and suggest ways to link different elements—including the relationship between health or societal outcomes and policies.},
pages = {35-51},
volume = {28},
issue = {1},
journal = {Convergence },
publisher = {SAGE Publications},
url = {https://doi.org/10.1177/13548565211048972},
}
