Upstream factors are important for preventing and mitigating health disparities. We bring focus to how data can be used to capture these social and environmental factors, and how to incorporate them in models that affect change.
Relevant Papers
Zhao, Y., Paul, R., Reid, S., Vieira, C. C., Wolfe, C., Zhang, Y., & Chunara, R., 2024. Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts. Social Indicators Research.
Zhang, M., Rahman, S., Mhasawade, V., & Chunara, R., 2024. Utilizing big data without domain knowledge impacts public health decision-making. Proceedings of the National Academy of Sciences of the United States of America, 121(39), e2402387121.
Chen, X., Porter, A., Abdur Rehman, N., Morris, S. K., Said, U., & Chunara, R., 2023. Area-based determinants of outreach vaccination for reaching vulnerable populations: A cross-sectional study in Pakistan. PLOS global public health, 3(9), e0001703.
Zhang, M., Arshad, H., Abbas, M., Jehanzeb, H., Tahir, I., Hassan, J., Samad, Z., & Chunara, R, 2023. Quantifying greenspace using deep learning in Karachi, Pakistan.
Duncan, D. T., Cook, S. H., Wood, E. P., Regan, S. D., Chaix, B., Tian, Y., & Chunara, R, 2023. Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men. Social science & medicine, (1982), 320, 115755.
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