Quantifying Policy Responses to a Global Emergency: Insights from the COVID-19 Pandemic

Public policy must confront emergencies that evolve in real time and in uncertain directions, yet little is known about the nature of policy response. Here we take the coronavirus pandemic as a global and extraordinarily consequential case, and study the global policy response by analyzing a novel dataset recording policy documents published by government agencies, think tanks, and intergovernmental organizations (IGOs) across 114 countries (37,725 policy documents from Jan 2nd through May 26th 2020). Our analyses reveal four primary findings. (1) Global policy attention to COVID-19 follows a remarkably similar trajectory as the total confirmed cases of COVID-19, yet with evolving policy focus from public health to broader social issues. (2) The COVID-19 policy frontier disproportionately draws on the latest, peer-reviewed, and high-impact scientific insights. Moreover, policy documents that cite science appear especially impactful within the policy domain. (3) The global policy frontier is primarily interconnected through IGOs, such as the WHO, which produce policy documents that are central to the COVID19 policy network and draw especially strongly on scientific literature. Removing IGOs' contributions fundamentally alters the global policy landscape, with the policy citation network among government agencies increasingly fragmented into many isolated clusters. (4) Countries exhibit highly heterogeneous policy attention to COVID-19. Most strikingly, a country's early policy attention to COVID-19 shows a surprising degree of predictability for the country's subsequent deaths. Overall, these results uncover fundamental patterns of policy interactions and, given the consequential nature of emergent threats and the paucity of quantitative approaches to understand them, open up novel dimensions for assessing and effectively coordinating global and local responses to COVID-19 and beyond.

[1]  S. Bhatt,et al.  Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe , 2020, Nature.

[2]  Albert-Laszló Barabási,et al.  Bursts : the hidden patterns behind everything we do, from your e-mail to bloody crusades , 2011 .

[3]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[4]  T. Hollingsworth,et al.  How will country-based mitigation measures influence the course of the COVID-19 epidemic? , 2020, The Lancet.

[5]  Yuval Shavitt,et al.  A model of Internet topology using k-shell decomposition , 2007, Proceedings of the National Academy of Sciences.

[6]  J. Fowler,et al.  Rapid assessment of disaster damage using social media activity , 2016, Science Advances.

[7]  Nassim Nicholas Taleb,et al.  The Black Swan: The Impact of the Highly Improbable , 2007 .

[8]  R. Evans European Centre for Disease Prevention and Control. , 2014, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[9]  Albert-László Barabási,et al.  Collective Response of Human Populations to Large-Scale Emergencies , 2011, PloS one.

[10]  Paramveer S. Dhillon,et al.  Interdependence and the cost of uncoordinated responses to COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[11]  E. Gibney Whose coronavirus strategy worked best? Scientists hunt most effective policies , 2020, Nature.

[12]  Jianmin Jia,et al.  Population flow drives spatio-temporal distribution of COVID-19 in China , 2020, Nature.

[13]  L. Gostin,et al.  Policy opportunities to enhance sharing for pandemic research , 2020, Science.

[14]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[15]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[16]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[17]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[18]  Luna Yue Huang,et al.  The effect of large-scale anti-contagion policies on the COVID-19 pandemic , 2020, Nature.

[19]  Yang Wang,et al.  Quantifying the dynamics of failure across science, startups and security , 2019, Nature.

[20]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[21]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[22]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[23]  Stacy Konkiel,et al.  Dimensions: Bringing down barriers between scientometricians and data , 2020, Quantitative Science Studies.

[24]  A. Barabasi,et al.  Percolation in directed scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.