Coronavirus: Using AI to Understand Policy Effects on the Economically Marginalized during Pandemics
A team of 28 AI experts and data scientists collaborated to gauge the impact of pandemic policy implemented post-COVID-19 on vulnerable populations to find correlations and encourage data-driven policymaking to lessen the adversity for the most vulnerable populations around the world.
Conducting data-driven impact-analyses on how various pandemic policies affect the well-being of vulnerable populations.
An important step of the project was to define “vulnerability” with respect to the particular context. The project focused on the factors of employment and wage loss, access to health, and domestic violence. To identify the vulnerable population for each of these categories, the team looked to the Inequality-adjusted Human Development Index, considered populations above 65 years of age, and women.
The team looked at 17 types of policies from the Oxford COVID-19 Pandemics Government Response Tracker, across the categories of containment, economic response, and health systems. The policies explored included closing of public transportation, stay at home requirements, income support, COVID-19 testing policy, and emergency investment in healthcare.
To analyze the effects of these policies, three key aspects were considered:
Time of policy enactment: comparing the time of policy enactment with the effect on a target variable
Stringency metric: the degree of intensity of the policies enacted
Google Mobility Dataset: quantifies the movement of people in places (e.g. grocery stores vs. parks)
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