Data science in public health: building next generation capacity

@article{Mirin2022DataSI,
  title={Data science in public health: building next generation capacity},
  author={Nicholas Mirin and Heather Mattie and Latifa Jackson and Zainab Samad and Rumi Chunara},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.03461}
}
Rapidly evolving technology, data and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills including data literacy is particularly prominent given both the potential of novel data types and analysis methods to fill gaps in existing public health research and intervention practices, as well as the potential of such data or methods to perpetuate or augment health disparities. Through a review of public health courses and programs at… 

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References

SHOWING 1-10 OF 26 REFERENCES

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa

A roadmap for building capacity in health data science in Africa to help spur health discovery and innovation is highlighted, and a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement is proposed.

The Data Science Mentoring Fire Next Time: Innovative Strategies for Mentoring in Data Science

The mentoring strategies that were undertook at the 2019 Broadening Participation in Data Mining workshop and how those were received are reported on.

Health disparities and health equity: concepts and measurement.

  • P. Braveman
  • Medicine, Political Science
    Annual review of public health
  • 2006
This paper aims to clarify the concepts of health disparities/inequalities (used interchangeably here) and health equity, focusing on the implications of different definitions for measurement and hence for accountability.

Dissecting racial bias in an algorithm used to manage the health of populations

It is suggested that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

Why We Need Crowdsourced Data in Infectious Disease Surveillance

This work considers how crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models and challenges and methods for overcoming limitations of the data.

No Computation without Representation: Avoiding Data and Algorithm Biases through Diversity

A set of key recommendations is developed that provide concrete steps for the computing community to increased diversity: 1) building collaborations with minority serving institutions, 2) prioritizing research collaboration between the ethics in AI community and underrepresented/interdisciplinary groups, and 3) providing enhanced mentorship to trainees at research conferences.

Integrated Postsecondary Education Data System (IPEDS).

 Human Resources The Integrated Postsecondary Education Data System (IPEDS) is the National Center for Education Statistics’ (NCES) core postsecondary education data collection program, designed to

Quantifying the impact of dengue containment activities using high-resolution observational data

Both analyses suggest that activities aimed at the adult phase of the mosquito lifecycle have the highest efficacy, with fogging having the largest quantifiable effect in reducing cases immediately after deployment.