About

Elaine O. Nsoesie is an Associate Professor in the Department of Global Health at the Boston University School of Public Health. She is an internationally recognized data scientist and a leading voice on the use of data and technology to advance health equity. Her expertise includes applying data science methods such as machine learning and artificial intelligence, utilizing data from non-traditional public health sources like mobile phones, satellites, and social media to address major global health challenges. She approaches health equity from multiple angles, including increasing representation of underrepresented communities in data science, addressing bias in health data and algorithms, and using data and policy to promote racial equity. Nsoesie has contributed significantly to the field through her leadership roles, including serving as a Data Science Faculty Fellow, a founding faculty member of the Boston University Faculty of Computing and Data Sciences, and a program lead and senior advisor to the NIH AIM-AHEAD Program. She has led projects such as the Racial Data Tracker at the Boston University Center for Antiracist Research and co-edited the book 'Urban Health in Africa.' Her work has involved collaborations with local health departments, international organizations like UNICEF and UNDP, and serving as a Data & Innovation Fellow in Sierra Leone. She has published extensively, received numerous awards, and is known for effectively communicating complex scientific information to diverse audiences.

Research topics

• Computer Security
• Sociology
• Computer Science
• Artificial Intelligence
• Data Mining
• Medicine
• Machine Learning
• Nursing
• Socioeconomics
• Economics
• Demographic economics
• Environmental health
• Data science
• Geography

Selected publications

• Correction to: AI-Y: An AI Checklist for Population Ethics Across the Global Context

  Current Epidemiology Reports · 2026-03-26

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Beyond Content Exposure: Systemic Factors Driving Moderators' Mental Health Crisis in Africa

  arXiv (Cornell University) · 2026-03-03

  preprintOpen access

  Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators' severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators' mental health.

  PublisherDOI

• Beyond Content Exposure: Systemic Factors Driving Moderators' Mental Health Crisis in Africa

  2026-04-13 · 1 citations

  articleOpen access

  Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long-term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators’ severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators’ mental health.

  PublisherDOI

• Association Between Racial Equity Plans and Political and Sociodemographic Factors in US Cities

  Journal of Racial and Ethnic Health Disparities · 2025-07-21

  articleSenior author
  PublisherDOI

• Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit

  Minds and Machines · 2025-07-02 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Scoping review of artificial intelligence via mobile technology and social media for health in Africa

  Nature Communications · 2025-12-20

  articleOpen accessSenior author

  The combination of mobile technologies and social media with Artificial Intelligence (AI) opens new opportunities for multi-modal data generation, analysis, and inference for various health applications. To investigate how these tools are being used for health applications in Africa, we conduct a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. We screen 469 articles and synthesize 116. We include 29 studies documenting the use of a broad range of advanced and straightforward machine-learning techniques to study infectious and chronic diseases such as COVID-19 (4 studies, 13.8%), malaria (5, 17.2%), and cervical cancer (2, 6.9%). Countries with high internet and mobile phone penetration have higher representation. Based on identified gaps, we make research and policy recommendations to enhance the contribution of these tools in advancing health in Africa. These include investing in studies on chronic diseases and implementing frameworks to address geographic inequity. There are many opportunities to combine mobile technologies and social media with Artificial Intelligence to improve health in Africa. Here, the authors conduct a scoping review on such tools and make recommendations to enhance their contribution in advancing health in Africa.

  PublisherOA PDFDOI

• Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit

  SSRN Electronic Journal · 2025-01-01 · 3 citations

  preprintOpen access
  PublisherDOI

• The overlapping global distribution of dengue, chikungunya, Zika and yellow fever

  Nature Communications · 2025-04-10 · 97 citations

  articleOpen access

  Arboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats.

  PublisherOA PDFDOI

• The promise and pitfalls of generative AI

  Nature Reviews Psychology · 2025-01-15 · 10 citations

  article
  PublisherDOI

• AI-Y: An AI Checklist for Population Ethics Across the Global Context

  Current Epidemiology Reports · 2025-07-09 · 4 citations

  reviewOpen accessSenior author

  Purpose of Review: , a structured ethical framework created to evaluate the development and deployment of artificial intelligence (AI) technologies in public health. The review addresses key questions: How can AI be ethically assessed across global healthcare contexts and what principles are needed to ensure contextually appropriate AI use in population health. Recent Findings: Recent research highlights a significant disconnect between AI development and ethical implementation, especially in low-resource settings. Studies reveal issues such as homogeneity in the training data, and limited accessibility. Through six global case studies-spanning dementia care in Sweden, environmental forecasting in Europe, suicide prevention in Native American communities, schizophrenia care in India and the U.S., and cervical cancer and tuberculosis diagnosis in Low- and Middle-Income Countries-researchers demonstrate AI's promise in enhancing preparedness diagnosis, screening, and care delivery while also underscoring ethical gaps in accountability, and governance. Summary: Our examination using the AI-Y Checklist found that ethical blind spots are widespread in the development and deployment of AI tools for population health-particularly in areas of model generalizability, accountability, and transparency of AI decision-making. Although AI demonstrates strong potential to enhance disease detection, resource allocation, and preventive care across diverse global settings, most systems evaluated in our six case studies did not meet key ethical criteria such as access, and localized validation and development. The major takeaway is that technical excellence alone is insufficient; ethical alignment is critical to the responsible implementation of AI in public health. The AI-Y Checklist provides a scalable framework to identify risks, guide ethical decision-making, and foster global accountability. For future research, this framework enables standardized evaluation of AI systems, encourages community co-design practices, and supports the creation of policy and governance structures that ensure AI technologies advance health ethics.

  PublisherOA PDFDOI

Recent grants

• A Framework for Integrating Multiple Data Sources for Modeling and Forecasting of Infectious Diseases

  NIH · $322k · 2014–2018

• An Approach for Estimating Foodborne Illnesses and Assessing Risk Factors

  NIH · $1.2M · 2015–2019

Frequent coauthors

• John S. Brownstein

  Boston Children's Hospital

  195 shared

• Simon I Hay

  62 shared

• Dan J. Stein

  South African Medical Research Council

  33 shared

• David M. Pigott

  31 shared

• Jared B. Hawkins

  29 shared

• Alize J Ferrari

  28 shared

• Sumiko R. Mekaru

  Boston University

  28 shared

• Nina Cesare

  Boston University

  26 shared

Awards & honors

• NIH Director's Award
• Mozilla Rise25 Award
• Boston University School of Public Health Excellence in Publ…
• Boston Congress of Public Health’s Health Innovators to Watc…