Joshua Schwab
- Statistics and Probability top 2%
- Infectious Diseases
- Economics and Econometrics
- General Health Professions
- Epidemiology
- Co-authors
- Mark J. van der LaanMaya PetersenMark van der LaanMaya L. PetersenSamuel LendleNello BlaserMichael SchomakerSusan Gruber
- Topics
- Advanced Causal Inference Techniques (7 papers)HIV/AIDS Research and Interventions (6 papers)Statistical Methods and Bayesian Inference (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEClinical Infectious Diseases
- Partner nations
- United StatesKenyaUganda
In The Last Decade
Joshua Schwab
17 papers receiving 311 citations
Peers
Comparison fields: 5 of 89
- Statistics and Probability 164
- Infectious Diseases 63
- Economics and Econometrics 60
- General Health Professions 45
- Epidemiology 40
Countries citing papers authored by Joshua Schwab
This map shows the geographic impact of Joshua Schwab'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 Joshua Schwab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua Schwab more than expected).
Fields of papers citing papers by Joshua Schwab
This network shows the impact of papers produced by Joshua Schwab. 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 Joshua Schwab. The network helps show where Joshua Schwab may publish in the future.
Co-authorship network of co-authors of Joshua Schwab
This figure shows the co-authorship network connecting the top 25 collaborators of Joshua Schwab. A scholar is included among the top collaborators of Joshua Schwab 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 Joshua Schwab. Joshua Schwab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 15 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | 27 | |
| 8 | 7 | |
| 9 | 4 | |
| 10 | 7 | |
| 11 | 5 | |
| 12 | 7 | |
| 13 | Longitudinal Targeted Maximum Likelihood Estimation [R package ltmle version 1.2-0] | 1 |
| 14 | 21 | |
| 15 | 67 | |
| 16 | 42 | |
| 17 | Longitudinal Targeted Maximum Likelihood Estimation | 5 |
| 18 | 101 |
About Joshua Schwab
Joshua Schwab is a scholar working on Virology, Statistics and Probability and Modeling and Simulation, having authored 18 papers that have together received 324 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (7 papers), HIV/AIDS Research and Interventions (6 papers) and Statistical Methods and Bayesian Inference (5 papers). The work is most often cited by research in Statistics and Probability (164 citations), Virology (18 citations) and Infectious Diseases (63 citations). Joshua Schwab has collaborated with scholars based in United States, Kenya and Uganda. Frequent co-authors include Mark J. van der Laan, Maya Petersen, Mark van der Laan, Maya L. Petersen, Samuel Lendle, Nello Blaser, Michael Schomaker, Susan Gruber, Diane V. Havlir and Cheng Ju. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Clinical Infectious Diseases.
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.