Bryan Wilder

44 papers receiving 1.0k citations

Hit Papers

Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening 2021 · 565 citations
5652021202620222024100200300400500

Peers

Bryan Wilder
Comparison fields: 5 of 126
  • Modeling and Simulation 224
  • Infectious Diseases 509
  • Statistical and Nonlinear Physics 116
  • Health Informatics 9
  • Computer Science Applications 35
Replace Anel Nurtay with:
Anel Nurtay United Kingdom
Jalal S. Alowibdi Saudi Arabia
Christopher L. Barrett United States
Ye Wu China
Alberto Aleta Spain
Jiangzhuo Chen United States
Lucía Russo Italy
Ashima Yadav India
Damián Knopoff Argentina
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Citations per field
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Citations per year

Countries citing papers authored by Bryan Wilder

Since Specialization
Citations

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

Fields of papers citing papers by Bryan Wilder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Bryan Wilder, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bryan Wilder Line = papers co-authored together Bryan Wilder links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening
Hit paper breakdown →
2021565
2 201984
3 202035
4 201834
5 201826
6 201724
7 202022
8 201721
9 201721
10 201719
11 201819
12 201513
13 201811
14 201811
15 201811
16 201811
17 20249
18 20209
19 20219
20 20209

About Bryan Wilder

Bryan Wilder is a scholar working on Sociology and Political Science, Artificial Intelligence, Modeling and Simulation, General Health Professions and Statistical and Nonlinear Physics, having authored 48 papers that have together received 1.0k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (10 papers), Complex Network Analysis Techniques (8 papers), Opinion Dynamics and Social Influence (7 papers), Homelessness and Social Issues (7 papers), Evolutionary Game Theory and Cooperation (5 papers), Data-Driven Disease Surveillance (4 papers), Spam and Phishing Detection (4 papers) and Functional Equations Stability Results (3 papers). The work is most often cited by research in Modeling and Simulation (224 citations), Infectious Diseases (509 citations), Statistical and Nonlinear Physics (116 citations), Health Informatics (9 citations) and Computer Science Applications (35 citations). Bryan Wilder has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Milind Tambe, Michael J. Mina, James A. Hay, James M. Burke, Daniel B. Larremore, Roy Parker, Soraya I. Shehata, Evan Lester, Bistra Dilkina and Eric Rice. Their work appears in journals such as Aequationes Mathematicae, PLoS Computational Biology, International Journal of Artificial Intelligence in Education, Journal of the Society for Social Work and Research and IEEE Intelligent Systems.

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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