David Haw
Impact in
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
Papers in
-
- COVID-19 epidemiological studies 15
-
- SARS-CoV-2 and COVID-19 Research 6
- COVID-19 Clinical Research Studies 5
- Co-authors
- Steven Riley (7 shared papers)Haowei Wang (5 shared papers)Deborah Ashby (5 shared papers)Christina Atchison (5 shared papers)Christl A. Donnelly (5 shared papers)Oliver Eales (5 shared papers)William Barclay (5 shared papers)Helen Ward (5 shared papers)
- Journals
- PLoS Computational Biology (3 papers)Nature Computational Science (2 papers)Journal of Mathematical Sociology (2 papers)Management Science (1 paper)Nature Communications (1 paper)
- Partner nations
- United KingdomSwedenAustralia
In The Last Decade
David Haw
20 papers receiving 226 citations
Peers
Comparison fields: 5 of 62
- Modeling and Simulation 117
- Infectious Diseases 119
- Health 23
- Emergency Medical Services 11
- Economics and Econometrics 39
Countries citing papers authored by David Haw
This map shows the geographic impact of David Haw'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 David Haw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Haw more than expected).
Fields of papers citing papers by David Haw
This network shows the impact of papers produced by David Haw. 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 David Haw. The network helps show where David Haw may publish in the future.
Co-authors
The 25 scholars most cited alongside David Haw, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 80 | |
| 2 | 2022 | 24 | |
| 3 | 2022 | 24 | |
| 4 | 2023 | 17 | |
| 5 | 2021 | 14 | |
| 6 | 2022 | 14 | |
| 7 | 2020 | 10 | |
| 8 | 2022 | 10 | |
| 9 | 2019 | 8 | |
| 10 | 2023 | 7 | |
| 11 | 2019 | 7 | |
| 12 | Implications of the Age Profile of the Novel Coronavirus | 2020 | 3 |
| 13 | 2022 | 2 | |
| 14 | 2023 | 2 | |
| 15 | 2020 | 2 | |
| 16 | 2018 | 2 | |
| 17 | 2023 | 2 | |
| 18 | 2024 | 1 | |
| 19 | 2020 | 1 | |
| 20 | 2020 | 1 |
About David Haw
David Haw is a scholar working on Modeling and Simulation, Infectious Diseases, Economics and Econometrics, Epidemiology and Sociology and Political Science, having authored 22 papers that have together received 231 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (15 papers), SARS-CoV-2 and COVID-19 Research (6 papers), COVID-19 Pandemic Impacts (5 papers), COVID-19 Clinical Research Studies (5 papers), Data-Driven Disease Surveillance (3 papers), Health Systems, Economic Evaluations, Quality of Life (2 papers), Mathematical and Theoretical Epidemiology and Ecology Models (2 papers) and Urban, Neighborhood, and Segregation Studies (2 papers). The work is most often cited by research in Modeling and Simulation (117 citations), Infectious Diseases (119 citations), Health (23 citations), Emergency Medical Services (11 citations) and Economics and Econometrics (39 citations). David Haw has collaborated with scholars based in United Kingdom, Sweden and Australia. Frequent co-authors include Steven Riley, Haowei Wang, Deborah Ashby, Christina Atchison, Christl A. Donnelly, Oliver Eales, William Barclay, Helen Ward, Graham Cooke and Ara Darzi. Their work appears in journals such as PLoS Computational Biology, Nature Computational Science, Journal of Mathematical Sociology, Management Science and Nature Communications.
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.