Coronavirus: Understanding Policy Effects on Vulnerable Populations
A team of 28 AI experts and data scientists collaborated to gauge the impact of policies on vulnerable populations implemented during pandemics like COVID-19. The goal was to find correlations and encourage data-driven policymaking to lessen the adversity for most vulnerable populations.
The entire data analysis including a live demonstration is accessible in our demo day wrap article.
The project goal
Conducting AI enabled impact-analyses on how various pandemics 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.
Assessing policies and their effects
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)
The entire data analysis including a live demonstration is accessible in our demo day wrap article