Xiangjun Du

4.3k total citations · 1 hit paper
54 papers, 1.6k citations indexed

About

Xiangjun Du is a scholar working on Epidemiology, Modeling and Simulation and Infectious Diseases. According to data from OpenAlex, Xiangjun Du has authored 54 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Epidemiology, 25 papers in Modeling and Simulation and 15 papers in Infectious Diseases. Recurrent topics in Xiangjun Du's work include Influenza Virus Research Studies (25 papers), COVID-19 epidemiological studies (25 papers) and SARS-CoV-2 and COVID-19 Research (10 papers). Xiangjun Du is often cited by papers focused on Influenza Virus Research Studies (25 papers), COVID-19 epidemiological studies (25 papers) and SARS-CoV-2 and COVID-19 Research (10 papers). Xiangjun Du collaborates with scholars based in China, United States and Hong Kong. Xiangjun Du's co-authors include Chi Zhang, Andrew J. Tatem, Nick Ruktanonchai, Olivia Prosper, Shengjie Lai, Jessica Floyd, Amy Wesolowski, Wei Luo, Liangcai Zhou and Mauricio Santillana and has published in prestigious journals such as Nature, Nucleic Acids Research and Nature Communications.

In The Last Decade

Xiangjun Du

52 papers receiving 1.6k citations

Hit Papers

Effect of non-pharmaceutical interventions to contain COV... 2020 2026 2022 2024 2020 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiangjun Du China 14 803 563 405 294 259 54 1.6k
Xingjie Hao China 14 833 1.0× 252 0.4× 587 1.4× 319 1.1× 120 0.5× 55 1.8k
Leonid Chindelevitch United Kingdom 13 453 0.6× 226 0.4× 309 0.8× 218 0.7× 315 1.2× 49 1.1k
Sung-mok Jung Japan 14 976 1.2× 254 0.5× 636 1.6× 394 1.3× 57 0.2× 45 1.7k
Alexander E. Zarebski Australia 12 901 1.1× 305 0.5× 1.0k 2.5× 279 0.9× 59 0.2× 21 1.9k
Anel Nurtay United Kingdom 5 920 1.1× 352 0.6× 492 1.2× 163 0.6× 43 0.2× 5 1.6k
Abhishek Pandey United States 22 1.1k 1.4× 396 0.7× 1.0k 2.6× 322 1.1× 69 0.3× 62 2.3k
Tim K. Tsang Hong Kong 21 1.4k 1.7× 1.1k 2.0× 1.1k 2.7× 361 1.2× 137 0.5× 87 2.9k
Andrei R. Akhmetzhanov Japan 14 1.5k 1.9× 352 0.6× 995 2.5× 577 2.0× 77 0.3× 37 2.2k
Affan Shoukat Canada 16 885 1.1× 336 0.6× 832 2.1× 302 1.0× 49 0.2× 31 1.8k
Lander Willem Belgium 24 800 1.0× 654 1.2× 376 0.9× 211 0.7× 66 0.3× 73 1.7k

Countries citing papers authored by Xiangjun Du

Since Specialization
Citations

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

Fields of papers citing papers by Xiangjun Du

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiangjun Du. 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 Xiangjun Du. The network helps show where Xiangjun Du may publish in the future.

Co-authorship network of co-authors of Xiangjun Du

This figure shows the co-authorship network connecting the top 25 collaborators of Xiangjun Du. A scholar is included among the top collaborators of Xiangjun Du 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 Xiangjun Du. Xiangjun Du 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
2.
Feng, Jianbo, Rui Shi, Huixin Zhou, et al.. (2025). Mapping the Global Antigenic Evolution of Human Influenza A/H3N2 Neuraminidase Based on a Machine Learning Model — 1968–2024. China CDC Weekly. 7(29). 973–978. 1 indexed citations
3.
Zeng, Jinfeng, Haoyu Long, Jian Chen, et al.. (2024). Recombination and selection trajectory of the monkeypox virus during its adaptation in the human population. Journal of Medical Virology. 96(7). e29825–e29825. 3 indexed citations
4.
Zhai, Ke, Jinfeng Zeng, Pei‐Wen Cheng, et al.. (2024). Global antigenic landscape and vaccine recommendation strategy for low pathogenic avian influenza A (H9N2) viruses. Journal of Infection. 89(2). 106199–106199. 4 indexed citations
5.
Tang, Kang, Jinfeng Zeng, Jing Tang, et al.. (2024). Network-based approach for drug repurposing against mpox. International Journal of Biological Macromolecules. 270(Pt 2). 132468–132468. 5 indexed citations
6.
Chen, Yilin, Jinfeng Zeng, Chi Zhang, et al.. (2024). Global pattern and determinant for interaction of seasonal influenza viruses. Journal of Infection and Public Health. 17(6). 1086–1094. 4 indexed citations
7.
Zeng, Jinfeng, Kang Tang, Haoyu Long, et al.. (2023). Variation in synonymous evolutionary rates in the SARS-CoV-2 genome. Frontiers in Microbiology. 14. 1136386–1136386. 5 indexed citations
9.
Tang, Jing, Kang Tang, Xiaoyan Ye, et al.. (2023). Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome. Frontiers in Immunology. 13. 1048774–1048774. 2 indexed citations
10.
Lei, Hao, Nan Zhang, Shenglan Xiao, et al.. (2023). Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017. JMIR Public Health and Surveillance. 9. e41435–e41435. 11 indexed citations
11.
Lin, Yi‐Fan, Yuwei Li, Qibin Duan, et al.. (2022). Vaccination strategy for preventing the spread of SARS‐CoV‐2 in the limited supply condition: A mathematical modeling study. Journal of Medical Virology. 94(8). 3722–3730. 3 indexed citations
12.
Lei, Hao, Lei Yang, Gang Wang, et al.. (2022). Transmission Patterns of Seasonal Influenza in China between 2010 and 2018. Viruses. 14(9). 2063–2063. 21 indexed citations
13.
Zhang, Bing, Jing‐Kai Huang, Sen Pei, et al.. (2022). Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study. PLoS Pathogens. 18(12). e1011046–e1011046. 6 indexed citations
14.
Sun, Honglei, Fangtao Li, Qingzhi Liu, et al.. (2021). Mink is a highly susceptible host species to circulating human and avian influenza viruses. Emerging Microbes & Infections. 10(1). 472–480. 32 indexed citations
15.
Wang, Yaqi, Guoqin Mai, Min Zou, et al.. (2021). Heavy chain sequence-based classifier for the specificity of human antibodies. Briefings in Bioinformatics. 23(1). 3 indexed citations
16.
Zhang, Bing, Min Zou, Wei Shen, et al.. (2021). Synchronized nonpharmaceutical interventions for the control of COVID-19. Nonlinear Dynamics. 106(2). 1477–1489. 5 indexed citations
17.
Zhang, Chi, Yi Teng, Ting Xie, et al.. (2020). Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study. Journal of Medical Internet Research. 22(11). e23853–e23853. 11 indexed citations
18.
Lai, Shengjie, Nick Ruktanonchai, Liangcai Zhou, et al.. (2020). Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 585(7825). 410–413. 864 indexed citations breakdown →
19.
Du, Xiangjun & Mercedes Pascual. (2018). Incidence Prediction for the 2017-2018 Influenza Season in the United States with an Evolution-informed Model. PLoS Currents. 10. 3 indexed citations
20.
Du, Xiangjun, Damian Wójtowicz, A. Bowers, et al.. (2013). The genome-wide distribution of non-B DNA motifs is shaped by operon structure and suggests the transcriptional importance of non-B DNA structures in Escherichia coli. Nucleic Acids Research. 41(12). 5965–5977. 45 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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