David Haw

4.8k total citations
22 papers, 231 citations indexed

About

David Haw is a scholar working on Modeling and Simulation, Infectious Diseases and Economics and Econometrics. According to data from OpenAlex, David Haw has authored 22 papers receiving a total of 231 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Modeling and Simulation, 8 papers in Infectious Diseases and 8 papers in Economics and Econometrics. Recurrent topics in David Haw's work include COVID-19 epidemiological studies (15 papers), SARS-CoV-2 and COVID-19 Research (6 papers) and COVID-19 Pandemic Impacts (5 papers). David Haw is often cited by papers focused on COVID-19 epidemiological studies (15 papers), SARS-CoV-2 and COVID-19 Research (6 papers) and COVID-19 Pandemic Impacts (5 papers). David Haw collaborates with scholars based in United Kingdom, Sweden and Australia. David Haw's co-authors include Steven Riley, Deborah Ashby, Oliver Eales, Christina Atchison, Paul Elliott, Helen Ward, Christl A. Donnelly, Ara Darzi, William Barclay and Graham Cooke and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

David Haw

20 papers receiving 226 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Haw United Kingdom 9 119 117 48 39 23 22 231
Shu Yang China 8 111 0.9× 136 1.2× 51 1.1× 54 1.4× 27 1.2× 17 321
Alexei Yavlinsky United Kingdom 8 133 1.1× 140 1.2× 40 0.8× 60 1.5× 31 1.3× 23 273
Zihao Guo Hong Kong 10 160 1.3× 136 1.2× 69 1.4× 27 0.7× 31 1.3× 41 298
Jacob Curran-Sebastian United Kingdom 4 98 0.8× 73 0.6× 31 0.6× 15 0.4× 17 0.7× 8 170
Nicholas Steyn New Zealand 13 149 1.3× 179 1.5× 68 1.4× 42 1.1× 33 1.4× 19 331
Tanner J. Varrelman United States 6 161 1.4× 70 0.6× 64 1.3× 17 0.4× 29 1.3× 10 241
Adrian Lison Switzerland 7 62 0.5× 146 1.2× 38 0.8× 46 1.2× 22 1.0× 12 210
Athalia Christie United States 7 146 1.2× 84 0.7× 55 1.1× 26 0.7× 74 3.2× 7 246
Ikkoh Yasuda Japan 7 109 0.9× 114 1.0× 56 1.2× 24 0.6× 12 0.5× 16 231
Emma S. Garlock Canada 2 116 1.0× 137 1.2× 32 0.7× 27 0.7× 9 0.4× 4 198

Countries citing papers authored by David Haw

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of David Haw

This figure shows the co-authorship network connecting the top 25 collaborators of David Haw. A scholar is included among the top collaborators of David Haw 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 David Haw. David Haw 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.
Hill, Edward M., David Haw, Ruth McCabe, et al.. (2024). Integrating human behaviour and epidemiological modelling: unlocking the remaining challenges. ORCA Online Research @Cardiff (Cardiff University). 1(1).
2.
Johnson, Rob, Martha Carnalla, Ana Basto‐Abreu, et al.. (2024). Promoting healthy populations as a pandemic preparedness strategy: a simulation study from Mexico. The Lancet Regional Health - Americas. 30. 100682–100682.
3.
Morgenstern, Christian, Daniel J. Laydon, Charles A. Whittaker, et al.. (2024). The interaction of disease transmission, mortality, and economic output over the first 2 years of the COVID-19 pandemic. PLoS ONE. 19(6). e0301785–e0301785. 1 indexed citations
4.
D'Aeth, Joshua C, Fiona Grimm, David Haw, et al.. (2023). Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic. Management Science. 69(10). 5923–5947. 7 indexed citations
5.
Eales, Oliver, David Haw, Haowei Wang, et al.. (2023). Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England. PLoS Biology. 21(5). e3002118–e3002118. 17 indexed citations
6.
Johnson, Rob, Bimandra A Djaafara, David Haw, et al.. (2023). The societal value of SARS-CoV-2 booster vaccination in Indonesia. Vaccine. 41(11). 1885–1891. 2 indexed citations
7.
Haw, David, Christian Morgenstern, Rob Johnson, et al.. (2022). Data needs for integrated economic-epidemiological models of pandemic mitigation policies. Epidemics. 41. 100644–100644. 2 indexed citations
8.
Eales, Oliver, Leonardo de Oliveira Martins, Andrew J. Page, et al.. (2022). Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nature Communications. 13(1). 4375–4375. 24 indexed citations
9.
Haw, David, Patrick Doohan, Robert A. Johnson, et al.. (2022). Optimizing social and economic activity while containing SARS-CoV-2 transmission using DAEDALUS. Nature Computational Science. 2(4). 223–233. 24 indexed citations
10.
Eales, Oliver, Haowei Wang, David Haw, et al.. (2022). Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021. PLoS Computational Biology. 18(11). e1010724–e1010724. 14 indexed citations
11.
Chadeau‐Hyam, Marc, Oliver Eales, Barbara Bodinier, et al.. (2022). Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study. EClinicalMedicine. 48. 101419–101419. 10 indexed citations
12.
D'Aeth, Joshua C, Fiona Grimm, David Haw, et al.. (2021). Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic. Nature Computational Science. 1(8). 521–531. 14 indexed citations
13.
Elliott, Paul, David Haw, Haowei Wang, et al.. (2021). Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant. Science. 374(6574). eabl9551–eabl9551. 80 indexed citations
14.
Haw, David, Rachael Pung, Jonathan M. Read, & Steven Riley. (2020). Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering. Proceedings of the National Academy of Sciences. 117(38). 23636–23642. 10 indexed citations
15.
Hay, James A., David Haw, William P. Hanage, C. Jessica E. Metcalf, & Michael Mina. (2020). Implications of the Age Profile of the Novel Coronavirus. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 3 indexed citations
16.
Haw, David & S. J. Hogan. (2020). A dynamical systems model of unorganized segregation in two neighborhoods. Journal of Mathematical Sociology. 44(4). 221–248. 1 indexed citations
17.
18.
Haw, David, et al.. (2020). DAEDALUS: An economic-epidemiological model to optimize economic activity while containing the SARS-CoV-2 pandemic. Spiral (Imperial College London). 1 indexed citations
19.
Haw, David, Derek A. T. Cummings, Justin Lessler, et al.. (2019). Differential mobility and local variation in infection attack rate. PLoS Computational Biology. 15(1). e1006600–e1006600. 8 indexed citations
20.
Haw, David & S. J. Hogan. (2018). A dynamical systems model of unorganized segregation. Journal of Mathematical Sociology. 42(3). 113–127. 2 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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