Hannah E. Reses

2.2k citations
36 papers · 1.3k · h-index 16

Impact in

Papers in

    • Vaccine Coverage and Hesitancy 10
    • SARS-CoV-2 and COVID-19 Research 11
    • COVID-19 Clinical Research Studies 6
    • SARS-CoV-2 detection and testing 3

Hannah E. Reses

36 papers receiving 1.2k citations

Peers

Hannah E. Reses
Comparison fields: 5 of 95
  • Modeling and Simulation 201
  • Infectious Diseases 558
  • Endocrinology 90
  • Virology 79
  • Health 115
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Lei Jia China
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Taro Kamigaki Japan
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Citations per field
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Citations per year

Countries citing papers authored by Hannah E. Reses

Since Specialization
Citations

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

Fields of papers citing papers by Hannah E. Reses

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hannah E. Reses. 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 Hannah E. Reses. The network helps show where Hannah E. Reses may publish in the future.

Co-authors

The 25 scholars most cited alongside Hannah E. Reses, 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 Hannah E. Reses Line = papers co-authored together Hannah E. Reses links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017260
2 2021122
3 2020111
4 202195
5 202079
6 201578
7 202076
8 202172
9 202166
10 202048
11 202236
12 201532
13 202128
14 201827
15 202224
16 202220
17 202314
18 202314
19 202312
20 202312

About Hannah E. Reses

Hannah E. Reses is a scholar working on Health, Infectious Diseases, Modeling and Simulation, Molecular Medicine and Health, Toxicology and Mutagenesis, having authored 36 papers that have together received 1.3k indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (11 papers), Vaccine Coverage and Hesitancy (10 papers), COVID-19 epidemiological studies (8 papers), Antibiotic Resistance in Bacteria (7 papers), COVID-19 Clinical Research Studies (6 papers), SARS-CoV-2 detection and testing (3 papers), Fecal contamination and water quality (2 papers) and Geriatric Care and Nursing Homes (2 papers). The work is most often cited by research in Modeling and Simulation (201 citations), Infectious Diseases (558 citations), Endocrinology (90 citations), Virology (79 citations) and Health (115 citations). Hannah E. Reses has collaborated with scholars based in United States, Uganda and Australia. Frequent co-authors include Jonathan S. Yoder, Kathleen E. Fullerton, Vincent R. Hill, Elizabeth D. Hilborn, Virginia A. Roberts, Timothy J. Wade, Katharine M. Benedict, Laura A. Cooley, Marissa K. Vigar and Mia Catharine M. Mattioli. Their work appears in journals such as MMWR Morbidity and Mortality Weekly Report, Infection Control and Hospital Epidemiology, Clinical Infectious Diseases, American Journal of Infection Control and The Journal of 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.

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