Max S. Y. Lau
- Modeling and Simulation top 0.5%
- Infectious Diseases top 5%
- Epidemiology
- Public Health, Environmental and Occupational Health
- Economics and Econometrics
- Co-authors
- Bryan T. GrenfellKristin N. NelsonSteven RileyBen LopmanBenjamin J. CowlingMichael BryanBenjamin D. DalzielAmanda McClelland
- Topics
- COVID-19 epidemiological studies (15 papers)Animal Disease Management and Epidemiology (7 papers)Influenza Virus Research Studies (4 papers)
- Partner nations
- United StatesUnited KingdomHong Kong
In The Last Decade
Max S. Y. Lau
19 papers receiving 623 citations
Peers
Comparison fields: 5 of 88
- Modeling and Simulation 413
- Infectious Diseases 252
- Epidemiology 186
- Public Health, Environmental and Occupational Health 102
- Economics and Econometrics 63
Countries citing papers authored by Max S. Y. Lau
This map shows the geographic impact of Max S. Y. Lau'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 Max S. Y. Lau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max S. Y. Lau more than expected).
Fields of papers citing papers by Max S. Y. Lau
This network shows the impact of papers produced by Max S. Y. Lau. 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 Max S. Y. Lau. The network helps show where Max S. Y. Lau may publish in the future.
Co-authorship network of co-authors of Max S. Y. Lau
This figure shows the co-authorship network connecting the top 25 collaborators of Max S. Y. Lau. A scholar is included among the top collaborators of Max S. Y. Lau 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 Max S. Y. Lau. Max S. Y. Lau 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 | 0 | |
| 3 | 19 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 3 | |
| 7 | 4 | |
| 8 | 58 | |
| 9 | 142 | |
| 10 | 9 | |
| 11 | 10 | |
| 12 | 11 | |
| 13 | 3 | |
| 14 | Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore: a spatio-temporal model | 0 |
| 15 | 27 | |
| 16 | 17 | |
| 17 | 43 | |
| 18 | 48 | |
| 19 | 21 | |
| 20 | 80 |
About Max S. Y. Lau
Max S. Y. Lau is a scholar working on Modeling and Simulation, Agronomy and Crop Science and Infectious Diseases, having authored 23 papers that have together received 631 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (15 papers), Animal Disease Management and Epidemiology (7 papers) and Influenza Virus Research Studies (4 papers). The work is most often cited by research in Modeling and Simulation (413 citations), Infectious Diseases (252 citations) and Epidemiology (186 citations). Max S. Y. Lau has collaborated with scholars based in United States, United Kingdom and Hong Kong. Frequent co-authors include Bryan T. Grenfell, Kristin N. Nelson, Steven Riley, Ben Lopman, Benjamin J. Cowling, Michael Bryan, Benjamin D. Dalziel, Amanda McClelland, Gavin J. Gibson and George Streftaris. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.
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.