The COVID-19 pandemic has had disproportionately negative impacts on socially disadvantaged and underserved populations around the world. Inequality and the related social determinants that impact certain groups are directly related to the adverse health outcomes of vulnerable populations during the pandemic. People in disadvantaged communities are generally more prone to occupational exposure to the virus and tend to have limited access to health care and higher rates of comorbidities. Outcomes related to widespread school closures are also of particular concern for underserved communities. Additionally, these populations are more susceptible to the negative economic outcomes of the pandemic. There is an urgent need for research and policy solutions regarding the impact of the COVID-19, with particular attention to the needs of disadvantaged and vulnerable populations, a foundation for which is offered in this discussion.
Big data is increasingly the cornerstone on which policy making is based. However, with potential benefits and applications come challenges and dilemmas. In this set of symposium articles, authors examine the promise and problems of big data, exploring associated prospects, risks, parameters, and payoffs from a variety of perspectives. The articles address myriad challenges in the handling of big data sets, such as collection, validation, integrity, and security; ontological issues attending data analytics and conceptual transformations; the foundations of big data collection for social science research; the gap between the acquisition of data and its use to advance discovery and innovation; the costs and benefits of using big data in decision making and analysis; and, finally, related problems of privacy, security, and ethics. Issues such as these will continue to arise with increased use of big data as fundamental to policy making and governance in today's growing information society.
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