Howard Burkom

2.0k total citations
66 papers, 1.4k citations indexed

About

Howard Burkom is a scholar working on Epidemiology, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Howard Burkom has authored 66 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Epidemiology, 14 papers in Artificial Intelligence and 13 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Howard Burkom's work include Data-Driven Disease Surveillance (49 papers), Influenza Virus Research Studies (13 papers) and Anomaly Detection Techniques and Applications (13 papers). Howard Burkom is often cited by papers focused on Data-Driven Disease Surveillance (49 papers), Influenza Virus Research Studies (13 papers) and Anomaly Detection Techniques and Applications (13 papers). Howard Burkom collaborates with scholars based in United States, United Kingdom and Uganda. Howard Burkom's co-authors include Galit Shmueli, Julie A. Pavlin, Joseph S. Lombardo, William R. Hogan, David L. Buckeridge, Yevgeniy Elbert, Andrew Moore, Murray Campbell, Eugene Elbert and Jacqueline Coberly and has published in prestigious journals such as PLoS ONE, Technometrics and The Journal of the Acoustical Society of America.

In The Last Decade

Howard Burkom

63 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Howard Burkom United States 20 927 268 266 210 177 66 1.4k
Luiz H. Duczmal Brazil 18 922 1.0× 247 0.9× 304 1.1× 208 1.0× 52 0.3× 46 1.6k
David Muscatello Australia 26 1.2k 1.3× 78 0.3× 334 1.3× 226 1.1× 123 0.7× 116 2.3k
Kung‐Jong Lui United States 19 577 0.6× 88 0.3× 101 0.4× 118 0.6× 65 0.4× 146 2.2k
Julie A. Pavlin United States 22 966 1.0× 170 0.6× 264 1.0× 371 1.8× 293 1.7× 56 1.7k
Shaun J. Grannis United States 27 927 1.0× 351 1.3× 107 0.4× 413 2.0× 185 1.0× 139 2.4k
Jeremy U. Espino United States 16 725 0.8× 241 0.9× 149 0.6× 129 0.6× 250 1.4× 30 1.0k
Matthew Scotch United States 24 539 0.6× 350 1.3× 178 0.7× 244 1.2× 322 1.8× 103 1.8k
Leonardo Soares Bastos Brazil 21 324 0.3× 91 0.3× 292 1.1× 518 2.5× 47 0.3× 91 1.8k
W. Katherine Yih United States 28 1.4k 1.5× 88 0.3× 116 0.4× 196 0.9× 114 0.6× 62 2.9k
Yingcun Xia Singapore 27 181 0.2× 519 1.9× 271 1.0× 150 0.7× 119 0.7× 76 2.9k

Countries citing papers authored by Howard Burkom

Since Specialization
Citations

This map shows the geographic impact of Howard Burkom'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 Howard Burkom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Howard Burkom more than expected).

Fields of papers citing papers by Howard Burkom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Howard Burkom. 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 Howard Burkom. The network helps show where Howard Burkom may publish in the future.

Co-authorship network of co-authors of Howard Burkom

This figure shows the co-authorship network connecting the top 25 collaborators of Howard Burkom. A scholar is included among the top collaborators of Howard Burkom 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 Howard Burkom. Howard Burkom 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.
McShea, Michael, et al.. (2022). Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology. JMIR Medical Informatics. 10(3). e33212–e33212. 4 indexed citations
2.
Burkom, Howard, et al.. (2021). Feral Swine Commercial Slaughter and Condemnation at Federally Inspected Slaughter Establishments in the United States 2017–2019. Frontiers in Veterinary Science. 8. 690346–690346. 7 indexed citations
3.
Burkom, Howard, et al.. (2021). Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications. JMIR Public Health and Surveillance. 7(6). e26303–e26303. 26 indexed citations
4.
Zhou, Hong, et al.. (2017). Comparing the historical limits method with regression models for weekly monitoring of national notifiable diseases reports. Journal of Biomedical Informatics. 76. 34–40. 3 indexed citations
5.
Burkom, Howard, et al.. (2016). Syndromic Surveillance System for Korea–US Joint Biosurveillance Portal: Design and Lessons Learned. Health Security. 14(3). 152–160. 9 indexed citations
6.
Reid, Margaret, Julia Gunn, Snehal Shah, et al.. (2016). Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston. Online Journal of Public Health Informatics. 8(3). e199–e199. 1 indexed citations
7.
Shmueli, Galit & Howard Burkom. (2011). Statistical challenges facing early outbreak detection in biosurveillance. Quality Engineering. 56(1). 139–140. 3 indexed citations
8.
Xing, Jian, Howard Burkom, & Jerome I. Tokars. (2011). Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance. Journal of Biomedical Informatics. 44(6). 1093–1101. 20 indexed citations
9.
Burkom, Howard, et al.. (2011). An integrated approach for fusion of environmental and human health data for disease surveillance. Statistics in Medicine. 30(5). 470–479. 16 indexed citations
10.
Baer, Atar, et al.. (2010). Usefulness of Syndromic Data Sources for Investigating Morbidity Resulting From a Severe Weather Event. Disaster Medicine and Public Health Preparedness. 5(1). 37–45. 17 indexed citations
11.
Burkom, Howard, et al.. (2009). Recursive least squares background prediction of univariate syndromic surveillance data. BMC Medical Informatics and Decision Making. 9(1). 4–4. 2 indexed citations
12.
Elbert, Yevgeniy & Howard Burkom. (2009). Development and evaluation of a data‐adaptive alerting algorithm for univariate temporal biosurveillance data. Statistics in Medicine. 28(26). 3226–3248. 23 indexed citations
13.
Babin, Steven M., et al.. (2008). Drinking Water Security and Public Health Disease Outbreak Surveillance. Johns Hopkins APL technical digest. 27(4). 403–411. 11 indexed citations
14.
Chrétien, Jean-Paul, Howard Burkom, Endang R. Sedyaningsih, et al.. (2008). Syndromic Surveillance: Adapting Innovations to Developing Settings. PLoS Medicine. 5(3). e72–e72. 81 indexed citations
15.
Burkom, Howard, et al.. (2007). Automated time series forecasting for biosurveillance. Statistics in Medicine. 26(22). 4202–4218. 13 indexed citations
16.
Buckeridge, David L., Howard Burkom, Murray Campbell, William R. Hogan, & Andrew Moore. (2004). Algorithms for rapid outbreak detection: a research synthesis. Journal of Biomedical Informatics. 38(2). 99–113. 145 indexed citations
17.
Burkom, Howard. (2003). Development, adaptation, and assessment of alerting algorithms for biosurveillance. Johns Hopkins APL technical digest. 24(4). 335–342. 27 indexed citations
18.
Lombardo, Joseph S., Howard Burkom, Eugene Elbert, et al.. (2003). A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II). Journal of Urban Health. 80(S1). i32–i42. 166 indexed citations
19.
Cutchis, Protagoras N., et al.. (2003). Simulated release of plague in Montgomery County, Maryland. Johns Hopkins APL technical digest. 24(4). 354–359. 2 indexed citations
20.
Skura, Joseph P., et al.. (1988). An analysis of EMPE code performance in a selection of laterally inhomogeneous atmospheric-duct environments. Johns Hopkins APL technical digest. 9. 89–100. 5 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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