Daniel B. Neill

3.5k total citations
89 papers, 2.0k citations indexed

About

Daniel B. Neill is a scholar working on Epidemiology, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Daniel B. Neill has authored 89 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Epidemiology, 36 papers in Artificial Intelligence and 12 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Daniel B. Neill's work include Data-Driven Disease Surveillance (48 papers), Anomaly Detection Techniques and Applications (24 papers) and COVID-19 epidemiological studies (11 papers). Daniel B. Neill is often cited by papers focused on Data-Driven Disease Surveillance (48 papers), Anomaly Detection Techniques and Applications (24 papers) and COVID-19 epidemiological studies (11 papers). Daniel B. Neill collaborates with scholars based in United States, Brazil and United Kingdom. Daniel B. Neill's co-authors include Andrew Moore, Gregory F. Cooper, Feng Chen, Edward McFowland, Maheshkumar Sabhnani, Sonali Joyce, Rebecca J. Leeman‐Neill, Sufi M. Thomas, Kenny Daniel and Jeff Schneider and has published in prestigious journals such as American Psychologist, American Journal of Epidemiology and Clinical Cancer Research.

In The Last Decade

Daniel B. Neill

83 papers receiving 1.9k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel B. Neill United States 26 743 727 179 167 160 89 2.0k
Pang Wei Koh United States 15 508 0.7× 248 0.3× 83 0.5× 136 0.8× 293 1.8× 30 1.9k
Zubair Shah Qatar 24 593 0.8× 364 0.5× 197 1.1× 519 3.1× 38 0.2× 108 2.9k
Eiji Aramaki Japan 19 694 0.9× 274 0.4× 306 1.7× 278 1.7× 75 0.5× 153 1.6k
Nicholas Blumm United States 8 263 0.4× 437 0.6× 333 1.9× 207 1.2× 1.8k 11.0× 10 3.0k
Rok Sosič United States 17 401 0.5× 85 0.1× 141 0.8× 100 0.6× 168 1.1× 40 2.1k
Adrian Dobra United States 23 587 0.8× 255 0.4× 542 3.0× 97 0.6× 136 0.8× 59 2.1k
Robert Olszewski Poland 14 256 0.3× 238 0.3× 125 0.7× 86 0.5× 55 0.3× 83 937
Tyler H. McCormick United States 19 554 0.7× 204 0.3× 45 0.3× 541 3.2× 69 0.4× 64 1.8k
Arif Khan Australia 17 566 0.8× 239 0.3× 207 1.2× 61 0.4× 20 0.1× 77 2.0k
James R. Wilson United States 36 249 0.3× 271 0.4× 469 2.6× 41 0.2× 73 0.5× 215 4.1k

Countries citing papers authored by Daniel B. Neill

Since Specialization
Citations

This map shows the geographic impact of Daniel B. Neill's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel B. Neill with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel B. Neill more than expected).

Fields of papers citing papers by Daniel B. Neill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel B. Neill. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel B. Neill. The network helps show where Daniel B. Neill may publish in the future.

Co-authorship network of co-authors of Daniel B. Neill

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel B. Neill. A scholar is included among the top collaborators of Daniel B. Neill based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel B. Neill. Daniel B. Neill is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Allen, Bennett, Victoria Jent, Magdalena Cerdá, et al.. (2025). Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island. Preventive Medicine. 194. 108276–108276.
2.
Neill, Daniel B., Bennett Allen, Maxwell S. Krieger, et al.. (2025). Assessing User Engagement With an Interactive Mapping Dashboard for Overdose Prevention Informed by Predictive Modeling in Rhode Island. Journal of Public Health Management and Practice. 31(6). E330–E337.
3.
Pourang, Aunna, et al.. (2025). Chronic Retiform Purpura in the Setting of Levamisole‐Tainted Heroin Use: A Diagnostic Challenge. International Journal of Dermatology.
4.
Matthay, Ellicott C., Daniel B. Neill, Andrea R. Titus, et al.. (2025). Integrating Artificial Intelligence into Causal Research in Epidemiology. Current Epidemiology Reports. 12(1). 2 indexed citations
5.
McFowland, Edward, et al.. (2023). Provable Detection of Propagating Sampling Bias in Prediction Models. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9562–9569. 4 indexed citations
6.
Swartz, Jordan, et al.. (2023). Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis. Journal of Urban Health. 100(4). 802–810. 1 indexed citations
7.
Ibrahim, Said A., Mary E. Charlson, & Daniel B. Neill. (2020). Big Data Analytics and the Struggle for Equity in Health Care: The Promise and Perils. Health Equity. 4(1). 99–101. 27 indexed citations
8.
Flaxman, Seth, Daniel B. Neill, & Alex Smola. (2018). Correlates of homicide: new space/time interaction tests for spatiotemporal point processes. Figshare. 2 indexed citations
9.
Wilson, Andrew Gordon, Hannes Nickisch, Seth Flaxman, et al.. (2016). Scalable gaussian processes for characterizing multidimensional change surfaces. Oxford University Research Archive (ORA) (University of Oxford). 5 indexed citations
10.
Chen, Feng & Daniel B. Neill. (2015). Human Rights Event Detection from Heterogeneous Social Media Graphs. Big Data. 3(1). 34–40. 15 indexed citations
11.
Hasan, Sharique, George T. Duncan, Daniel B. Neill, & Rema Padman. (2011). Automatic detection of omissions in medication lists. Journal of the American Medical Informatics Association. 18(4). 449–458. 18 indexed citations
12.
Neill, Daniel B.. (2011). Fast Bayesian scan statistics for multivariate event detection and visualization. Statistics in Medicine. 30(5). 455–469. 15 indexed citations
13.
Dubrawski, Artur, et al.. (2009). T-cube web interface in support of real-time bio-surveillance program. 495–495. 3 indexed citations
14.
Jiang, Xia, Daniel B. Neill, & Gregory F. Cooper. (2009). A Bayesian network model for spatial event surveillance. International Journal of Approximate Reasoning. 51(2). 224–239. 12 indexed citations
15.
Neill, Daniel B.. (2009). An empirical comparison of spatial scan statistics for outbreak detection. International Journal of Health Geographics. 8(1). 20–20. 47 indexed citations
16.
Neill, Daniel B., Andrew Moore, & Gregory F. Cooper. (2005). A Bayesian Spatial Scan Statistic. Neural Information Processing Systems. 18. 1003–1010. 84 indexed citations
17.
Sabhnani, Maheshkumar, Daniel B. Neill, Andrew W. Moore, et al.. (2005). Detecting anomalous patterns in pharmacy retail data. 7 indexed citations
18.
Neill, Daniel B., Andrew Moore, Francisco Pereira, & Tom M. Mitchell. (2004). Detecting Significant Multidimensional Spatial Clusters. Neural Information Processing Systems. 17. 969–976. 37 indexed citations
19.
Neill, Daniel B. & Andrew Moore. (2003). A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. Neural Information Processing Systems. 16. 651–658. 31 indexed citations
20.
Neill, Daniel B.. (2001). Optimality Under Noise: Higher Memory Strategies for the Alternating Prisoner's Dilemma. Journal of Theoretical Biology. 211(2). 159–180. 23 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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